Breaking the rate limiting barrier in lithium||sulfur batteries via spin state engineering

Domain: biology
Published: 2026-03-24
Editorial outcome: minor_revision

Abstract
Elucidating the mechanisms governing sulfur redox reactions is important for the development of high-energy-density Li||S batteries. Despite progress, the kinetics of the solid-solid conversion from Li2S2 to Li2S remain poorly understood. This work demonstrates that spin-state transitions within reaction intermediates are the key factor of the sluggish kinetics. Guided by density functional theory and machine-learning-assisted catalyst screening, we find a negative correlation between the spin moment of the catalyst and the Gibbs free energy barrier for the Li2S2 to Li2S conversion. Among a series of dual-metal doped catalysts, a Co,Ni-doped MoS2 catalyst, with its high spin moment, modulates the spin states of the reactants, reducing the high free-energy barrier associated with spin-state transitions. Therefore, Li||S batteries incorporating this catalyst show accelerated sulfur conversion, particularly during solid-solid transitions, suppressed polysulfide shuttling, and have stable electrochemical performance. A pouch cell achieves a capacity of 13.2 Ah and a specific energy of 435 Wh kg-1. These findings show mechanistic understanding into the role of spin moments in sulfur conversion, enabling to design efficient and durable catalysts for Li||S batteries.

Introduction
Lithium||sulfur (Li||S) batteries are regarded as promising next-generation energy storage systems due to the natural abundance of sulfur resources and their significantly higher theoretical specific capacity (1,672 mAh g-1) than existing commercialized battery systems1-3. In the typical redox process of Li||S batteries, the sulfur conversion chemistry involves a multi-step, 16-electron transfer process, including solid-liquid (S8-soluble lithium polysulfides (LiPSs)), liquid-solid (LiPSs-Li2S2), and solid-solid (Li2S2-Li2S) transitions4-7. The conversion from Li2S2 to Li2S contributes to approximately three-quarters of the total capacity, indicating the critical importance of this process8-11. Previous studies have predominantly focused on the liquid-solid transformation, as this reaction is often associated with a high activation energy and the aggregation of polysulfides, which produces a severe shuttling effect, resulting in poor sulfur utilization and cycling performance12-14. To accelerate the liquid-solid transition, various catalysts (such as different carbon materials12,15, metal compounds16,17, single-atom catalysts18,19, and heterojunction materials20,21) have been developed to effectively adsorb LiPSs and accelerate their conversion, thereby alleviating the shuttling and improving the cycling performance of the system. Despite extensive research on the interaction mechanisms between various catalysts and LiPSs, such as adsorption and catalytic processes, the mechanism of the final solid-solid transition remains insufficiently explored. Numerous theoretical calculations suggest that the solid-solid transition from Li2S2 to Li2S is the rate-limiting step in the sulfur redox reaction22-25. Therefore, exploring efficient electrocatalysts for sulfur reduction reactions (SRR) to facilitate this key step is essential for improving the overall performance of Li||S batteries.

Although recent studies have proposed the concept of electrocatalytic methods, the electrocatalytic kinetics of the rate-determining step, i.e., the transformation from Li2S2 to Li2S, remains largely unexplored, and the problem of using the electrocatalytic effect to solve the slow precipitation of Li2S has not been clearly resolved. The Gibbs free energy changes (ΔG) of the rate-determining step are commonly used to evaluate the performance of electrocatalysts26-28. Unfortunately, the actual reaction pathway is dominated by the formation of intermediates, which are difficult to be experimentally detected directly. Recently, three intermediates (*LiS, *Li3S2, and *S) have been proposed to guide three potential reaction paths (Supplementary Figs. 1a-c)29-31. By analyzing the thermodynamic stability and Gibbs free energy changes (ΔG1i, ΔG2i, ΔG3i) of these intermediates along the paths, it has been found that the formation of intermediates determines the catalytic activity and efficiency of Li||S batteries. Investigations in other fields have also demonstrated that the properties of intermediates are critical to the reaction kinetics32-35. For example, the slow kinetics of the oxygen evolution reaction (OER) can be attributed to a multi-electron transfer process, in which the formation of triplet molecular oxygen (↑ O = O ↑) involves spin changes36. In the electro-reduction of nitrates to ammonia, the hydrogenation of a non-spin-state *NO intermediate to form a spin-state-transformed hydride *NHO/*NOH requires a large amount of energy, resulting in slow kinetics37,38. Inspired by these findings, the spin-state transitions during the conversion of Li2S2 to Li2S have been studied, and it was found that the slow process of this transformation is closely related to the spin-state transitions of the intermediate products. The difference from traditional catalytic explanation in Li||S batteries, i.e., the intermediates in the conversion of Li2S2 to Li2S involve spin polarization, means that the process is not only thermodynamically controlled but also influenced by spin-state changes. This discovery explains the fundamental cause of the slow conversion from Li2S2 to Li2S and provides a perspective on addressing the rate-determining step of the SRR process.

Proper manipulation of the spin states of metal active sites in electrocatalysts is an effective strategy to accelerate the spin-state transition between intermediate products, because spin-polarized metal active sites can provide spin electrons to increase the conversion efficiency of Li2S2 to Li2S, as proposed by Li et al39,40. More importantly, the magnetic coupling of two metal species, such as Ni-O-Fe41 or Ti-O-Fe42, has recently been suggested and demonstrated to trigger quantum spin exchange interactions, thereby establishing electronic transfer channels in the lattice and providing additional spin electron transfer pathways for spin-dependent electrocatalytic reactions36,43-45. Dual-metal doping preserves the structural stability of the pristine lattice, while enabling stronger lattice-wide spin interactions than single-atom doping or heterostructure engineering. This strategy has been successfully applied in other electrocatalytic fields, not only helping to increase reaction rates but also stabilizing key intermediates in the reaction process, significantly improving the electrocatalytic performance36,42,46. Unfortunately, previous studies have not yet provided a comprehensive and in-depth understanding of the fundamental mechanisms behind the catalysis of Li2S2 to Li2S reduction. The interaction between the properties of the metal sites of a catalyst and the conversion efficiency of Li2S2 to Li2S also remains inadequately understood, and this could provide a standard descriptor for the development of transition metal-based catalysts. Theoretical calculations combined with machine learning (ML) enable systematic feature analysis to uncover the correlation between spin states and solid-solid conversion, which can provide a powerful platform for the rational design of catalysts for high-performance Li||S batteries (Fig. 1a).

In this work, a fundamental investigation was conducted on how manipulating the spin moments of two different metal atoms influences the rate-limiting step of the electrocatalytic SRR. The kinetics of the conversion process from Li2S2 to Li2S were systematically analyzed, focusing on the initial and final states as well as the characteristics of plausible intermediates. The results show that the sluggish kinetics of this conversion process are fundamentally governed by spin-state transitions in the intermediate states. Based on this finding, we designed a series of pairs of transition metal dopants (V-Mn, V-Fe, V-Co, V-Ni, Mn-Fe, Mn-Co, Mn-Ni, Fe-Co, Fe-Ni, Co-Ni) with spin-electronic channels into a MoS2 catalyst to explore the relationship between the spin environment of active sites and reaction activity in the rate-limiting step of Li||S batteries. DFT results show that pure MoS2 has no spin state, while the different two metal doping sites affect the spin state around the active sites and form spin-electronic transfer channels. Based on the data-driven ML models, we identify spin states as a key factor in optimizing the catalytic activity of the rate-determining step in Li||S batteries. A negative correlation is revealed between the thermodynamic energy barrier of the Li2S2 to Li2S conversion and the spin density of the active sites, which means that the deposition kinetics of Li2S are improved by increasing spin density. A Co,Ni-MoS2 catalyst, with its high spin moment, has been shown to change the spin states of both the initial and final reactants and, as a result, its conversion of Li2S2 to Li2S circumvents the substantial energy barrier that is typically linked to spin-state changes. As a result, Li||S batteries incorporating with Co,Ni-MoS2 have rapid sulfur transformations, particularly during solid-solid phase transitions, and effectively prevent polysulfide shuttling. These improvements translate into a high specific capacity, a stable cycling performance, and good rate capability. Furthermore, a Li||S pouch cell with the Co,Ni-MoS2 catalyst with a low electrolyte/sulfur ratio (E/S, 3.2 μl mg-1), achieves a high specific energy of 435 Wh kg-1 with 13.2 ampere-hour (Ah)-level devices. The two-metal active sites reveal the connection between the magnetic moment of catalysts and the SRR rate-limiting step, deepening our fundamental understanding of designing efficient catalysts for Li||S batteries.

Results
Understanding the Spin-Regulated Kinetics in Li2S2 to Li2S Conversion using ML and DFT

The conversion from Li2S2 to Li2S is widely recognized as a kinetically sluggish step in the SRR process, because of its intrinsically high energy barrier. However, the mechanistic origins of this barrier remain poorly understood. From the above reaction, it is evident that the process involves a two-electron transfer, indicating the potential formation of intermediates. Through systematic investigation and analysis, three potential intermediates have been identified: *LiS, *Li3S2, and *S, which involve the following three corresponding reaction pathways: equation (1) *Li2S2 → *LiS → *Li2S, equation (2) *Li2S2 → *Li3S2 → *LiS → *Li2S, or equation (3) *Li2S2 → *S → *Li2S (Supplementary Fig. 2). To elucidate the electronic properties governing these transformations, we conducted density of states (DOS) analyses to examine the spin states of each molecular species along the three proposed pathways. Given that spin-state transitions are known to play a crucial role in determining reaction kinetics, the DOS results indicate that both the reactant (Li2S2) and the final product (Li2S) have symmetric spin states in the spin-up and spin-down configurations, while the intermediate species *LiS has a pronounced asymmetry in its spin states, deviating from the symmetric spin configurations observed in Li2S2 and Li2S (Supplementary Fig. 3a). This asymmetry strongly suggests that the conversion from Li2S2 to *LiS and the subsequent one from *LiS to Li2S are intrinsically associated with spin-state transitions. Moreover, similar spin-state transitions are observed in reaction paths (2) and (3), where the intermediates *Li3S2 and *S also have asymmetric spin states in the spin-up and spin-down configurations (Supplementary Figs. 3b, c). These findings highlight the pivotal role of spin polarization in governing the kinetics of the Li2S2-to-Li2S conversion. The molecular orbitals of all intermediates along the three reaction paths were also analyzed, and it was found that both Li2S2 and Li2S have fully paired electrons, consistent with their non-magnetic, spin-symmetric nature (Supplementary Figs. 4-6). In contrast, the ground-state intermediates *LiS, *Li3S2, and *S show the presence of unpaired electrons (↑), confirming their intrinsic spin polarization and underscoring the spin-correlated character of the electron transfer process throughout the reaction. This intrinsic spin polarization accounts for the non-zero spin moments observed in the intermediate species. Consequently, regardless of the reaction path, the conversion from Li2S2 to Li2S inevitably involves spin-state transitions. However, such transitions are often energetically demanding and kinetically sluggish, posing a major obstacle to efficient sulfur reduction42. Previous studies have shown that spin polarization at metal active sites is a critical factor in facilitating the spin-state transitions of reaction intermediates, particularly within two-metal systems that provide spin-active channels. To explore the relationship between the spin state of metal active sites and the rate-determining step in the Li2S2 → Li2S conversion, we selected MoS2, an intrinsically non-spin-polarized material, as an initial model. We then changed its spin polarization by introducing a two-atom doping strategy using five transition metals: vanadium (V), iron (Fe), manganese (Mn), cobalt (Co), and nickel (Ni). As a result, we constructed ten two-metal (V-Mn, V-Fe, V-Co, V-Ni, Mn-Fe, Mn-Co, Mn-Ni, Fe-Co, Fe-Ni, Co-Ni) doped catalysts with different degrees of spin polarization. We systematically evaluated the optimum reaction paths and catalytic activities of these catalysts under different mechanisms, and analyzed the optimized structures of these catalysts and their intermediates along various paths (Supplementary Figs. 7-11 and Supplementary Data 1-11). The results show that the different catalysts have different values of the Gibbs free energy changes (ΔG) for the different intermediate paths (Figs. 1b-d, Supplementary Fig. 12, and Table 2). By comparing the calculated ΔG of the rate-determining steps along the three possible paths for each catalyst, we identified both the preferred reaction path and the associated conversion barrier. Of all the catalysts, Co,Ni-MoS2 catalyst had the lowest free energy change for the rate-determining steps, indicating its fast kinetics in promoting the Li2S2 to Li2S conversion.

To investigate how the intrinsic properties of catalysts influence the rate-determining step of Li2S2-to-Li2S conversion, an initial feature space of 65 descriptors was constructed for MoS2 doped with two transition metals, covering electronic structure, elemental physicochemical and geometric properties, as well as adsorption- and reaction-related parameters (Supplementary Fig. 13). After a redundancy-testing and feature-selection procedure, a 29-dimensional feature space was retained for subsequent machine-learning analysis to identify the key features that govern ensemble effects based on a dataset (Supplementary Table 1). Given the established impact of spin density on catalytic behavior, spin polarization parameters were explicitly included. Because strong correlations between adsorption energy and catalytic activity have been demonstrated in previous studies, the adsorption energies of Li2S2 and Li2S on catalyst surfaces were incorporated as one of the essential features. Guided by the Sabatier principle, which emphasizes the decisive role of electronic structure at active sites in governing catalytic behavior, multiple electronic descriptors were included, such as spin density, d-band centers of transition metals, p-band centers of non-transition metals, and doping-induced charge transfer. Fundamental physicochemical properties of dopant elements, such as the first ionization energy and Pauling electronegativity (for both metal and non-metal active site constituents), were considered. Geometric and electronic parameters, including bond lengths and charge distributions, were introduced to capture local structural variations. The feature space also included elemental attributes, such as atomic mass, valence electron count, and melting point, thereby ensuring a comprehensive representation of the system. On the basis of this robust feature set, ML revealed ensemble effects associated with the MoS2 catalysts doped with two metals during sulfur electrochemical reactions.

To evaluate model performance, a series of ML algorithms, including random forest, least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting, were assessed using the “Leave-One-Out” cross-validation method. All four models had a relatively high predictive accuracy. Among them, the LASSO regression model was selected as the optimal algorithm of structure-performance relationships, with the highest accuracy as shown by its coefficient of determination (R2) and mean square error (MSE) metrics (Supplementary Figs. 14 and 15). The strong predictive performance of this model (R2 = 0.911) highlights the ability of the ML framework to efficiently and accurately predict the reaction free energy of the rate-determining step in catalysts doped with two transition metals. To further interpret the model and identify the most influential descriptor for ΔG (Li2S2 → Li2S), the SHapley Additive exPlanations (SHAP) method was used. Feature importance analysis was performed across all 29 parameters, the top 12 of which are shown in Fig. 1e. The final ranking of the importance of the different parameters from the predictive model elucidates the key factors governing the catalytic activity of multi-site catalysts, which can be broadly categorized into two groups (Fig. 1f). The first category includes spin-related parameters, of which the spin density was identified as one of the most influential features, suggesting that changing the spin characteristics plays a critical role in improving the catalytic activity. This would most likely be caused by the facilitation of spin-electronic transfer channels and the promotion of quantum spin exchange interactions, which significantly lower the energy barrier and accelerate the Li2S2-to-Li2S conversion. The second group includes the adsorption energies of Li2S2 and Li2S on the catalyst surface because they influence the stability of the initial and final states of the reaction, thereby directly impacting the overall catalytic performance. These findings underscore the crucial role of electronic properties, particularly spin moment, in governing the rate-determining step reaction in a Li||S battery and offer valuable insight for the investigations of ensemble effects. Upon completion of model training, a comparison between ML-predicted and DFT-calculated ΔG (Li2S2 → Li2S) values is shown in Fig. 1g. The close agreement between the ML predictions and DFT results (adjusted R2 = 0.93) shows the high generalizability of the model to data points. As more high-quality data become available, increasingly accurate and reliable ML models can be developed, significantly advancing our understanding of complex catalytic mechanisms and highlighting the vast potential of ML in catalysis research.

In addition, the linear correlation between the spin moment of catalysts and the free energy change of the rate-determining step during Li2S2 to Li2S conversion was also investigated. (Fig. 1h). The analysis shows a clear linear relationship: catalysts with higher spin moments tend to have a lower ΔG during the conversion process, thereby facilitating faster redox kinetics. The Co,Ni-MoS2 catalysts has possibly the highest spin moment and the lowest ΔG value, which is mainly attributed to the spin-polarized Co and Ni metal active sites which drive quantum spin exchange interactions, providing spin electron transfer channels for spin-related electrocatalytic reactions, accelerating the spin state transition between reactant intermediates, and thus promoting the conversion between Li2S2 and Li2S (Figs. 1i, j)41,42. In contrast, the parent MoS2 with no intrinsic spin polarization has a significantly higher ΔG value, thereby leading to sluggish redox kinetics.

Synthesis and characterization of MoS2 catalysts doped with two different metal cations

To experimentally validate the above findings, four representative catalysts (MoS2, Co,V-MoS2, Co,Mn-MoS2, and Co,Ni-MoS2) were synthesized by a one-step hydrothermal method under identical conditions. The surface morphologies of the resulting materials were characterized by scanning electron microscopy (SEM), as shown in Fig. 2a. The pristine MoS2 material has an ultrathin nanosheet morphology (Supplementary Fig. 16), which is not changed by the introduction of Co and Ni dopants, so that the resulting Co,Ni-MoS2 consists of thin and uniform nanosheets. Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) also show that the Co,Ni-MoS2 catalyst is comprised of a few nanosheets approximately 5 nm in thickness (Figs. 2b, c). The observed lattice fringe spacing of 0.623 nm agrees well with the value for the (002) planes of MoS2, confirming structural integrity after doping47. The corresponding energy dispersive spectroscopy (EDS) mapping shows a uniform distribution of Mo, S, Co, and Ni throughout the nanosheets, indicating the homogeneous incorporation of Co and Ni cations in the Co,Ni-MoS2 catalyst (Figs. 2d, e). The nCo:nMo and nNi:nMo atomic ratios in Co,Ni-MoS2 were both determined to be about 0.04 from ICP-MS analysis (Supplementary Table 3). Similar structural features are observed in the HRTEM and EDS images of Co,V-MoS2 and Co,Mn-MoS2 catalysts (Supplementary Figs. 17 and 18). To further elucidate the microscopic atomic arrangement of the introduced metal cations in MoS2, High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) images of these catalysts were analyzed. As shown in Figs. 2f, g, Co,Ni-MoS2 has a well-defined lattice with alternating bright and dark atomic regions. Since the brightness is proportional to the atomic number, the darker regions are attributed to the presence of isolated Co and Ni atoms. An enlarged image shows that single Co and Ni atoms (marked with red and green circles) replace the Mo atoms in the atomic lattice (Fig. 2g)47. In addition, the linear intensity profiles were analyzed using a rectangular scanning profile (marked with white rectangles in Fig. 2g), and the result shows that the signal intensity of Mo atoms is higher than that of the substituted Ni and Co atoms, which is consistent with the optimized atomic structure model of Co,Ni-MoS2 (Fig. 2h). Consistent atomic configurations of the bimetallic dopant pairs (Co, Mn) in Co,Mn-MoS2 and (Co, V) in Co,V-MoS2 were also observed, reinforcing the universality of the substitutional doping mechanism (Supplementary Figs. 17 and 18). The spectra of the crystal structure of all the products were obtained by powder X-ray diffraction (XRD) and show a good match to the standard 2H MoS2 phase (JCPDS card no. 37-1492), with peaks at 14.0o, 34.6o, 39.5o, and 57.3o of the (002), (100), (103), and (110) planes, respectively48 (Fig. 2i). Rietveld refinement analyses confirmed that the doped samples remain a single phase with a good fit to MoS2 (Supplementary Fig. 19 and Tables 4-7). These results verify that the transition metal dopants were successfully incorporated into the Mo lattice sites without generating secondary phases. Raman spectroscopy further verifies the absence of a 2H to 1T phase transformation. Both pristine MoS2 and dual-metal incorporation MoS2 display only the signature E1g and A1g modes of the 2H phase, while no J1/J2/J3 vibrational fingerprints corresponding to 1T phases are observed (Supplementary Fig. 20). To gain deep insights into the local configuration and bonding characteristics of the Co and Ni sites in Co,Ni-MoS2, X-ray absorption fine structure (XAFS) spectra were used. The Co and Ni K- edge X-ray absorption near edge structure (XANES) spectra of Co,Ni-MoS2 lay between those of the corresponding metallic foils (Co, Ni) and the sulfide references (CoS2, NiS2) (Figs. 2j, k). In the Fourier transform k3-weighted Co K-edge extended X-ray absorption fine-structure (FT-EXAFS) spectra (Fig. 2l), only a dominant peak at 1.59 Å was observed, corresponding to the Co-S coordination shell. The obvious Co-Co peak (2.08 Å) on the standard Co foil was absent in Co,Ni-MoS2. This coordination environment aligns well with a structural model in which adjacent Co and Ni atoms substitute for Mo atoms in the MoS2 lattice (Supplementary Fig. 21 and Table 8). Similarly, in the Ni K-edge FT-EXAFS spectra (Fig. 2m), the characteristic signal is different from that of Ni-Ni (2.05 Å) in the Ni foil, which is associated with the Ni-S scattering path (1.62 Å). The agreement between the experimental Ni K-edge EXAFS data and the simulated Co,Ni-MoS2 model suggests that adjacent Co and Ni atoms substitute for Mo atoms in the MoS2 (Supplementary Fig. 21). Taking advantage of the enhanced resolution provided by incorporating both radial distance and k-space information, wavelet transform (WT) analysis was applied to the k2-weighted EXAFS signal to identify the backscattering atoms (Fig. 2n). The WT contour plots of the Co,Ni-MoS2 sample reveal two distinct intensity maxima, which can be assigned to Co-S and Ni-S coordination, respectively. Similar spectral features are observed for Co,V-MoS2 and Co,Mn-MoS2, where adjacent Co-Mn and Co -V atomic pairs substituted for Mo in the MoS2 lattice (Supplementary Figs. 22-25). This behavior aligns with the characteristics previously reported for metal atom-doped MoS2-based catalysts47,49.

To investigate the complex electronic configuration and electronic structure of the catalysts in depth, DFT calculations were performed. The calculated DOS in Supplementary Fig. 26 shows that MoS2 is a wide-gap semiconductor, while the Co,V-MoS2, Co,Mn-MoS2, and Co,Ni-MoS2 catalysts have smaller gaps at the Fermi level, indicating it is easier to remove electrons (Supplementary Figs. 27 and 28). To quantitatively evaluate the spin polarization of the four catalysts, their spin densities were calculated. As shown in Supplementary Fig. 29, MoS2 has no observable spin density, indicating the absence of intrinsic magnetic properties. In contrast, the presence of unpaired 3d electrons localized on the dopant atoms induces ferromagnetic spin polarization in the neighboring Mo and S atoms. Among the four catalysts, Co,Ni-MoS2 has the highest spin density, suggesting a substantial increase in spin polarization, which is greater than in Co,V-MoS2 and Co,Mn-MoS2. The increased spin density in Co-Ni-MoS2 can be attributed to the interaction between Co and Ni atoms, which may induce localized spin moments and facilitate spin-polarized electronic states. The localized spin moments and the spin-polarized electronic states in different doped regions are reflected in the DOS of the polarized regions. The DOS plots show an asymmetric distribution of spin-up and spin-down electron densities near the Fermi level. Specifically, the pure MoS2 sample has a highly symmetric electronic structure around the Fermi level, confirming its non-spin-polarized nature (Supplementary Fig. 26a). In the Co,Ni-MoS2 sample, the spin-polarized states near Fermi level primarily originate from the 3d and 3d orbitals of the Co and Ni dopants, the 3px, 3py, and 3pz orbitals of adjacent S atoms, and the dz2 orbitals of neighboring Mo atoms (Supplementary Fig. 30). Similarly, in the Co,Mn-MoS2 sample, the spin-polarized states near Fermi level mainly arise from the 3d and 3d orbitals of the Co dopant, the 3px, 3py, and 3pz orbitals of adjacent S atoms, and the dz2 orbitals of both neighboring Mo atoms and the doped Mn atoms (Supplementary Fig. 31). In the Co,V-MoS2 sample, the spin-polarized states near Fermi level are primarily derived from the 3d and 3d orbitals of the Co dopant, the 3px, 3py, and 3pz orbitals of adjacent S atoms, and the dz2 orbitals of both neighboring Mo atoms and the doped V atoms (Supplementary Fig. 32). Because spin polarization fundamentally arises from spin-dependent splitting and asymmetric occupation of electronic states, orbital-resolved PDOS analysis offers direct insight into the relative contributions of specific d orbitals to spin-polarization behavior. Co-Ni doping introduces dopant-derived 3d states with pronounced spin splitting and substantial spin-resolved weight, which hybridize more effectively with neighboring Mo 4d and S 3p orbitals, as evidenced by the enhanced spin-resolved Mo/S contributions and the more delocalized spin-density distribution near Fermi level (Supplementary Fig. 30). This strong dopant-lattice electronic coupling facilitates the propagation of spin polarization into the Mo-S framework, enabling the formation of delocalized spin channels that are accessible for electrochemical reactions. By comparison, the Co-Mn system exhibits weaker spin-resolved weight and reduced coupling to Mo-S orbitals, leading to intermediate spin polarization, whereas Co,V-MoS2 shows only limited spin-polarized states near Fermi level and minimal Mo/S participation, corresponding to the weakest overall spin polarization (Supplementary Figs. 31 and 32). To elucidate the effective magnetic moment (μeff) of the catalysts, temperature-dependent magnetic susceptibility (M-T) measurements were conducted and analyzed using the Curie-Weiss law. As illustrated in the 1/χ versus temperature plots for MoS2, Co,V-MoS2, Co,Mn-MoS2, and Co,Ni-MoS2 (Figs. 2o, p), the calculated μeff values are as follows: Co,Ni-MoS2 (μeff = 0.3632) > Co,Mn-MoS2 (μeff = 0.2681) > Co,V-MoS2 (μeff = 0.2130) > MoS2 (μeff = 0.0304). These results indicate that Co,Ni-MoS2 has a significantly higher number of unpaired spin electrons than other samples, which is consistent with the results from both DFT and ML analyses. Collectively, these findings clearly show that atomic pair doping can effectively induce spin polarization in the catalysts, offering an opportunity for the development of spin-polarized electrocatalysts aimed at accelerating the reaction of the rate-determining step in Li||S batteries.

Kinetics and mechanistic analysis of the redox reaction between Li2S2 and Li2S.

The catalytic conversion of Li2S2 was evaluated by a potentiostatic intermittent titration technique (PITT) measurement to verify the relationship between the catalytic activity and the spin moment of catalysts. As shown in Fig. 3a, the potentiostatic discharge profiles of MoS2, Co,V-MoS2, Co,Mn-MoS2, and Co,Ni-MoS2 catalysts show distinct differences in Li2S nucleation behavior. Among them, Co,Ni-MoS2 has the highest nucleation current response, surpassing those of MoS2 and the other doped variants. Based on Faraday’s law, the Li2S nucleation capacity was calculated and showed that the Co,Ni-MoS2 catalyst with a high spin moment has a specific capacity of 449.6 mAh g-1 associated with Li2S precipitation. This value is significantly larger than that of 182.5 mAh g-1 obtained for a cell containing the MoS2 catalyst without the spin moment (Fig. 3c). In addition, the nucleation capacity of the Co,Ni-MoS2 catalyst is larger than that of the Co,V-MoS2 and Co,Mn-MoS2 catalysts, which confirms that the deposition kinetics of Li2S are significantly improved with the increased spin moment of the catalysts. In addition, the reverse Li2S decomposition process was examined under potentiostatic charging conditions (Fig. 3b). The Co,Ni-MoS2 catalyst with the high spin moment, again has the strongest dissociation current with a corresponding capacity of 398.5 mAh g-1, which is substantially higher than the value of 221.5 mAh g-1 recorded for the MoS2 catalyst without a spin moment (Fig. 3c). Both the Co,V-MoS2 and Co,Mn-MoS2 catalysts have intermediate capacities, reinforcing the role of spin moment in facilitating Li2S decomposition. In addition, the relationship between the spin moment of the catalyst and the nucleation and decomposition capacity of Li2S was evaluated (Fig. 3c). As the spin moment increases from 0 (MoS2) to 4.62 (Co,Ni-MoS2), the nucleation and decomposition capacity of Li2S increases linearly. This positive linear relationship confirms that the spin moment plays a key role in promoting the formation and decomposition of Li2S. These findings confirm that Co,Ni-MoS2 substantially speeds up both the oxidation and reduction of Li2S, which underscores the effectiveness of modifying the catalyst’s spin moment in facilitating the electrochemical reactions of Li2S.

The improved catalytic deposition of Li2S on Co,Ni-MoS2 was directly observed using an in-situ XRD test, which gives more detailed information on the effectiveness of the increased spin moment for the deposition dynamics of Li2S (Figs. 3d, e). During an ideal discharge process, the crystalline phase of sulfur (-S8), characterized by a primary peak at 23.1°, gradually disappears and changes into a Li2S crystal, characterized by a broad diffraction peak at 26.7°50. For the cell with the MoS2 catalyst, the Li2S peak has a much lower intensity and smaller area during the entire discharge process, indicating sluggish and low conversion to Li2S (Fig. 3d). For the cell with the Co,Ni-MoS2 catalyst, the -S8 signal disappears quickly, and the Li2S peak appears earlier and gradually strengthens until the end of discharge (Fig. 3e). These results offer direct evidence that increasing the spin moment significantly accelerates the Li2S conversion is good agreement with the experimental results of the PITT tests.

Simulations of Li2S reduction and decomposition on the surfaces of these catalysts were conducted. With the increased spin moment, the reaction free energy for the conversion of Li2S2 to Li2S at the Co,Ni-MoS2 site decreased to -1.72 eV, compared to -0.88 eV for Co,Mn-MoS2, -0.19 eV for Co,V-MoS2, and 1.64 eV for MoS2, indicating an improvement in catalytic efficiency for solid-solid conversion (Fig. 1h). In addition, simulations of Li2S decomposition on the surfaces of various catalysts was used to confirm the improved catalytic capability. The energy barrier for Li2S decomposition of Co,Ni-MoS2 is remarkably low at 0.26 eV, representing a reduction of 0.83 eV compared to the value for MoS2, which has a barrier of 1.09 eV (Figs. 3f-i). Furthermore, the Co,Ni-MoS2 catalyst has a lower decomposition barrier than its Co,V-MoS2 and Co,Mn-MoS2 counterparts, suggesting that a high spin moment in the catalyst substantially hastens the Li2S decomposition. These results confirm that the Co,Ni-MoS2 catalyst, with high spin moment coordination, is highly effective in facilitating the Li2S redox reactions.

Previous analysis has indicated that the transition from symmetrical spin-state Li2S2 to Li2S involves an asymmetric spin-state intermediate, introducing spin polarization changes throughout the reaction. This spin-state shift usually requires substantial energy and proceeds slowly, thus significantly hindering the Li2S2 to Li2S conversion. To more deeply understand how the spin density of the catalyst affects this catalytic transformation mechanism, the spin state changes of symmetric spin states of Li2S2 and Li2S combined with MoS2 (no spin moment) and Co,Ni-MoS2 (high spin moment) were simulated. The results show that the reactant (Li2S2-MoS2) and the product (Li2S-MoS2) still have a symmetrical spin state in the DOS of the spin-up and spin-down states after combining with MoS2 without a spin moment (Figs. 3j, k). This indicates that using MoS2 as a catalyst, which lacks a spin moment, still faces problems due to the spin polarization transition involved in the conversion from the Li2S2 to Li2S reaction. In contrast, when the reactant (Li2S2) and the product (Li2S) are combined with Co,Ni-MoS2, both the initial state Li2S2-Co,Ni-MoS2 and the final state Li2S-Co,Ni-MoS2 have an asymmetrical spin state in the DOS (Figs. 3l, m), which indicates that when Li2S2 and Li2S are combined with Co,Ni-MoS2, the reaction from Li2S2 to Li2S becomes a spin-to-spin transition, which can initiate quantum spin exchange interactions and provide a means of spin electron transfer, thereby facilitating the rate-determining step from Li2S2 to Li2S in the electrocatalytic conversion processes.

Overall, the Li2S2 to Li2S conversion is a spin-involved process, and the catalyst’s spin state fundamentally determines whether this step can proceed efficiently. MoS2 without a spin moment forces Li2S2 and Li2S into symmetric spin states, thereby failing to activate the required spin-polarized reaction pathway and resulting in sluggish solid-solid conversion. In contrast, Co,Ni-MoS2 provides strong spin polarization and induces asymmetric spin states for both Li2S2 and Li2S, enabling a spin-to-spin transition pathway that accelerates electron transfer and directly lowers the free-energy barrier.

Potential analysis of the inhibition mechanism of the shuttle effect

To achieve stable cycling and high-performance Li||S batteries, inhibiting the shuttling of polysulfides also plays a vital role. This shuttling is caused by the dissolution and accumulation of soluble LiPSs in electrolytes due to the slow conversion of LiPSs to insoluble Li2S. Therefore, the effective capture and fast conversion of LiPSs are the two key factors to solve this problem. To evaluate the polysulfide adsorption ability of different catalysts, visual LiPSs adsorption tests were conducted by adding an equal amount of each sample to a Li2S6 solution (Fig. 4a). It is noted that MoS2 remained dark after prolonged contact, suggesting a very weak interaction between MoS2 and Li2S6. In contrast, Co,Ni-MoS2 showed an adsorption strength sufficient to effectively capture soluble LiPSs. This finding was supported by UV-visible absorption spectroscopy, where the peak intensity of the Li2S6 solution was significantly reduced after the addition of Co,Ni-MoS2, compared to MoS2. This efficient adsorption of polysulfides plays a crucial role in achieving fast redox kinetics. To assess the electrocatalytic efficiency of the catalysts on soluble LiPSs, the same amount of catalysts was deposited onto electrodes in a symmetric cell, and the cyclic voltammetry (CV) results of the cell are shown in Fig. 4b. The current response and voltage gap between the anodic and cathodic peaks typically correlate with the energy barrier of the conversion process, where a higher response current and narrower gap indicates a lower activation barrier and quicker polysulfide conversion kinetics. Compared to MoS2 with the lowest current response, the rapid increase in current response observed in the cells with the spin moment catalysts indicates a significant improvement in kinetics in the conversion process of LiPSs. A direct comparison of the CV peaks in Fig. 4c reveals that, under identical conditions, Co,Ni-MoS2 has the highest current response and smallest voltage gap, indicating that the high spin moment of the catalyst is better for the catalytic conversion of LiPSs. Furthermore, to demonstrate the rapid redox kinetics from Li2S4 to insoluble solid sulfur species, thus avoiding polysulfide aggregation, the differential charge density between the catalysts and Li2S4 was simulated. The adsorption of polysulfides by two-dimensional layer sulfide catalysts typically induces electron accumulation between surface sulfur atoms and terminal lithium atoms, reflecting the strength of the covalent nature of the Li-S bond. Among all the catalysts studied, Co,Ni-MoS2 induces the highest electron density in the Li-S bond, demonstrating its effective adsorption ability for Li2S4 and ensuring a solid foundation for subsequent rapid catalysis (Figs. 4d-g and Supplementary Fig. 33). Following the Li2S4 to Li2S conversion, the transformation from low-order soluble Li2S4 to insoluble Li2S is generally regarded as the second most challenging step in the entire SRR process. This step typically involves a high activation energy barrier. Therefore, assessing the difference in activation energy for different catalytic systems provides a reliable means of evaluating the catalyst’s effectiveness in increasing the system’s redox kinetics. The activation energy barrier of the SRR at a given voltage of 2.0 V (considered to be the conversion step from Li2S4 to Li2S) was then determined by fitting the charge transfer resistance measured by electrochemical impedance spectroscopy (EIS) at different temperatures to the Arrhenius equation51. Based on the results, Fig. 4h and Supplementary Fig. 34 show the Nyquist plots of these catalyst-based cells at different temperatures. In line with the Arrhenius equation, the charge transfer resistance shows a linear dependency between the logarithm of its reciprocal value and the inverse of the absolute temperature at varying temperatures for the studied catalysts (Fig. 4i). Overall, the resulting Ea value of the MoS2 based cell is 22.45 kJ mol-1, which is substantially higher than those observed for Co,V-MoS2 (15.88 kJ mol-1), Co,Mn-MoS2 (14.00 kJ mol-1), and Co,Ni-MoS2 (9.79 kJ mol-1) (Fig. 4j). Because of the lower activation energy barrier for a cell incorporating the Co,Ni-MoS2 catalyst, fast reaction kinetics for the Li2S4 to Li2S catalytic conversion were found. The above results unequivocally show that the Co,Ni-MoS2 catalyst with a high spin moment has suitable adsorption and faster redox kinetics than the MoS2 catalyst without a spin moment, which is believed to effectively avoid the aggregation of soluble polysulfides in electrolytes, thereby inhibiting their shuttling effect.

In-situ Raman tests were analyzed to confirm this exciting result. Supplementary Fig. 35 shows the setup for the experiment. During testing, polysulfides diffuse through the separator toward the lithium negative electrode. The Raman laser can detect the characteristic signals of polysulfides through a quartz window and small apertures in the lithium electrode. When the battery experiences a severe shuttling effect, pronounced polysulfide signal peaks will be observed. However, if the catalyst effectively prevents polysulfide shuttling, the Raman spectrum will show no polysulfide-related peaks51. Using this approach, the behavior of a Li||S battery using a MoS2 catalyst was analyzed (Figs. 4k, l). At the start of the discharge process, a distinct peak at 281.5 cm-1, corresponding to Li2S8, was detected (Fig. 4l). As discharge progressed, peaks attributed to S62- and S42- emerged and persisted throughout the reaction until the end of the discharge process. These observations confirm the presence of significant polysulfide shuttling, leading to reduced sulfur utilization and a loss of capacity. In contrast, when Co,Ni-MoS2 was used as the catalyst, only weak signals for S62- and S82- were detected during discharge, which clearly shows the ability of Co,Ni-MoS2 to effectively prevent the polysulfide shuttling in the Li||S system (Figs. 4m, n).

These results reveal that the high spin moment of Co,Ni-MoS2 plays an important role in suppressing the shuttle effect by simultaneously enhancing polysulfide adsorption and accelerating their redox conversion. The spin-polarized active sites strengthen electronic coupling with soluble polysulfides, resulting in stronger charge accumulation at Li-S bonds and markedly improved adsorption capability compared with spin-inactive MoS2. This spin-induced electronic activation also lowers the energy barrier for Li2S2 to Li2S conversion among all catalysts. By enabling stronger spin-dependent interactions and faster catalytic transformation, the spin-polarized Co,Ni-MoS2 effectively prevents LiPSs from diffusing into the electrolyte, thereby mitigating the shuttle effect and promoting more stable long-term cycling.

Electrochemical properties of high-energy-density Li||S batteries

Because of its strong polysulfide adsorption, faster redox kinetics, reduced activation energy, suppression of the shuttling, and facilitation of the rate-determining Li2S2 to Li2S conversion, the Co,Ni-MoS2 catalyst enables in producing Li||S batteries with good performance. Consequently, the electrochemical performance of Li||S batteries using these catalysts has been assessed (Fig. 5). The CV curves of Li||S cells with these catalysts were analyzed by scanning in the voltage range of 1.7-2.8 V at scan rates ranging from 0.1 to 0.5 mV s-1 (Figs. 5a, b and Supplementary Fig. 36). All the CV curves have the characteristic redox profiles of Li||S batteries. During the reduction phase, the Co,Ni-MoS2-based cells have distinct reduction peaks at 2.31 V (peak A) and 2.02 V (peak B), corresponding to the multiphase reduction of solid S8 to soluble LiPSs and subsequently to insoluble Li2S. In the oxidation phase, two peaks at 2.33 V (peak C) and 2.41 V (peak D) represent the oxidation of Li2S to Li2Sx and eventually to S8. More importantly, the Co,Ni-MoS2 cells display the highest peak current response and the most favorable onset potentials for reduction and oxidation of all the cells, indicating an accelerated redox process throughout charge and discharge (Supplementary Fig. 37a). Tafel curves obtained from the corresponding CV curves during oxidation and reduction, are shown in Supplementary Figs. 37b-d. Peak B is associated with Li2S nucleation kinetics, while peak C corresponds to Li2S dissociation kinetics. The fitted Tafel slopes for MoS2, Co,V-MoS2, Co,Mn-MoS2, and Co,Ni-MoS2-based cells are respectively 93.6, 77.6, 65.9, and 45.7 mV dec-1 for peak B, and 103.7, 84.2, 76.7, and 56.4 mV dec-1 for peak C. The fitted Tafel slopes for Co,Ni-MoS2-based cells show a reduction compared to those for cells using pure MoS2. These results also highlight the catalytic performance of the Co,Ni-MoS2-based cells in promoting both Li2S nucleation and dissociation, consistent with the abovementioned results. In addition, a linear fitting model was used to illustrate the correlation between the peak current and the square root of the scan rate, indicating that the process is diffusion-limited. The dynamics of Li-ion diffusion were analyzed using the Randles-Sevcik equation, from which it is clear that the cell containing Co,Ni-MoS2 gives steeper fitted curves, indicating considerably higher Li-ion diffusion coefficients than other cells in all the reaction stages (Supplementary Fig. 38).

Fig. 5c shows the rate performance of Li||S batteries using different catalysts, tested at rates ranging from 335 mA g-1 to 6700 mA g-1 then back to 837.5 mA g-1. The Co,Ni-MoS2-based cell gives distinct charge and discharge plateaus, even at a high current rate of 6700 mA g-1, and delivers reversible capacities of 1235, 982, and 631 mAh g-1 at 335, 1675, and 6700 mA g-1, respectively. In contrast, due to their lower catalytic efficiencies, the Co,Mn-MoS2, Co,V-MoS2, and pure MoS2-based cells have lower capacities, particularly under high-rate conditions. This difference is caused by the different catalytic efficiencies introduced by incorporating different metal cation pairs into MoS2. A comparison of the charge-discharge profiles at identical current densities shows that the Co,Ni-MoS2-based cell has the lowest overpotential and polarization, which provides confirmation of its catalytic efficiency and favorable reaction kinetics (Supplementary Fig. 39).

Cycling tests were conducted to evaluate the durability of the batteries with a sulfur loading of approximately 2 mg cm-2 (Figs. 5d-f). After 100 cycles at 502.5 mA g-1, the cell with pure MoS2 retains a reversible discharge capacity of 563.9 mAh g-1. In contrast, the Co,Ni-MoS2-based cell shows a better performance, maintaining a capacity of 1077.5 mAh g-1 after 100 cycles (Figs. 5d, e). The Co,Ni-MoS2-based cell also has a low capacity decay rate of only 0.191% per cycle. By comparison, the capacity fade rates for cells containing the Co,Mn-MoS2, Co,V-MoS2, and pristine MoS2 catalysts are respectively 0.271%, 0.311%, and 0.482% per cycle. This clear difference underscores the performance improvements enabled by the increased SRR catalytic activity. To verify the catalytic stability of the material over extended cycling, the long cycle test was conducted at 3350 mA g-1 (Fig. 5g). The Co,Ni-MoS2-based cell retains a capacity of 595.1 mAh g-1 after 500 cycles, with a minimal average capacity loss of 0.06% per cycle. In contrast, the MoS2-based cell has a much lower reversible discharge capacity, decreasing to 335.7 mAh g-1 after 500 cycles, with an average capacity degradation of 0.106% per cycle. The electrochemical performance at a higher current density of 8375 mA g-1 was further evaluated, as shown in Supplementary Figs. 40A and 41. The MoS2-based cell is unable to sustain effective charge-discharge cycling, whereas the Co,Ni-MoS2-based cell retains a higher reversible capacity with stable cycling behavior, retaining 503 mAh g-1 after 400 cycles. These results indicate that Co and Ni doping effectively enhances sulfur redox kinetics, enabling reliable operation under high-rate conditions. In addition, the cycling performance at low temperatures was further evaluated. As shown in Supplementary Fig. 42, the Co,Ni-MoS2-based cell maintains a higher reversible capacity and improved cycling stability over a wide temperature range from 0 °C down to -20 °C, whereas the pristine MoS2 cell suffers from severe performance degradation. Based on these results, it is evident that without the influence of spin moments, MoS2 suffers an inferior cycling stability. In contrast, the presence of spin moments accelerates the conversion between Li2S2 and Li2S, prevents dead sulfur accumulation, and sustains continuous catalytic transformation of LiPSs, which suppresses polysulfide shuttling and improves sulfur utilization, thereby ensuring high capacity and stable cycling performance (Supplementary Table 9). Post-cycling characterizations were conducted to evaluate the structural, coordination environment, and spin-state stability of the Co,Ni-MoS2 catalyst. XRD and TEM analyses confirm that the 2H-MoS2 phase, lattice fringes, and crystal structure remain well preserved after prolonged cycling, indicating the absence of structural degradation or phase transformation (Supplementary Figs. 43 and 44). XPS spectra of Co 2p and Ni 2p show nearly unchanged binding energies and peak features, suggesting stable oxidation states without metal dissolution or chemical poisoning (Supplementary Fig. 45). Moreover, magnetic susceptibility measurements reveal that the localized magnetic moments of the spin-active Co/Ni sites are retained after cycling (Supplementary Fig. 46). Consistently, Li||S cells reassembled with the cycled catalyst exhibit well-defined redox features and preserved electrochemical kinetics (Supplementary Fig. 47). Together, these results demonstrate that the Co and Ni dopants remain structurally anchored, chemically stable, and spin-active during long-term operation, confirming the durability of the spin-regulated catalytic.

To demonstrate the ability of the Co,Ni-MoS2 catalyst for practical applications, high specific energy Li||S cells with a high sulfur loading of about 4.5 mg cm-2 were investigated, and the long cycling performance at 167.5 mA g-1 and rate performance from 167.5 mA g-1 to 1675 mA g-1 then back to 167.5 mA g-1 were tested (Supplementary Figs. 48 and 49). The Co,Ni-MoS2-based cells have stable cycling and reversible capacities of 1192, 1033, 937, and 669 mAh g-1 at 167.5, 335, 837.5, and 1675 mA g-1, respectively. Ah-scale pouch cells were also tested to assess their practical use with a high sulfur loading and a lean electrolyte. The mass distribution of each component in the pouch cell is shown in Supplementary Fig. 50 and Supplementary Table 10. The sulfur loading in this pouch cell reached 10.2 g, surpassing that of most previously reported Li||S pouch cells, and achieved a capacity of 13.2 Ah and a specific energy of 435 Wh kg-1 under low electrolyte conditions (E/S ratio of 3.2 ul mg-1) at a current of about 1000 mA, while having a stable cycling performance (Fig. 5h and Supplementary Figs. 51-53). Given its electrochemical stability, a comparison with previously reported pouch cells was made, and our cell has both high capacity (Ah) and specific energy (Supplementary Fig. 54 and Supplementary Table 11).7,51-61 To sum up, concerning specific energy, cycle life, and capacity retention, the Li||S batteries using the Co,Ni-MoS2 catalyst have a faster rate-determining conversion step, giving mechanistic understanding for the future design of high-energy-density batteries.

In summary, this study offers critical insights into the effect of changing the spin moment in bi-metallic catalysts on the rate-limiting step of electrocatalytic sulfur reduction reaction in Li||S batteries. Through a detailed investigation of the Li2S2 to Li2S conversion process, we found that the sluggish kinetics of this step are governed by spin-state transitions in the intermediate states. A series of bi-transition metal dopants (V-Mn, V-Fe, V-Co, V-Ni, Mn-Fe, Mn-Co, Mn-Ni, Fe-Co, Fe-Ni, Co-Ni) were designed and incorporated into the structure of a MoS2 catalyst to systematically explore the relationship between the spin environment and catalytic activity in the SRR rate-limiting step. DFT calculations showed a negative correlation between the spin density of the active sites and the thermodynamic energy barrier for Li2S2 conversion, indicating that a higher spin density is associated with faster Li2S deposition kinetics. Among these catalysts, Co,Ni-MoS2, with the highest spin moment, modifies the spin states of both the initial and final reactants, forming spin-electronic transfer channels at the active sites, thereby reducing the energy barrier and promoting the conversion of Li2S2 to Li2S. Experimental confirmation that Li||S cells with the Co,Ni-MoS2 catalyst have rapid sulfur conversion for both Li2S reduction and oxidation reactions, almost no accumulation of polysulfides, and no polysulfide shuttling, which together increase sulfur utilization. They, therefore, have a high specific capacity, stable cycling performance, and rate capability. A 13.2 Ah-level Li||S pouch cell with the Co,Ni-MoS2 catalyst had a specific energy of 435 Wh kg-1 at a high sulfur loading. Overall, these findings provide an understanding of the fundamental role of spin moments in sulfur reduction reaction kinetics and enable to produce efficient, durable catalysts for next-generation Li||S batteries.

Methods
Preparation of M1,M2-MoS2 catalysts

All the chemicals were used as received with Aladdin without further purification. The Co,Ni-MoS2 catalysts were synthesized following the procedure. Initially, 0.5 mmol of a molybdenum precursor (NH4)6Mo7O24·4H2O (Sigma-Aldrich, 99.98%), 0.1 mmol of metal salts Co(CH3COO)2·4H2O (Sigma-Aldrich, ≥98%), and 0.1 mmol Ni(CH3COO)2·4H2O (Sigma-Aldrich, ≥99%) were mixed in 60 ml deionized (DI) water under stirring conditions. Thiourea (16 mmol, Sigma-Aldrich, ≥99%) and thioacetamide (4 mmol, Sigma-Aldrich, ≥99%) were then added to this solution under vigorous stirring for 60 min, after which the obtained precursor solution was transferred to a Teflon-lined stainless-steel autoclave and heated at 220 °C for 18 h. After the autoclave was allowed to cool naturally to room temperature (27 ± 0.5 oC), the resulting precipitate was repeatedly washed with deionized water and absolute ethanol and then dried in a vacuum oven at 60 °C for 12 hours. The resulting powder was then heated at 400 °C for 2 hours in an Ar atmosphere and subsequently cooled to room temperature. By replacing the metal salt precursors (Ni(CH3COO)2·4H2O) with (Mn(CH3COO)2·4H2O) (Sigma-Aldrich, ≥99%), or (NH4VO3) (Sigma-Aldrich, ≥99%), the Co,Mn-MoS2 and Co,V-MoS2 catalysts were also produced. The Co-MoS2, Ni-MoS2 and pure MoS2 catalysts were synthesized using a similar method, except using metal salts Co(CH3COO)2·4H2O, Ni(CH3COO)2·4H2O, and no salt, respectively.

Physicochemical characterization

Powder X-ray diffraction (PXRD) patterns of all samples were recorded using a Rigaku Smartlab 9000W diffractometer equipped with Cu Kα radiation (λ = 0.15418 nm), operating at 40 kV and 40 mA. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images were respectively acquired with a JEOL JSM-7500FA field emission scanning electron microscope and a JEOL JEM-2100 electron microscope at an accelerating voltage of 200 kV. X-ray absorption fine structure (XAFS) data were obtained at the XAFACA beamline of Singapore Synchrotron Light Sources (SSLS), and the data were processed according to the standard procedures in ATHENA module within the Demeter software packages. All the samples were measured in transmission mode, and the corresponding metal foils or powder were measured simultaneously with the sample for energy calibration.

Visualized adsorption and UV-vis absorption measurement

A Li2S6 solution was prepared by dissolving sublimed sulfur (Sigma-Aldrich, ≥99.98%) and Li2S (Sigma-Aldrich, ≥99.98%) in a stoichiometric ratio of 5:1 in a mixed solvent of 1,3-dioxolane (DOL) (Sigma-Aldrich, ≥99.8%) and dimethoxyethane (DME) (Sigma-Aldrich, ≥99.9%), followed by vigorous stirring at 70 °C for 12 hours under an argon atmosphere. The catalyst powder was added to the Li2S6 solution, and ultraviolet-visible absorption spectra were collected after the samples were soaked in the solution for 3 hours. All procedures were conducted in an argon-filled glove box.

Li2S nucleation and dissociation study

Li2S8 cells were examined by chronoamperometry to study Li2S nucleation kinetics. A 0.2 mol L-1 Li2S8 electrolyte was prepared by dissolving sulfur and Li2S in a stoichiometric ratio of 7:1 in tetraglyme (Sigma-Aldrich, ≥99%), followed by magnetic stirring at 60 °C for 24 hours under an argon atmosphere. The experimental setup included a Celgard 2400 PP separator (thickness of 25 um, porosity of 41%, Canrd Technology Co., Ltd.) positioned between a working electrode composed of carbon paper (Canrd Technology Co., Ltd.) loaded with the different catalysts/CNTs (XFNANO) and a lithium metal negative electrode (diameter of 16 mm and a thickness of 0.2 mm prior, CEL, 99.9%).

For preparation, 20 μL of the synthesized Li2S8 electrolyte was applied to the working electrode side, while 25 μL of a conventional electrolyte was added to the lithium negative electrode side. To analyze Li2S nucleation, the batteries were initially discharged at 2.06 V with a current of 0.1 mA and then further discharged at a constant voltage of 2.05 V until the discharge current dropped to 10-5 A. This procedure allowed for the evaluation of the full charge and assessment of Li2S nucleation kinetics.

For Li2S decomposition, the same cells used in the nucleation test were used. They were discharged at 1.7 V with a current of 0.1 mA and then discharged further at the same voltage with a current of 0.01 mA to ensure complete Li2S formation. Following full conversion, the cells were charged at a constant voltage of 2.4 V until the current decreased below 10-5 A, ensuring complete Li2S decomposition. The capacities for Li2S nucleation and decomposition were calculated by integrating the corresponding curves using Faraday’s Law.

Cell assembly and electrochemical measurements

Sulfur was impregnated into CNTs (XFNANO, ≥95%) using a melt-diffusion method at 155 °C for 10 h, resulting in S/CNTs composites with an 80 wt% sulfur content. The carbon-sulfur composite, conductive carbon (XFNANO), and polyvinylidene fluoride (PVDF, Mw: 1000000 g mol-1, Canrd Technology Co., Ltd.) were combined in a mixer at ratios of 80:10:10, with the addition of an appropriate amount of 1-methyl-2-pyrrolidinone (NMP, Aladdin, ≥99.9%) as a solvent to create a homogeneous slurry. This slurry was then uniformly applied to a carbon-coated aluminum foil (Canrd Technology Co., Ltd.) current collector using a mold, which was then dried at 60 oC for 12 hours. A disk was punched out from the dried material to create positive electrodes. The total mass loading of sulfur was about 1.5-2 mg cm-2 for typical measurements, while a larger sulfur loading of 4.5 mg cm-2 was also investigated. The catalyst-modified separator was prepared by a simple surface coating method. 60 wt% catalyst, 30 wt% conductive carbon, and 10 wt% PVDF were mixed in a NMP solvent with vigorous stirring to form a homogeneous slurry, which was then coated onto a clean Celgard PP separator. The functionalized separator was dried at 60 oC overnight under vacuum. Electrochemical tests were conducted by assembling coin-type (CR2032) cells in an Ar-filled glove box (Super 1220/750, Mikrouna), with H2O and O2 levels less than 0.1 ppm. The negative electrode was Li metal foil (50 μm thickness, CEL, 99.9%). The electrolyte was composed of 1 M lithium bis-trifluoromethane sulfonylimide (LiTFSI) and 1 wt.% LiNO3 in a mixed solvent of DOL and DME (V_DOL/V_DME=1:1). For the electrochemical performance tests, the electrolyte/sulfur (E/S) ratio was approximately 11 L mg-1 under the low-loading conditions, and approximately 6 L mg-1 under the high-loading conditions. The charge/discharge tests were measured from 1.7 V to 2.8 V by using a battery test system (Neware CT-4000 battery tester from Shenzhen, China). The CV was measured on a Bio-Logic EC-LAB (VMP-300) electrochemical workstation, and the EIS was obtained in the frequency range from 0.01 Hz to 100 kHz at different temperatures at a potentiostatic voltage of 2.0 V (open-circuit voltage time applied before carrying out the EIS measurement).

Assembly of Li||S pouch cells. Typically, for the sulfur positive electrode used in pouch cells, a S/carbon composite, conductive carbon, LA-133, and Co,Ni-MoS2 were dispersed in an aqueous solution in mass ratios of 80:11:6:3 and stirred thoroughly for 12 hours. The resulting slurry was then dropped onto a carbon-coated aluminum foil and transferred to a 60°C heating plate to evaporate the solvent, to form the sulfur positive electrode for the cell. Lithium foil was used as the negative electrode, and Celgard PP as the separator. The electrolyte consisted of DOL/DME (1:1 by volume) with 1.5 M LiTFSI and 0.3 M LiNO3 as additives. The relevant parameters of the pouch cells are given in the Supplementary Table 10. The pouch cells were cycled under a constant stack pressure of 0.2 MPa. Unless otherwise specified, all electrochemical measurements were conducted in an air-conditioned laboratory at 27 ± 0.5 °C. Cycling tests were performed in a temperature-controlled chamber maintained at 27 ± 0.1 °C. All cell disassembly and ex situ sampling were conducted in an argon-filled glovebox. The harvested electrodes were transferred using airtight containers to the characterization instruments without air exposure. Sample preparation, storage, and transportation were carried out under strictly controlled inert conditions.

Computational methods

Density functional theory (DFT) calculations were conducted using the Vienna Ab initio Simulation Package (VASP). Structural optimizations were performed with a kinetic energy cut-off energy of 450 eV and a 2  2  1 k-points mesh for the first Brillouin zone. The conjugate-gradient algorithm was utilized to fully relax all structures until the total energy and Hellmann-Feynman forces were below 1 × 10-5 eV and 0.05 eV Å-1, respectively. A supercell size of 5  5  1 with a vacuum distance of approximately 15 Å was used to minimize interactions between the substrate and adsorbates. The adsorption energy was calculated by subtracting the energy of the intermediate state from the total energy after adsorption. The interaction energy between the substrate and the adsorbed molecule was evaluated as ΔE = E_surf+ads - (E_surf + E_ads), where E_surf+ads represent the energy of the absorbed system, E_ads is the energy of the adsorbate in a vacuum, and E_surf is the energy of the optimized bare surface. Li2S decomposition equation (Li2S → LiS + Li+ + e-) was simulated using the climbing image nudged elastic band method.

Interpretable multi-view machine-learned framework

Data collection

In this study, data collection was carried out based on first-principles calculations. The two atom doping configurations of MoS2 are illustrated in Supplementary Fig. 7. Using DFT, we calculated the ΔG for the Li2S2-to-Li2S conversion on various dual-doped MoS2 surfaces.

Parameter selection

To ensure effective model construction and enable interpretable insights into underlying chemical principles, we established three core criteria for feature selection: (1) features must be closely related to the catalytic performance of the material to ensure clear physical meaning; (2) features should accurately represent the geometric structure of the material; and (3) features should be easily obtainable and reproducible, facilitating data extension and model transferability.

Building upon previous research, we further expanded the feature space. Previous studies have demonstrated a strong correlation between adsorption energy and catalytic activity; thus, the adsorption energies of Li2S2 and Li2S on catalyst surfaces were included as key features. According to the Sabatier principle, the electronic structure of active catalytic sites plays a pivotal role in determining catalytic behavior. Therefore, we incorporated several electronic descriptors, including spin density, the d-band center of transition metals, the p-band center of non-transition metals, and the charge transfer induced by doping.

In addition, various fundamental physicochemical properties potentially affecting catalytic performance were considered, such as the first ionization energy of the metal atoms and the Pauling electronegativity of active-site elements (including both metals and non-metals). To more comprehensively capture the structural characteristics of the materials, geometric and electronic structure parameters such as bond lengths, charge distributions, and d-band centers corresponding to different doping elements were also included.

We also augmented the feature set with elemental properties sourced from the Mendeleev database, incorporating descriptors such as atomic mass, valence electron count, and melting point. These physicochemical features were used to construct machine learning models targeting the reaction ΔG, with the aim of uncovering the key factors influencing catalytic performance. Details and definitions of the elemental property data can be found at: https://mendeleev.readthedocs.io/en/stable/data.html.

Selection of machine learning methods

In this study, multiple machine learning algorithms were employed to analyze and predict target features, including Random Forest Regressor, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Ridge Regression, and eXtreme Gradient Boosting (XGBoost) Regressor. By comparing the predictive performance of these models, the objective was to identify the most suitable algorithm for the specific tasks of this research.

To increase the robustness of model evaluation, the Leave-One-Out Cross-Validation approach was adopted. In each iteration of this method, a single data point was held out as the validation set while the remaining samples were used for training. This process was repeated until every sample has served once as the validation set. This validation strategy effectively reduces model bias and improves generalization performance.

Hyperparameter optimization of regression models

To improve the regression models, a hybrid strategy combining automated search and manual tuning was employed for hyperparameter optimization. Initially, potential optimal hyperparameter combinations were identified using automated techniques such as Grid Search. Subsequently, fine-tuning was performed manually based on model performance on the validation set, aiming to improve both generalization ability and predictive accuracy.

Specifically, for the Random Forest Regressor, key parameters such as the number of data and the maximum data depth were optimized. For the LASSO Regressor, a systematic exploration of different regularization strengths (α) was conducted to determine the most appropriate level of sparsity. Similarly, for the Ridge Regressor, the regularization parameter (α) was optimized through a combination of automated and manual tuning to achieve a balanced performance across both training and validation datasets. For the XGBoost Regressor, a joint optimization of critical parameters such as the number of estimators, maximum depth, and learning rate was carried out to maximize predictive accuracy.

During each round of cross-validation, model performance was quantitatively assessed by recording the Coefficient of Determination (R2, Equation (1)) and the Mean Squared Error (MSE, Equation (2)). These metrics were used to compare and evaluate the effectiveness of each regression model.

Equation (1): 𝑅2 = 1 − \frac{\sum_{i}(Y_i− y_i )^2}{\sum_{i}(Y_i− \bar{Y}) ^2 }

Equation (2): MSE = \frac{1}{N}\sum_{i=1}^{n} (Y_i − y_i)^2

Y represents the true values obtained from DFT calculations, while y denotes the predictions made by the machine learning models, and Y is the mean value of the DFT data. An ideal model should have an R2 value close to 1 and an MSE value close to 0. Ultimately, the hyperparameter combination that performs best on the validation set is selected as the final configuration for each model. The performance of the different models across various metrics is shown in Supplementary Fig. 14.

Feature importance analysis

The SHapley Additive exPlanations (SHAP) method, proposed by Lundberg and Lee in 2017, is based on the Shapley values from cooperative game theory and is designed to provide interpretable explanations for model predictions. In this study, we used SHAP to perform a posteriori quantitative assessment of feature importance, aiming to estimate the contribution of each input feature to the model’s predictive outcomes.

For each individual data point, SHAP perturbs the input features and computes their marginal contributions to the prediction, resulting in a corresponding “importance value.” By aggregating SHAP values across all samples and calculating the mean, we can further evaluate the global importance of each feature within the entire dataset. This dual capability enables SHAP to offer both local interpretability (explaining individual predictions) and global interpretability (assessing feature impact across the entire model), making it a powerful and widely applicable tool. Features with higher importance exhibit larger SHAP values, while less influential features tend to have smaller or near-zero SHAP values.

As a model-agnostic interpretation framework, SHAP can be applied to various machine learning models, allowing for a transparent visualization and understanding of how input features influence the model’s decision-making process. In this work, we used SHAP to analyze six key features in our model, thereby identifying the variables with the most significant impact on prediction outcomes.

The SHAP value for a feature was computed using the following formula:

Φ_i = \sum_{S \subseteq F \setminus \{i\}} \frac{|S|!(|F|-|S|-1)!}{|F|!}[ f_{S \cup \{i\}}(x_{S \cup \{i\}}) - f_S(x_S) ]

Φi denotes the SHAP value of feature i, S is a subset of the full feature set F, X represents the input values corresponding to subset S, and f is the output function of the trained model. By evaluating the change in model output when feature i is added to subset S, the marginal contribution of feature i can be quantitatively determined.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, the authors combine machine learning with density functional theory (DFT) calculations, complemented by experimental mechanistic studies and electrochemical validation, to elucidate the intrinsic origins of the sluggish Li₂S₂ → Li₂S solid–solid conversion step in Li–S batteries. Guided by theoretical predictions, a series of catalysts are designed and demonstrated to regulate the spin state of the intermediates, thereby lowering the energy barrier associated with spin-state transitions and enabling high-energy-density Li–S batteries. The integration of theoretical and experimental approaches is compelling, and the findings provide novel insights into a critical step of the Li–S redox process. Overall, the study is innovative, the results are coherent and mutually supportive, and the conclusions are of significant importance to the Li–S battery field. I recommend publication after the authors address the following minor revisions:\n1. The DFT analysis systematically evaluates the Gibbs free energies of different catalysts along multiple reaction pathways. To improve clarity and facilitate comparison, the authors are encouraged to include a supplementary table corresponding to Figures 1b–d that summarizes the Gibbs free energies of all relevant intermediates for the different catalysts.\n2. Transition-metal compounds are widely studied as anode materials in Li-ion batteries. In the context of Li–S batteries, could the catalysts employed in this work also contribute to the overall electrochemical capacity during cycling? A brief discussion would strengthen the manuscript.\n3. The manuscript does not present experimental evidence regarding changes in the metallicity of MoS₂ after element doping. The inclusion of corresponding measurements (e.g., electrical conductivity or spectroscopic analyses) would enhance the discussion.\n4. The role of spin polarization in the target catalyst during the conversion of sulfur species deserves further clarification. A more detailed mechanistic explanation would help the reader better appreciate the influence of spin effects on the overall reaction process.\n5. Supplementary Fig. 30 should be revised. Raman spectroscopy is a scattering phenomenon; the observed signals correspond to inelastically scattered photons rather than refracted light.", "reviewer_2": "Reject\nThis paper investigates the role of spin moments in bi-metallic catalysts for lithium-sulfur batteries, finds spin-state transitions cause sluggish kinetics in the Li2S2 to Li2S conversion, and shows Co,Ni-MoS2 catalyst improves battery performance by regulating spin states. However, I do not recommend this manuscript for publication due to the following concerns:\n1.A 29-dimensional feature space is constructed in the paper to explore the influence of the intrinsic properties of catalysts on the rate-determining step of the reaction. However, it is not clearly explained how these features are selected, whether they have undergone strict redundancy testing, and how to ensure that these features can comprehensively and accurately reflect the correlation between the catalyst and the reaction. This will affect the reliability of subsequent machine-learning models.\n2.When analyzing the linear relationship between the spin moment and the free energy change of the rate-determining step, it is only based on the catalyst system studied in the paper, without comparative verification with other similar bimetallic-doped MoS2 catalyst systems or catalyst systems with different matrices. Is such a linear relationship universal?\n3.The paper mentions that atomic pair doping can effectively induce spin polarization in catalysts. However, there is a lack of detailed comparative analysis on the differences in the mechanisms by which different atomic pairs (such as Co-Ni, Co-Mn, and Co-V) induce spin polarization and the specific impacts of these differences on catalytic performance, which is not conducive to a deep understanding of the role of doped atomic pairs.\n4.The paper shows through various tests and simulations that the Co,Ni-MoS2 catalyst has excellent performance. However, there is a lack of research on the catalytic performance of this catalyst under high-rate charge-discharge conditions (such as 5C, 10C, etc.) and the influence of spin moment on reaction kinetics at high rates. High-rate performance is crucial in practical applications. How do the authors plan to supplement this?\n5.The paper mentions that the Co,Ni-MoS₂ catalyst can improve battery performance. However, the influence of this catalyst on the electrochemical performance of batteries under different temperature environments is not covered in the paper, and temperature is a non-negligible variable in actual use.", "reviewer_3": "Comments: NCOMMS-25-63975-T\nThis work is highly innovative and profound. It is the first time that the spin-state engineering strategy has been systematically applied to the solid-solid conversion reaction of lithium-sulfur batteries, breaking through the traditional understanding of the rate bottleneck in this field. By integrating theoretical calculations, machine learning, and experimental validation, the authors successfully revealed the negative correlation between the spin moment and the reaction barrier, and designed a Co,Ni-MoS2 catalyst with high performance. The developed pouch battery features both high capacity and high energy density, fully demonstrating its significant guiding significance for the development of the next generation of high-energy batteries. However, several specific issues must be further modified, and a reconsideration is suggested for publication in Nature communications after a minor revision.\nSpecific issues are as follows:\n(1) In fact, the 1T phase of MoS2 has a magnetic moment, while the 2H phase has no single electron. This article should indicate which item the calculated and prepared MoS2 belongs to, as well as whether the phase changes after diatomic doping.\n(2) The authors conducted a comprehensive study on a series of bimetallic dopants and identified Co,Ni-MoS2 as the catalyst with the best performance. However, given that Fe is a common and economically efficient transition metal, the lack of Co,Fe-MoS2 samples in the experimental comparison deserves attention. Can the authors explain why this specific combination was not synthesized and tested?\n(3) What are the reasons for choosing bimetallic atoms to doping MoS2 in this work? The introduction does not explain clearly. It is necessary to clarify the advantages of bimetallic atoms in regulating spin states compared to single-atom, heterojunction, etc.\n(4) In the article, STEM and XAFS have been utilized to demonstrate the successful doping of Co and Ni before cycling, but the structural characterization of the corresponding catalysts after cycling is lacking. Is the catalyst poisoned? Will Co and Ni dissolve out? Whether the valence state, coordination environment and local magnetic moment of Co/Ni have changed?\n(5) The innovation of this paper is that the spin moment of the catalyst regulates the spin state of the reaction intermediate, thereby lowering the energy barrier. However, all the evidence (such as DOS calculations and magnetic susceptibility tests) is a characterization of the static and initial state. There is a lack of experimental data for directly detecting spin state changes during electrochemical reactions.\n(6) In data-driven machine learning models, should the contributions of electronic conductivity and lattice strain be further considered among the existing 29 dimensions?\n(7) The following literature in the same direction may be helpful:\nAngew. Chem. Int. Ed. 2025, 64, e202416176."}, "author_response": "Response to Reviewers’ Comments\nDear Editor and Reviewers:\nWe sincerely appreciate your time and valuable feedback on the manuscript entitled\n“Breaking the Rate-Limiting Barrier in Solid-Solid Sulfur Redox Reactions via Spin-\nState Engineering” (Research Article, NCOMMS-25-63975-T). Your professional\ncomments and suggestions have greatly contributed to improving the scientific quality\nof our manuscript.\nWe have carefully considered all suggestions and incorporated the necessary revisions.\nTo indicate substantial changes, we have highlighted the modified text in the revised\nmanuscript with a yellow background. Below, we provide detailed point-by-point\nresponses to each of the reviewers’ comments.\nThank you again for your time and contribution. We look forward to hearing from you\nsoon!\nProf. Kwun Nam Hui\nOn behalf of all the co-authors\nThe point-to-point responses are summarized in the following:\nReviewer #1\nIn this manuscript, the authors combine machine learning with density functional theory\n(DFT) calculations, complemented by experimental mechanistic studies and\nelectrochemical validation, to elucidate the intrinsic origins of the sluggish Li2S2→\nLi2S solid-solid conversion step in Li-S batteries. Guided by theoretical predictions, a\nseries of catalysts are designed and demonstrated to regulate the spin state of the\n1\nintermediates, thereby lowering the energy barrier associated with spin-state\ntransitions and enabling high-energy-density Li-S batteries. The integration of theoretical and\nexperimental approaches is compelling, and the findings provide novel insights into a\ncritical step of the Li-S redox process. Overall, the study is innovative, the results are\ncoherent and mutually supportive, and the conclusions are of significant importance to\nthe Li-S battery field. I recommend publication after the authors address the following\nminor revisions:\nResponse: We are very grateful for your approval of this work. We also sincerely\nappreciate your valuable comments and suggestions, which are undoubtedly beneficial\nto improving the quality of our manuscript. According to your helpful advice, some\nnecessary data and corresponding discussion have been added to the revised manuscript.\nThe responses to your specific comments are listed below.\nComment #1: The DFT analysis systematically evaluates the Gibbs free energies of\ndifferent catalysts along multiple reaction pathways. To improve clarity and facilitate\ncomparison, the authors are encouraged to include a supplementary table corresponding\nto Figures 1b-d that summarizes the Gibbs free energies of all relevant intermediates\nfor the different catalysts.\nResponse: We sincerely thank you for your valuable suggestion. To enhance clarity and\nfacilitate direct comparison among the different catalysts, we have added a\nsupplementary table (Supplementary Table 2) that summarizes the calculated Gibbs\nfree energies of all key intermediates involved in the reaction pathways corresponding\nto Figs. 1b-d. This table provides a comprehensive overview of the energetics for each\nstep, allowing readers to more clearly understand the relative catalytic behaviors and\nenergy profiles of the different systems. The corresponding data have been added to the\nrevised Supplementary Table 2.\nSupplementary Table 2 | The calculated Gibbs free energies of all key intermediates\ninvolved in the reaction pathways.\n2\nG1i G1ii G2i G2ii G3i G3ii\nPristine 2.1353 -3.2256 1.6413 -1.6128 1.6603 -2.7506\nV,Mn-MoS 1.0792 -2.8208 1.3598 -1.4104 1.2502 -2.9918\nLi-ion batteries. In the context of Li-S batteries, could the catalysts employed in this\nwork also contribute to the overall electrochemical capacity during cycling? A brief\ndiscussion would strengthen the manuscript.\nResponse: Thank you very much for your insightful and thought-provoking comment.\nTo address this concern, it is well established in the literature that MoS , when used as\n2\nan anode material in Li-ion batteries, undergoes lithium intercalation over a wide\npotential window (3.0-0 V vs. Li/Li+), with a characteristic discharge plateau at\napproximately 1.2 V (Figure R1). This plateau corresponds to the insertion of Li+ into\nthe interlayer van der Waals gaps of MoS , following the reaction: MoS + xLi+ + xe-\n2 2\n→ Li MoS (0 ≤ x ≤ 1). The onset of MoS lithiation generally occurs below 1.5 V,\nx 2 2\nwhich is substantially lower than the typical cutoff discharge voltage of Li-S batteries\n(~1.7 V).[1-3] Therefore, within the working potential window of Li-S batteries, MoS\n2\ndoes not undergo significant lithiation, structural transformation, or decomposition.\nThis indicates that the MoS -based catalyst does not contribute additional capacity\n2\nthrough lithium storage processes in the Li-S batteries. Moreover, MoS -based catalyst\n2\nhas been extensively investigated as a functional material in Li-S batteries in previous\nstudies, further supporting its advantages and reliability in Li-S systems.[4-6]\nWe sincerely appreciate your valuable perspective. The relevant discussion has been\nadded to page 40 in the Supplementary Information section.\n“When MoS is used as an anode material in Li-ion batteries, the onset of MoS\n2 2\n3\nlithiation generally occurs below 1.5 V, which is substantially lower than the typical\ncutoff discharge voltage of Li-S batteries (~1.7 V).1-3 Therefore, within the working\npotential window of Li-S batteries, MoS does not undergo significant lithiation,\n2\nstructural transformation, or decomposition. This indicates that the MoS -based catalyst\n2\ndoes not contribute additional capacity through lithium storage processes in the Li-S\nbatteries.”\nFigure R1. a) Schematic illustration of the electrochemical pre-lithiation process of\nMoS .[1]\n[1] H. M. Wang, H. Yuan, W. W. Wang, X. Y. Wang, J. G. Sun, J. Yang, X. M. Liu, Q.\nZhao, T. Wang, N. Wen, Y. L. Gao, K. P. Song, D. R. Chen, S. J. Wang, Y. W. Zhang, J.\nH. Wang, Adv. Mater. 2024, 36, 2307741.\n[2] Y. K. Wang, Y. G. Zhao, K. L. Liu, S. B. Wang, N. Li, G. S. Shao, F. Wang, P. Zhang,\nCarbon Energy 2023, 5, e255.\n[3] X. L. Zhu, Z. P. Su, C. Wu, H. J. Cong, X. P. Ai, H. X. Yang, J. F. Qian, Nano Lett.\n2022, 22, 2956-2963.\n[4] H. B. Lin, L. Q. Yang, X. Jiang, G. C. Li, T. R. Zhang, Q. F. Yao, G. W. Zheng, J.\nY. Lee, Energy Environ. Sci. 2017, 10, 1476-1486.\n[5] Q. B. Jiang, H. F. Xu, K. S. Hui, Y. J. Wei, L. W. Liu, Z. Q. Ye, C. Y. Zha, M. T.\nZheng, J. Lu, K. N. Hui, Adv. Mater. 2025, 37, 2415986.\n[6] C. Y. Zhang, J. Yu, C. Huang, G. W. Sun, L. Balcells, J. Y. Li, X. D. Qi, C. Z. Yi, J.\nHerrero-Martín, L. Simonelli, F. Fauth, R. He, X. B. Pan, J. S. Li, J. Arbiol, J. Y. Zhou,\nComment #3: The manuscript does not present experimental evidence regarding\nchanges in the metallicity of MoS₂ after element doping. The inclusion of\ncorresponding measurements (e.g., electrical conductivity or spectroscopic analyses)\nwould enhance the discussion.\nResponse: Thank you very much for your insightful comment. Following your\nsuggestion, we have strengthened the discussion by providing both experimental and\ntheoretical evidence to demonstrate the enhanced metallicity of MoS after element\n2\ndoping.\nSpecifically, linear sweep voltammetry (LSV) measurements were performed to\nevaluate the charge-transport capability of MoS after doping. The pristine and doped\n2\nsamples were compressed into circular pellets (diameter 13 mm and thickness 1 mm)\n(Supplementary Fig. 27), and their voltage-current characteristics were recorded to\ncompare their conductivity (Supplementary Fig. 28). These results show that the Co,Ni-\nMoS (≈ 3.4·10-4 S·cm-1), Co,Mn-MoS (≈ 3.0·10-4 S·cm-1), and Co,V-MoS (≈ 2.5·10-\n2 2 2\n2\n1), clearly confirming the conductivity enhancement induced by doping.\nFurthermore, density functional theory (DFT) calculations were carried out to\nprovide mechanistic insight into the doping-induced changes in electronic structure. In\ncontrast, pristine MoS shows a distinct bandgap in its density of states (DOS),\n2\nconsistent with its semiconducting behavior. After Co-Ni, Co-Mn, or Co-V doping,\npronounced electronic states appear near the Fermi level in both the valence and\nconduction bands. This electronic redistribution effectively narrows the bandgap and\nfacilitates electron transport. These theoretical results indicate that atom doping\nenhances the metallicity and charge‐transport capability of MoS (Supplementary Fig.\n2\n26).\nIn summary, the experimental LSV measurements and DOS analysis consistently\ndemonstrate that element doping substantially enhances the electrical conductivity of\n2\nthe completeness and rigor of the manuscript.\nThis information has been added to page 29 in the Supplementary Information\nsection.\n“LSV tests were performed to evaluate the electrical conductivity of MoS after element\n2\ndoping. The pristine and doped samples were pressed into circular pellets, as shown in\nSupplementary Fig. 27. The corresponding voltage-current curves were recorded to\ncompare their electrical conductivity (Supplementary Fig. 28). These results show that\nthe Co,Ni-MoS , Co,Mn-MoS , and Co,V-MoS samples exhibit higher conductivity\n2 2 2\nthan pristine MoS , indicating that doping engineering effectively enhances the\n2\nmetallicity and electronic transport capability of the MoS catalyst.”\nmaterials.\nSupplementary Fig. 28 | LSV curve tests of MoS , Co,V-MoS , Co,Mn-MoS , and\n2 2 2\nCo,Ni-MoS circular pellets.\n2\n2 2 2\nComment #4: The role of spin polarization in the target catalyst during the conversion\nof sulfur species deserves further clarification. A more detailed mechanistic explanation\nwould help the reader better appreciate the influence of spin effects on the overall\nreaction process.\nResponse: Thank you very much for your valuable suggestion. We appreciate your\nemphasis on the mechanistic role of spin polarization, and we have expanded the\ndiscussion to clarify how spin effects govern sulfur conversion kinetics. In Li-S\nbatteries, sulfur species undergo complex multi-electron redox reactions. The\nadsorption, activation, and particularly the rate-determining Li S -Li S conversion\n2 2 2\ncritically govern the electrochemical kinetics. Spin polarization plays a key role in this\nprocess, primarily by modifying the spin-state transitions involved in the rate-\ndetermining step, while simultaneously reshaping the electronic structure and surface\nreactivity.\nSpin-state matching markedly accelerates the Li S to Li S rate-determining\n2 2 2\nstep. DFT results show that the Li S to Li S conversion intrinsically involves a change\n2 2 2\nin spin configuration: the initial and final states are non-spin-polarized, while the\nreaction intermediates are spin-polarized. This spin-state transition requires substantial\nenergy and leads to sluggish kinetics (Supplementary Figs. 3-6). To further clarify how\n7\ncatalyst spin density governs this transformation, we simulated the spin-state evolution\nof Li S and Li S when combined with spin-inactive MoS and spin-active Co,Ni-MoS .\n2 2 2 2 2\nWhen Li S or Li S is combined with pristine MoS , both the spin-up and spin-down\n2 2 2 2\nDOS remain symmetric (Figs. 3j, k), indicating no spin activation. Thus, the intrinsic\nspin-state mismatch in the Li S to Li S transition remains unresolved, limiting the\n2 2 2\nreaction kinetics. In contrast, when Li S and Li S are adsorbed on Co,Ni-MoS , clear\n2 2 2 2\nasymmetry appears in the spin-up/spin-down DOS for both the initial state and final\nstate (Figs. 3l, m). This indicates that Co,Ni-MoS induces a spin-polarized\n2\nenvironment that enables the Li S to Li S reaction to proceed through a spin-to-spin\n2 2 2\nmatched pathway. Such spin alignment facilitates quantum spin-exchange interactions\nand opens an efficient channel for spin-dependent electron transfer. As a result, the rate-\ndetermining step is significantly accelerated. Among all catalysts investigated, Co,Ni-\nMoS exhibits the lowest free-energy barrier for the Li S to Li S step (-1.7156 eV),\n2 2 2 2\nsignificantly lower than Co,Mn-MoS (-0.7917 eV), Co,V-MoS (-0.1952 eV), and\n2 2\npristine MoS (1.6413 eV) (Fig. 1h). This dramatic reduction directly demonstrates the\n2\nsuperior ability of Co,Ni-MoS to overcome the intrinsic kinetic bottleneck of the Li S\n2 2 2\nto Li S conversion.\nSpin polarization induces a redistribution of the DOS near the Fermi level\n(Supplementary Fig. 26), thereby modulating the electronic coupling between the\ncatalyst surface and adsorbed sulfur species (Figs. 4d-g). Catalysts with strong spin\npolarization possess unpaired electrons that can directly participate in charge-transfer\nprocesses. These unpaired electrons can facilitate the redox conversion of LiPSs,\neffectively enhancing catalytic activity.\nSpin polarization enhances orbital hybridization and optimizes adsorption\nenergies. Spin-polarized active sites exhibit stronger orbital hybridization with sulfur\nintermediates, which leads to adjusted adsorption energies and accelerates surface\nchemical transformations. Similar behavior has been reported previously (J. Am. Chem.\nSoc. 2023, 145, 22516-22526). Consistent with this mechanism, adsorption\n8\nconfigurations, adsorption energy results, and differential charge density analyses in\nthis work, demonstrate that spin-polarized catalysts (Co,Ni-MoS ) form significantly\n2\nstronger interactions with LiPSs compared to pristine MoS (Figs. 4a, d-g, and\nIn conclusion, these results provide compelling evidence that spin polarization is a\nkey determinant regulating lithium polysulfide conversion, particularly in accelerating\nthe rate-determining Li S to Li S step. By enabling spin-state matching and facilitating\n2 2 2\nspin-dependent electron transfer, spin-polarized catalysts significantly improve the\nsolid-solid conversion kinetics and ultimately enhance the electrochemical performance\nof Li-S batteries. We sincerely appreciate your insightful comments, which have helped\nus refine the mechanistic discussion and improve the clarity and depth of the manuscript.\nThis detailed explanation has been added to pages 23 and 27 in the revised manuscript.\n“Overall, the Li S to Li S conversion is a spin-involved process, and the catalyst’s\n2 2 2\nspin state fundamentally determines whether this step can proceed efficiently. MoS\n2\nwithout a spin moment forces Li S and Li S into symmetric spin states, thereby failing\n2 2 2\nto activate the required spin-polarized reaction pathway and resulting in sluggish solid-\nsolid conversion. In contrast, Co,Ni-MoS provides strong spin polarization and\n2\ninduces asymmetric spin states for both Li S and Li S, enabling a spin-to-spin\n2 2 2\ntransition pathway that accelerates electron transfer and directly lowers the free-energy\nbarrier.”\n“These results reveal that the high spin moment of Co,Ni-MoS plays an important\n2\nrole in suppressing the shuttle effect by simultaneously enhancing polysulfide\nadsorption and accelerating their redox conversion. The spin-polarized active sites\nstrengthen electronic coupling with soluble polysulfides, resulting in stronger charge\naccumulation at Li-S bonds and markedly improved adsorption capability compared\nwith spin-inactive MoS . This spin-induced electronic activation also lowers the energy\n2\nbarrier for Li S to Li S conversion among all catalysts. By enabling stronger spin-\n2 2 2\ndependent interactions and faster catalytic transformation, the spin-polarized Co,Ni-\nMoS effectively prevents LiPSs from diffusing into the electrolyte, thereby mitigating\n2\n9\nthe shuttle effect and promoting more stable long-term cycling.”\nSupplementary Fig. 26 | The calculated DOS near the Fermi level for a, MoS , b, Co,V-\n2 2 2\nFigs. 4d-g | Charge density differences ofd, Li S -MoS , e, Li S -Co,V-MoS , f, Li S -\n2 4 2 2 4 2 2 4\nCo,Mn-MoS , and g, Li S -Co,Ni-MoS . Blue and slight yellow regions indicate charge\n2 2 4 2\ndepletion and accumulation, respectively. Yellow, blue, slight blue, cyan, purple, green,\nand pink spheres denote S, Mo, Li, V, Mn, Ni, and Co atoms, respectively.\nFig. 4a | Visualized images of the pure Li S and Li S solutions after adding MoS ,\n2 6 2 6 2\nCo,V-MoS , Co, Mn-MoS and Co,Ni-MoS , and the UV-vis absorption spectra of the\n2 2, 2\nsolutions after adding these catalysts.\nof Li S on the a, MoS , b, Co,V-MoS , c, Co,Mn-MoS , and d, Co,Ni-MoS surfaces,\n2 4 2 2 2 2\nand the corresponding adsorption energies. Yellow, blue, slight blue, cyan, purple, green,\nand pink spheres denote S, Mo, Li, V, Mn, Ni, and Co atoms, respectively.\nSupplementary Fig. 3 | The DOS of each reaction intermediate of a, *Li S - *LiS -\n2 2\n*Li S, b, *Li S - *Li S - *LiS - *Li S, c, *Li S - *S - *Li S.\n2 2 2 3 2 2 2 2 2\nFig. 3j-m | DOSs of j, Li S -MoS , k, Li S-MoS , l, Li S -Co,Ni-MoS , and m, Li S-\n2 2 2 2 2 2 2 2 2\nscattering phenomenon; the observed signals correspond to inelastically scattered\nphotons rather than refracted light.\nResponse: We appreciate your careful examination of the schematic. Indeed, Raman\nspectroscopy is fundamentally a light-scattering process, and the emergent signal\ncorresponds to scattered photons rather than refracted light.\nIn response to your comment, we have thoroughly revised Supplementary Fig. 30 to\naccurately reflect the Raman scattering process and ensure that the figure description\naligns with the correct scientific principles. The revised figure now properly represents\nthe nature of the scattered light and its relation to the scattering phenomenon. Thank\nyou once again for your valuable comment, which has contributed to the improvement\nof the manuscript.\nSupplementary Fig. 30. The setup used for in-situ Raman spectroscopy analyses of the\ncell configuration.\nfor lithium-sulfur batteries, finds spin-state transitions cause sluggish kinetics in the\nLi2S2 to Li2S conversion, and shows Co,Ni-MoS2 catalyst improves battery\nperformance by regulating spin states. However, I do not recommend this manuscript\nfor publication due to the following concerns:\nResponse: We appreciate your thorough evaluation of our manuscript and your\nconstructive and professional comments. The concerns raised have been carefully\nconsidered. In response, we have carefully revised the manuscript and made extensive\ncorrections. Specifically, additional data has been incorporated, and the corresponding\ndiscussions have been significantly expanded to better support and clarify the\nconclusions. The detailed responses to each of your comments are provided below.\nComment #1: A 29-dimensional feature space is constructed in the paper to explore\nthe influence of the intrinsic properties of catalysts on the rate-determining step of the\nreaction. However, it is not clearly explained how these features are selected, whether\nthey have undergone strict redundancy testing, and how to ensure that these features\ncan comprehensively and accurately reflect the correlation between the catalyst and the\nreaction. This will affect the reliability of subsequent machine-learning models.\nResponse: We sincerely appreciate your valuable comments and constructive\nsuggestions regarding the feature-selection strategy and redundancy testing. In response,\nwe have revised the manuscript and provided detailed clarifications as follows:\nIn the revised manuscript, we have added a clear description of how the features were\nselected. Specifically, a total of 65 features were constructed to comprehensively\ncharacterize the catalysts from three perspectives:\n(i) electronic structure (e.g., d-band center, spin moment),\n(ii) elemental physicochemical and geometric properties (e.g., atomic radius,\ncoordination distances),\n(iii) adsorption and reaction-related parameters (e.g., adsorption energies of Li S and\n2 2\nand reaction kinetics.\nWe had performed a strict redundancy test. The Pearson correlation matrix of all\nfeatures is provided in the revised Supplementary Information (Supplementary Fig. 13).\nWe have eliminated some of the features with high correlation coefficients (|r| ≥ 0.85)\nto minimize multicollinearity and ensure the reliability of subsequent machine-learning\nmodels.\nTo ensure that the remaining features can comprehensively and accurately reflect the\ncorrelation between the catalysts and the reaction, we intentionally retained features\nthat capture different aspects of structural sensitivity.\nFor instance, M1S_Avg_Distance represents the overall bond strength and\ncoordination stability, whereas M1S_Max_Distance reflects dopant-induced strain or\nsurface relaxation-related local asymmetry. Based on the above feature-selection and\nredundancy-testing procedure, a final 29-dimensional feature space was deliberately\nconstructed. The trained model achieved a high coefficient of determination (R2=0.911)\nand a low mean squared error (MSE=0.078), indicating that although certain features\nstill exhibit statistical correlation, they provide complementary rather than redundant\ninformation. This complementary relationship enhances the model’s ability to capture\nthe multifaceted interactions between catalyst structure and reactivity.\nFurthermore, we constructed an additional LASSO regression model after removing\nall highly correlated features. As shown in Supplementary Fig. 15, the model exhibited\na notable performance decline (R2 = 0.79, MSE = 0.18), suggesting a loss of essential\ninformation and reduced stability. This further confirms that the retained features,\nalthough showing certain statistical correlations, are not redundant. Instead, they\nprovide mutually reinforcing contributions that are essential for reliable prediction and\nmechanistic interpretability.\nWe sincerely appreciate your insightful comments, which have helped us to improve\nthe clarity and depth of the manuscript. This detailed explanation has been added to\npage 11 in the revised manuscript.\n14\n“To investigate how intrinsic catalyst properties influence the rate-determining Li S\n2 2\nto Li S conversion, an initial feature space of 65 descriptors was constructed for MoS₂\n2\ndoped with two transition metals, covering electronic structure, elemental\nphysicochemical and geometric properties, as well as adsorption- and reaction-related\nparameters (Supplementary Fig. 13). After a redundancy-testing and feature-selection\nprocedure, a 29-dimensional feature space was retained for subsequent machine-\nlearning analysis to identify the key features that govern ensemble effects based on a\ndataset (Supplementary Table 1).”\nFurthermore, this detailed explanation has been added to pages 15 and 17 in the\nrevised Supplementary Information.\n“Specifically, a total of 65 features were constructed to comprehensively characterize\nthe catalysts from three perspectives: electronic structure (e.g., d-band center, spin\nmoment), elemental physicochemical and geometric properties (e.g., atomic radius,\ncoordination distances), and adsorption and reaction-related parameters (e.g.,\nadsorption energies of Li S and Li S ). These features collectively capture the intrinsic\n2 2 2\nattributes governing catalytic activity and reaction kinetics.\nA strict redundancy test was performed. The Pearson correlation matrix of all features\nis provided in the Supplementary Fig. 13. Some of the features with high correlation\ncoefficients (|r| ≥ 0.85) were eliminated to minimize multicollinearity and ensure the\nreliability of subsequent machine-learning models. To ensure that the remaining\nfeatures can comprehensively and accurately reflect the correlation between the\ncatalysts and the reaction, we intentionally retained features that capture different\naspects of structural sensitivity. For instance, M1S_Avg_Distance represents the overall\nbond strength and coordination stability, whereas M1S_Max_Distance reflects dopant-\ninduced strain or surface relaxation-related local asymmetry. Based on the above\nfeature-selection and redundancy-testing procedure, a final 29-dimensional feature\nspace was deliberately constructed. The trained model achieved a high coefficient of\ndetermination (R2=0.911) and a low mean squared error (MSE=0.078), indicating that\nalthough certain features still exhibit statistical correlation, they provide\n15\ncomplementary rather than redundant information. This complementary relationship\nenhances the model’s ability to capture the multifaceted interactions between catalyst\nstructure and reactivity.”\n“Furthermore, an additional LASSO regression model after removing all highly\ncorrelated features was constructed. As shown in Supplementary Fig. 15, the model\nexhibited a notable performance decline (R2 = 0.79, MSE = 0.18), suggesting a loss of\nessential information and reduced stability. This further confirms that the retained\nfeatures, although showing certain statistical correlations, are not redundant. Instead,\nthey provide mutually reinforcing contributions that are essential for reliable prediction\nand mechanistic interpretability.”\nSupplementary Fig. 13 | The Pearson correlation matrix of all features.\nremoving highly correlated features.\nComment #2: When analyzing the linear relationship between the spin moment and\nthe free energy change of the rate-determining step, it is only based on the catalyst\nsystem studied in the paper, without comparative verification with other similar\nbimetallic-doped MoS2 catalyst systems or catalyst systems with different matrices.\nIs such a linear relationship universal?\nResponse: Thank you very much for your insightful comment regarding the\nuniversality of the linear correlation between the spin moment of the catalyst and the\nfree energy change of the rate-determining step. Assessing the applicability of this\ncorrelation across other bimetallic MoS systems and catalysts with different matrices\n2\nis indeed important. Based on our survey of the literature, multiple independent studies\nhave reported highly consistent trends in diverse material systems, suggesting that spin\nmoment plays a common role in regulating SRR kinetics. These systems include metal\natoms on carbon materials, transition-metal chalcogen phosphates, oxides, and various\nheterostructure catalysts.\nFor example, Yan et al. reported that, in ferromagnetic-element-based single-atom\ncatalysts, spin density is key factors governing the kinetics of the Li S → Li S\n2 2 2\nreduction.[1] In particular, Fe-N sites with higher spin polarization exhibit the lowest\n4\nreaction barriers and the fastest Li S deposition kinetics. Similarly, Li et al.\n2\n17\ndemonstrated in bimetallic phosphorus trisulfide systems (FeCoPS /NCs) that a spin-\n3\nstate transition from low-spin to high-spin configurations induces orbital degeneracy\nand energy-level upshifts, which significantly enhance polysulfide adsorption,\naccelerate sulfur conversion, and ultimately improve electrochemical performance.[2]\nIn addition, Zhang et al. found that, under an external magnetic field, Co atoms in\nCoS systems undergo a transition from low-spin to high-spin states, generating more\nx\nunpaired electrons.[3] This spin delocalization effect enhances charge transfer and\norbital interactions between sulfur species and the catalyst, thereby lowering reaction\nbarriers and accelerating reaction kinetics. Wang et al. further reported that porous, low-\ncoordinated TiO nanosheets decorated with high-spin Co atoms (HS-Co/TiO )\n2-x 2-x\nexhibit excellent electrocatalytic activity.[4] The high-spin Co atoms contribute unpaired\nelectrons occupying different 3d orbitals, which facilitates electron transfer and orbital\nhybridization with polysulfides, effectively suppressing the shuttle effect and\nenhancing polysulfide conversion kinetics.\nMoreover, similar spin-related catalytic trends have also been observed in a variety\nof heterostructure materials.[5] As pointed out by your suggestion, this is indeed an\nimportant direction that merits further exploration. For clarity and comparison, we have\nsystematically summarized representative studies in Supplementary Table 9.\nAlthough numerous studies have addressed the sluggish kinetics of the SRR from\ndifferent perspectives, systematic investigations that clarify both why the rate-\ndetermining step is intrinsically sluggish and how the catalyst spin moment\nfundamentally regulates SRR kinetics remain limited. Addressing this gap is crucial for\nestablishing a solid physical foundation for spin-regulated sulfur redox catalysis.\nIn this study, our focus is to elucidate the mechanistic origin of the rate-determining\nstep associated with the Li S to Li S conversion in Li-S batteries. Theoretical\n2 2 2\ncalculation reveal that this rate-determining step intrinsically involves a spin-state\ntransition-from a non-spin-polarized Li S /Li S initial-final state pair to a spin-\n2 2 2\npolarized reaction intermediate. This spin-state mismatch requires the reaction to\novercome an additional spin-flip barrier, thereby leading to intrinsically sluggish\n18\nkinetics. This phenomenon has also been observed in other electrocatalytic reactions.\nFor example, in the electrochemical nitrate-to-ammonia reduction reaction, the spin-\nstate transition from paramagnetic NO to singlet NHO leads to intrinsically sluggish\nreaction kinetics. To deal with the intrinsically sluggish kinetics of rate-determining\nstep, recent studies have demonstrated that, compared with single-metal active centers,\nmagnetic coupling between two metal centers (e.g., Ni-O-Fe[6] or Ti-O-Fe[7]) can trigger\nquantum spin-exchange interactions. Such interactions establish efficient electron-\ntransport channels within the lattice and provide additional reaction pathways for spin-\ndependent electrocatalytic processes, thereby further accelerating reaction rates. This\nstrategy has been successfully validated in a variety of catalytic systems, where sluggish\nkinetics similarly originate from spin-state transitions of reaction intermediates.\nRational modulation of the catalyst magnetic moment can therefore effectively reduce\nspin-related reaction barriers, significantly improve reaction kinetics, and stabilize key\nintermediates. However, systematic investigations into the mechanistic role of spin-\nmoment regulation in Li-S battery chemistry remain largely unexplored.\nIn this work, MoS -one of the most widely studied layered transition-metal\n2\ndichalcogenides-was selected the model substrate. Guided by machine-learning\nanalysis, we systematically evaluated how intrinsic catalyst descriptors influence the\nGibbs free-energy change along the sulfur conversion pathway. Spin moment was\nidentified as the dominant descriptor affecting the rate-determining step barrier.\nTherefore, we designed a set of ten representative bimetallic systems composed of five\ntypical transition metals, and established a clear negative correlation between their spin\nmoments and the rate-determining step free-energy barriers. Catalysts with larger spin\nmoments consistently exhibited lower barriers and faster reaction kinetics.\nTo validate this finding experimentally while minimizing synthetic complexity and\nresource consumption, we synthesized several bimetallic MoS catalysts with distinct\n2\nspin-moment distributions and evaluated their electrochemical performance. The\nexperimental results were fully consistent with theory: catalysts with larger spin\nmoments exhibited faster redox kinetics and significantly reduced reaction barriers,\n19\nthereby delivering superior Li-S battery performance.\nTo further understand how the catalyst spin moment influences the Li S to Li S\n2 2 2\nconversion, we simulated the spin polarization states of Li S and Li S on MoS (spin-\n2 2 2 2\ninactive) and Co,Ni-MoS (spin-active). As shown in Figs. 3j-m, when Li S and Li S\n2 2 2 2\ninteract with pristine MoS , the spin-up and spin-down DOS remain symmetric,\n2\nindicating a non-spin-polarized state. Thus, the intrinsic spin-state transition of Li S to\n2 2\nLi S persists, leading to slow rate-determining step kinetics. In contrast, when Li S and\n2 2 2\nLi S are adsorbed on Co,Ni-MoS , both species exhibit clear spin asymmetry. This\n2 2\nsuggests that the rate-determining step transforms into a spin-to-spin conversion\nprocess, which enables quantum spin-exchange interactions and opens efficient\nchannels for spin-electron transfer, thereby accelerating the electrocatalytic conversion.\nThese results collectively demonstrate that the spin moment of the catalyst plays a\ncentral role in facilitating the rate-determining step in Li-S batteries, ultimately\nenhancing the electrochemical performance.\nEstablishing the universality of this linear relationship requires validation across a\nbroader range of catalyst systems. We are currently extending the study to additional\ncatalyst substrates and bimetallic configurations to further examine the generality of the\nspin moment-activity correlation. Within the scope of the present work, our results\ndemonstrate that, for bimetallic-doped MoS catalysts, the spin moment serves as a\n2\nphysically meaningful descriptor governing the rate-determining Li S and Li S\n2 2 2\nconversion. Your comments have been helpful in clarifying the scope of the present\nwork and in identifying meaningful directions for future study.\nSupplementary Table 9 | Comparison of transition metal-based catalysts with different\nspin states.\nSpin state catalyst Active site Spin state References\nFe-N\n4\n-C F e High [1]\nFeCoPS Fe, Co High [2]\nx\nHSCo/TiO Co High [4]\n2-x\nFe O /FeP@C Fe High [5]\n3 4\nv-CoSe Co High [9]\nNiS /NiSe @NC Ni High [10]\n2 2\nFig. 3j-m | DOSs of j, Li S -MoS , k, Li S-MoS , l, Li S -Co,Ni-MoS , and m, Li S-\n2 2 2 2 2 2 2 2 2\nCo,Ni-MoS .\n[1] R. Yan, Z. Y. Zhao, M. H. Cheng, Z. Yang, C. Cheng, X. K. Liu, B. Yin, S. Li,\nAngew. Chem., Int. Ed. 2023, 62, 2215414.\n[2] H. Li, M. Y. Chuai, X. Xiao, Y. Y. Jia, B. Chen, C. Li, Z. H. Piao, Z. J. Lao, M. T.\nZhang, R. H. Gao, B. K. Zhang, Z. Y. Han, J. L. Yang, G. M. Zhou, J. Am. Chem. Soc.\n2023, 145, 22516-22526.\n[3] C. Y. Zhang, C. Zhang, G. W. Sun, J. L. Pan, L. Gong, G. Z. Sun, J. J. Biendicho, L.\nBalcells, X. L. Fan, J. R. Morante, J. Y. Zhou, A. Cabot, Angew. Chem., Int. Ed. 2022,\n61, 2211570.\n[4] J. Y. Wang, X. M. Zhang, J. B. Liu, X. B. Wang, Y. H. Nie, K. Zong, X. Y. Zhang,\nC. J. Zhao, L. Yang, X. Wang, Z. W. Chen, Adv. Mater. 2025, 37, 2502075.\n[5] Z. Y. Wang, W. Z. Huang, H. Wu, Y. J. Wu, K. X. Shi, J. H. Li, W. G. Zhang, Q. B.\nLiu, Adv. Funct. Mater. 2024, 34, 2409303.\n[6] W. C. Wan, Y. G. Zhao, S. Q. Wei, C. A. Triana, J. G. Li, A. Arcifa, C. S. Allen, R.\n[7] J. Dai, Y. W. Tong, L. Zhao, Z. W. Hu, C. T. Chen, C. Y. Kuo, G. M. Zhan, J. X.\nWang, X. Y. Zou, Q. Zheng, W. Hou, R. Z. Wang, K. Y. Wang, R. Zhao, X. K. Gu, Y. C.\nYao, L. Z. Zhang, Nature Communications 2024, 15, 88.\n[8] J. Lu, Z. Wang, Y. Guo, Z. Jin, G. Cao, J. Qiu, F. Lian, A. Wang and W. Wang,\nEnergy Storage Mater. 2022, 47, 561-568.\n[9] J. Yu, C. Huang, O. Usoltsev, A. Black, K. Gupta, M. Spadaro, I. Pinto-Huguet, M.\nBotifoll, C. Li, J. Herrero-Martín, J. Zhou, A. Ponrouch, R. Zhao, L. Balcells, C. Zhang,\nA. Cabot and J. Arbiol, ACS Nano, 2024, 18, 19268-19282.\n[10] C. Huang, J. Yu, Chao Yue Zhang, Z. Cui, J. Chen, W. Lai, Y. Lei, B. Nan, X. Lu,\nR. He, L. Gong, J. Li, C. Li, X. Qi, Q. Xue, J. Zhou, X. Qi, L. Balcells, J. Arbiol and A.\nCabot, Adv. Mater. 2024, 36, 2400810.\nComment #3: The paper mentions that atomic pair doping can effectively induce spin\npolarization in catalysts. However, there is a lack of detailed comparative analysis on\nthe differences in the mechanisms by which different atomic pairs (such as Co-Ni,\nCo-Mn, and Co-V) induce spin polarization and the specific impacts of these differences\non catalytic performance, which is not conducive to a deep understanding of the role of\ndoped atomic pairs.\nResponse: We sincerely thank you for your insightful comment. A comparative\nunderstanding of how different atomic pairs induce spin polarization is indeed crucial\nfor elucidating the structure-spin-activity relationship in doped catalysts. In response,\nwe have added a detailed comparative analysis in the revised manuscript to explicitly\ndistinguish the spin-polarization mechanisms and their catalytic consequences for the\nCo-Ni, Co-Mn, and Co-V atomic pairs.\nIn 2H-MoS , the Mo atom is coordinated in a trigonal-prismatic environment with\n3 3\norbitals of Mo split into three symmetry-adapted groups: a’1 (d 2), e’ (d 2 2, d ), and\nz x -y xy\ne’’ (d , d ). When different bimetallic atomic pairs are introduced into 2H-MoS , the\nxz yz 2\n22\ndoping does not fundamentally alter the host coordination environment. Instead,\nthrough local electronic coupling and orbital hybridization, it induces different degrees\nof spin splitting and asymmetric electronic occupation near the Fermi level. These\ndifferences can be reasonably attributed to variations in the dopant 3d electronic\nconfigurations and local exchange interactions, as well as to differences in the energetic\nalignment and hybridization strength between the dopant 3d states and the host Mo 4d\n/ S 3p states near Fermi level. The combined effects of these factors lead to pronounced\ndifferences in the spin-resolved density of states and its asymmetry near Fermi level for\nthe Co-Ni, Co-Mn, and Co-V systems.\nBecause spin polarization fundamentally arises from spin-dependent splitting and\nasymmetric occupation of electronic states, orbital-resolved PDOS analysis offers\ndirect insight into the relative contributions of specific d orbitals to spin-polarization\nbehavior. Accordingly, we performed a systematic orbital-resolved PDOS analysis to\nelucidate the differences in spin polarization induced by the various atomic pairs\n(Supplementary Figs. 30-32).\nWithin this mechanistic framework, the superior spin polarization and catalytic\nperformance of the Co-Ni atomic pair can be attributed to its ability to generate more\npronounced and more effective spin-polarized states near Fermi level (Supplementary\nFig. 30). On the one hand, Co-Ni doping introduces dopant-derived 3d states that\nexhibit stronger spin splitting and larger spin-resolved weight in the vicinity of the\nFermi level (Supplementary Figs. 30b and c). On the other hand, these spin-polarized\nstates show more pronounced hybridization with neighboring Mo 4d and S 3p orbitals,\nas evidenced by the enhanced spin-resolved Mo/S contributions near Fermi level and\nthe more delocalized spin-density distribution (Supplementary Figs. 30d and e). This\nindicates stronger dopant-lattice electronic coupling, which facilitates the propagation\nof spin polarization into the Mo-S framework and enables the formation of delocalized\nspin channels that are accessible for electrochemical reactions. In the Co-Mn system,\nalthough spin-polarized dopant states are present, their spin-resolved weight and\ncoupling to nearby Mo-S orbitals near Fermi level are weaker, resulting in an\n23\nintermediate degree of spin polarization (Supplementary Fig. 31). In contrast, Co,V-\nMoS exhibits only limited spin-polarized orbital contributions near the Fermi level and\n2\nminimal involvement of neighboring Mo/S orbitals, corresponding to the weakest\noverall spin polarization (Supplementary Fig. 32).\nThese electronic-structure distinctions provide a clear mechanistic basis for the\nobserved catalytic trends: stronger and more effectively delocalized spin polarization\nnear Fermi level (Co-Ni > Co-Mn > Co-V) correlates with a lower free-energy barrier\nfor the rate-determining Li S → Li S conversion and faster sulfur redox kinetics.\n2 2 2\nThis trend is fully consistent with both the calculated reaction energetics and the\nexperimental kinetic measurements.\nThese differences in electronic structure and magnetic interactions are directly\nreflected in the observed catalytic activity trends. As shown in the revised manuscript,\nthe induced spin polarization follows the order Co-Ni > Co-Mn > Co-V (Supplementary\nFig. 29), and the corresponding rate-determining-step energy barriers for the Li S to\n2 2\nLi S conversion follow the same sequence. This clear correlation demonstrates that the\n2\nextent of spin polarization critically governs the catalytic behavior, thereby validating\nthe mechanistic relevance of atomic-pair selection in designing efficient sulfur\nconversion catalysts.\nFurthermore, we have conducted comprehensive experimental studies to validate the\nmechanistic trend. As shown in Figs. 3o, p, M-T measurements were performed to\nprobe the magnetic moments of the catalysts. The experimentally measured magnetic\nmoment values exhibit the same trend as predicted by our DFT calculations, with Co-\nNi showing the highest magnetic moment among the three atomic pairs, followed by\nCo-Mn and Co-V. In addition to magnetic characterization, we systematically evaluated\nthe catalytic behavior through a series of electrochemical kinetics analyses, including\nLi S nucleation and dissolution tests, activation-energy analyses derived from\n2\ntemperature-dependent impedance measurements, and various in situ characterization\ntechniques. All these results consistently demonstrate that catalysts with stronger\ninduced spin polarization, particularly Co,Ni-MoS , exhibit faster redox kinetics during\n2\n24\nsulfur conversion and thereby deliver markedly improved electrochemical performance\nin Li-S batteries. These comprehensive experimental and theoretical results jointly\nsupport the validity of the proposed spin-polarization driven catalytic mechanism. To\nbetter highlight this, we have included the following explanatory content on page 31 in\nthe revised Supplementary Information.\n“In 2H-MoS , Mo atoms are coordinated in a trigonal-prismatic geometry with D h\n2 3\nsymmetry rather than an octahedral crystal field. Under D h symmetry, the five Mo d\n3\norbitals split into three symmetry-adapted manifolds: a’1 (d 2), e’ (d 2 2, d ), and e’’\nz x -y xy\n(d , d ). When different bimetallic atomic pairs are introduced into 2H-MoS , the\nxz yz 2\ndoping does not fundamentally alter the host coordination environment. Instead,\nthrough local electronic coupling and orbital hybridization, it induces different degrees\nof spin splitting and asymmetric electronic occupation near the Fermi level. These\ndifferences can be reasonably attributed to variations in the dopant 3d electronic\nconfigurations and local exchange interactions, as well as to differences in the energetic\nalignment and hybridization strength between the dopant 3d states and the host Mo 4d\n/ S 3p states near Fermi level. The combined effects of these factors lead to pronounced\ndifferences in the spin-resolved density of states and its asymmetry near Fermi level for\nthe Co-Ni, Co-Mn, and Co-V systems.\nBecause spin polarization fundamentally arises from spin-dependent splitting and\nasymmetric occupation of electronic states, orbital-resolved PDOS analysis offers\ndirect insight into the relative contributions of specific d orbitals to spin-polarization\nbehavior. Accordingly, we performed a systematic orbital-resolved PDOS analysis to\nelucidate the differences in spin polarization induced by the various atomic pairs\n(Supplementary Figs. 30-32).\nWithin this mechanistic framework, the superior spin polarization and catalytic\nperformance of the Co-Ni atomic pair can be attributed to its ability to generate more\npronounced and more effective spin-polarized states near Fermi level (Supplementary\nFig. 30). On the one hand, Co-Ni doping introduces dopant-derived 3d states that\nexhibit stronger spin splitting and larger spin-resolved weight in the vicinity of the\nshow more pronounced hybridization with neighboring Mo 4d and S 3p orbitals,\nas evidenced by the enhanced spin-resolved Mo/S contributions near Fermi level and the\nmore delocalized spin-density distribution (Supplementary Figs. 30 d, e). This indicates\nstronger dopant-lattice electronic coupling, which facilitates the propagation of spin\npolarization into the Mo-S framework and enables the formation of delocalized spin\nchannels that are accessible for electrochemical reactions. In the Co-Mn system,\nalthough spin-polarized dopant states are present, their spin-resolved weight and\ncoupling to nearby Mo-S orbitals near Fermi level are weaker, resulting in an\nintermediate degree of spin polarization (Supplementary Fig. 31). In contrast, Co,V-\nMoS exhibits only limited spin-polarized orbital contributions near the Fermi level and\n2\nminimal involvement of neighboring Mo/S orbitals, corresponding to the weakest\noverall spin polarization (Supplementary Fig. 32).”\nFurthermore, we have included the following explanatory content on page 19 in the\nrevised manuscript.\n“Because spin polarization fundamentally arises from spin-dependent splitting and\nasymmetric occupation of electronic states, orbital-resolved PDOS analysis offers\ndirect insight into the relative contributions of specific d orbitals to spin-polarization\nbehavior. Co-Ni doping introduces dopant-derived 3d states with pronounced spin\nsplitting and substantial spin-resolved weight, which hybridize more effectively with\nneighboring Mo 4d and S 3p orbitals, as evidenced by the enhanced spin-resolved Mo/S\ncontributions and the more delocalized spin-density distribution near Fermi level\n(Supplementary Fig. 30). This strong dopant-lattice electronic coupling facilitates the\npropagation of spin polarization into the Mo-S framework, enabling the formation of\ndelocalized spin channels that are accessible for electrochemical reactions. By\ncomparison, the Co-Mn system exhibits weaker spin-resolved weight and reduced\ncoupling to Mo-S orbitals, leading to intermediate spin polarization, whereas Co,V-\nMoS shows only limited spin-polarized states near Fermi level and minimal Mo/S\n2\nparticipation, corresponding to the weakest overall spin polarization (Supplementary\nSupplementary Fig. 26 | The calculated DOS near the Fermi level for a, MoS , b, Co,V-\n2 2 2\nSupplementary Fig. 30 | a, The calculated PDOS near the Fermi level for Co,Ni-MoS .\n2\nb, Total density of states and projected density of states of individual, c, Co and d,\nMo\nNi sites with e, nearby S, and f, Mo atoms in Co,Ni-MoS .\nMo 2\n2\nb, Total density of states and projected density of states of individual, c, Co and d,\nMo\nMn sites with e, nearby S, and f, Mo atoms in Co,Mn-MoS .\nMo 2\nSupplementary Fig. 32 | a, The calculated PDOS near the Fermi level for Co,V-MoS .\n2\nb, Total density of states and projected density of states of individual, c, Co and d,\nMo\nV sites with e, nearby S, and f, Mo atoms in Co,V-MoS .\nMo 2\n2 2\nCo,Mn-MoS , and d, Co,Ni-MoS . Yellow, blue, red, cyan, purple, and green spheres\n2 2\ndenote S, Mo, Co, V, Mn, and Ni atoms, respectively. The light purple isosurfaces show\nthe spin-state density.\nFig. 3 o, p | 1/χ versus temperature plots and the calculated μ of MoS , Co,V-MoS ,\nm eff 2 2\nCo-Mn-MoS and Co-Ni-MoS .\n2 2\nComment #4: The paper shows through various tests and simulations that the Co,Ni-\nMoS2 catalyst has excellent performance. However, there is a lack of research on the\ncatalytic performance of this catalyst under high-rate charge-discharge conditions (such\nas 5C, 10C, etc.) and the influence of spin moment on reaction kinetics at high rates.\nHigh-rate performance is crucial in practical applications. How do the authors plan to\nsupplement this?\nResponse: Thank you for your comment. High-rate performance is indeed a critical\nindicator for evaluating the practical applicability of Li-S batteries and is essential for\n29\nverifying the effectiveness of catalysts under fast reaction kinetics. In response to your\nsuggestion, we have now supplemented high-rate cycling tests to directly assess the\ncatalytic performance of Co,Ni-MoS under rapid charge-discharge conditions.\nSupplementary Fig. 40 and 41, the cell incorporating Co,Ni-MoS exhibits significantly\n2\nsuperior electrochemical performance compared with the cell using pristine MoS ,\n2\nincluding higher reversible capacity and more stable long-term cycling. These\nexperimental results are fully consistent with the spin polarization mechanism proposed\nin this work: the Co-Ni atomic pair induces the strongest spin polarization and yields\nthe lowest Li S to Li S reaction barrier, enabling rapid solid-solid conversion even at\n2 2 2\nhigh rates.\nBeyond coin-cell-level evaluations, the practical relevance of Li-S catalysts is more\nrigorously assessed through Ah-scale pouch cells operated under realistic conditions,\nwhich constitute a more representative for practical applications. Accordingly, the\nCo,Ni-MoS catalyst was further evaluated in Ah-level pouch cells. As shown in Fig.\n2\n5h-k and Supplementary Figs. 50-52, the pouch cell operates with a high sulfur loading\nof 10.2 g and a lean electrolyte condition (E/S = 3.2 ul mg-1), delivering an ultra-high\ncapacity of 13.2 Ah and a gravimetric energy density of 435 Wh kg-1 at a current density\nof approximately 1000 mA, while maintaining stable cycling performance. A\nsystematic comparison with previously reported Li-S pouch cells further demonstrates\nthat this system simultaneously achieves Ah-level capacity, high energy density, and\nrobust cycling stability, indicating its strong potential for practical energy-storage\napplications.\nThus, the validated high-rate electrochemical performance and the Ah-scale pouch\ncell results provide a consistent and comprehensive conclusion: spin-moment\nengineering effectively accelerates the rate-determining sulfur conversion step, thereby\nenabling fast reaction kinetics at high rates and reliable operation under practically\nrelevant conditions.\nWe sincerely appreciate your valuable suggestion, which has substantially improved\n30\nthe completeness of this work by strengthening both the high-rate electrochemical\nevaluation and the assessment of practical applicability. To better highlight these\nimprovements, we have included the following explanatory content on page 30 in the\nrevised manuscript:\n“The electrochemical performance at a higher current density of 5 C was further\nevaluated, as shown in Supplementary Figs. 40 and 41. The MoS -based cell is unable\n2\nto sustain effective charge-discharge cycling, whereas the Co,Ni-MoS -based cell\n2\nretains a higher reversible capacity with markedly cycling stability, retaining 503 mAh\ng-1 after 400 cycles. These results indicate that Co,Ni doping effectively enhances sulfur\nredox kinetics, enabling reliable operation under high-rate conditions.”\nSupplementary Fig. 40 | High current density performances of Co,Ni-MoS -based cells\n2\nat 5.0 C for 400 cycles.\nSupplementary Fig. 41 | High current density performances of MoS -based cells at 5.0\nComment #5: The paper mentions that the Co,Ni-MoS₂ catalyst can improve battery\nperformance. However, the influence of this catalyst on the electrochemical\n31\nperformance of batteries under different temperature environments is not covered in the\npaper, and temperature is a non-negligible variable in actual use.\nResponse: We sincerely appreciate your constructive comment. Temperature is indeed\na critical parameter influencing the practical operation of Li-S batteries, as it affects\nionic conductivity, polysulfide solubility, and especially the kinetics of the solid-solid\nLi S to Li S conversion. We fully agree that evaluating the catalytic behavior of the\n2 2 2\nCo,Ni-MoS catalyst under different temperature conditions is important for assessing\n2\nits practical applicability.\nFollowing your suggestion, we have supplemented the manuscript with additional\nelectrochemical measurements conducted under different temperature conditions. As\nshown in Supplementary Fig. 42, the cell incorporating the Co,Ni-MoS catalyst retains\n2\na higher and more stable reversible capacity under low-temperature environments (0 oC\n→ -10 oC → -20 oC → -10 oC → 0 oC) compared with pristine MoS . These results\n2\nindicate that the Co,Ni-MoS catalyst effectively promotes the redox kinetics of sulfur\n2\nspecies even under severe thermal constraints.\nThese observations further corroborate our mechanistic conclusion that the Co-Ni\ndual-metal synergy induces the strongest spin polarization and provides the lowest\nLi S to Li S conversion barrier among all tested catalysts. As a result, the catalyst\n2 2 2\nmaintains fast solid-solid conversion kinetics even when ionic transport is significantly\nhindered by low temperature.\nWe sincerely appreciate your insightful suggestion, which has allowed us to further\nstrengthen the completeness and practical relevance of our work. To better highlight\nthis, we have included the following explanatory content on page 30 in the revised\nmanuscript:\n“In addition, the cycling performance at low temperatures was further evaluated. As\nshown in Supplementary Fig. 42, the Co,Ni-MoS -based cell maintains a higher\n2\nreversible capacity and improved cycling stability over a wide temperature range from\n0 °C down to -20 °C, whereas the pristine MoS cell suffers from severe performance\n2\ndegradation.”\n2\n10 oC → 0 oC).\nThis work is highly innovative and profound. It is the first time that the spin-state\nengineering strategy has been systematically applied to the solid-solid conversion\nreaction of lithium-sulfur batteries, breaking through the traditional understanding of\nthe rate bottleneck in this field. By integrating theoretical calculations, machine\nlearning, and experimental validation, the authors successfully revealed the negative\ncorrelation between the spin moment and the reaction barrier, and designed a Co,Ni-\nMoS2 catalyst with high performance. The developed pouch battery features both high\ncapacity and high energy density, fully demonstrating its significant guiding\nsignificance for the development of the next generation of high-energy batteries.\nHowever, several specific issues must be further modified, and a reconsideration is\nsuggested for publication in Nature communications after a minor revision.\nSpecific issues are as follows:\nResponse: We sincerely appreciate your positive evaluation and recognition of the\nnovelty and significance of this work. We are also grateful for the constructive\ncomments and valuable suggestions, which have helped us further improve the\ncompleteness and pertinency of the manuscript. According to your comments, some\nnecessary data and corresponding discussion have been added to the revised manuscript.\nThe responses to your specific comments are listed below.\nComment #1: In fact, the 1T phase of MoS2 has a magnetic moment, while the 2H\nphase has no single electron. This article should indicate which item the calculated and\nprepared MoS2 belongs to, as well as whether the phase changes after diatomic\ndoping.\nResponse: Thank you for your suggestions. Indeed, the magnetic characteristics of\nMoS are strongly correlated with its crystal phase: metallic 1T-MoS exhibit magnetic\n2 2\nmoments, whereas intrinsically 2H-MoS is non-magnetic stable configuration. This\n2\ndistinction is fundamental for clarifying the origin of spin polarization in our work, and\nwe appreciate you for highlighting this point. Although the metallic 1T phase may\nexhibit spin-related properties, it is thermodynamically metastable and is generally\n34\nobtained through external treatments such as chemical activation or intercalation. This\nintrinsic instability poses significant challenges for the precise and controllable\nregulation of spin states in practical applications. Accordingly, the present study does\nnot rely on structural phase transitions to introduce magnetism; instead, it focuses on\nachieving controllable spin polarization within the thermodynamically stable 2H lattice\nvia electronic structure reconstruction.\nTo address this concern experimentally, we carefully examined whether dual-metal\ndoping could induce a 2H to 1T phase transition in our synthesized catalysts. As shown\nin Fig. 2i, the XRD pattern of pristine MoS displays distinct characteristic reflections\n2\nthat match well with the standard 2H-MoS reference (JCPDS 37-1492), including\n2\n(002), (100), (103), and (110) planes. After Co-Ni incorporation, the doped MoS\n2\nsamples exhibit XRD patterns that remain fully consistent with the same JCPDS card,\nwith no characteristic signatures of the metallic 1T phase. These results clearly confirm\nthat the 2H crystal phase is preserved after doping.\nFurthermore, Raman spectroscopy further verifies the absence of a 2H to 1T phase\ntransformation. Both pristine MoS and dual-metal incorporation MoS display only the\n2 2\nsignature E g1 and A g modes of the 2H phase, while no J /J /J vibrational fingerprints\n2 1 1 2 3\ncorresponding to 1T phases are observed (Supplementary Fig. 20). The same Raman\nprofiles before and after doping demonstrate that dual-metal incorporation does not\ninduce a structural phase transition, but instead preserves the 2H lattice.\nTherefore, the spin polarization observed in this work originates from localized\nelectronic reconstruction induced by Co-Ni dual-atom dopants, rather than a structural\nphase transformation into the 1T phase. This demonstrates that spin modulation can be\nachieved directly within the stable 2H lattice through targeted atomic engineering,\noffering a more controllable and reliable strategy.\nTo maintain consistency with the experimental results, all DFT simulations in this\nstudy were performed using the thermodynamically stable 2H-MoS structure, and no\n2\n1T-phase model was considered. The calculations were designed to examine whether\nspin polarization can be induced within the conventional 2H lattice solely through\n35\nelectronic reconstruction, without invoking any structural phase transition. This\nclarification has now been explicitly added to the revised manuscript.\nWe sincerely thank you for this thoughtful suggestion, which has helped us\nstrengthen the rigor, clarity, and scientific interpretation of the work. To better highlight\nthis, we have included the following explanatory content on page 16 in the revised\nmanuscript:\n“The spectra of the crystal structure of all the products were obtained by powder X-\nray diffraction (XRD) and show a good match to the standard 2H MoS phase (JCPDS\n2\ncard no. 37-1492), with peaks at 14.0o, 34.6o, 39.5o, and 57.3o of the (002), (100), (103),\nand (110) planes, respectively48 (Fig. 2i). Rietveld refinement analyses confirmed that\nthe doped samples remain a single phase with an excellent fit to MoS (Supplementary\nsuccessfully incorporated into the Mo lattice sites without generating secondary phases.\nRaman spectroscopy further verifies the absence of a 2H to 1T phase transformation.\nBoth pristine MoS and dual-metal incorporation MoS display only the signature E g1\n2 2 2\nand A g modes of the 2H phase, while no J /J /J vibrational fingerprints corresponding\n1 1 2 3\nto 1T phases are observed (Supplementary Fig. 20).”\nSupplementary Fig. 20 | Raman spectra of MoS , Co,V-MoS , Co,Mn-MoS , and\n2 2 2\nCo,Ni-MoS catalysts.\ndopants and identified Co,Ni-MoS2 as the catalyst with the best performance. However,\n36\ngiven that Fe is a common and economically efficient transition metal, the lack of\nCo,Fe-MoS2 samples in the experimental comparison deserves attention. Can the\nauthors explain why this specific combination was not synthesized and tested?\nResponse: We are grateful for your insightful and constructive comment. We fully\nagree that Fe is an abundant and cost-effective transition metal, and the absence of\nCo,Fe-MoS in the main experimental comparison requires further clarification.\nseries of bimetallic MoS catalysts. The theoretical results reveal a clear negative\n2\ncorrelation between the spin magnetic moment of the catalyst and the Gibbs free-energy\nbarrier of the Li S to Li S rate-determining step. To validate this trend experimentally\n2 2 2\nin an efficient yet scientifically rigorous manner, we selected representative catalysts\nexhibiting high, medium, and low spin polarization levels (e.g., Co,Ni-MoS , Co,Mn-\n2 2\nstrategy enables direct comparison across distinct spin states while reducing\nunnecessary material consumption and ensuring mechanistic clarity.\nTo further address your concern, we have additionally synthesized Co,Fe-MoS and\n2\ncarried out structural and electrochemical characterizations. As shown in the Figure R2,\nthe XRD pattern of Co,Fe-MoS matches well with the standard 2H-MoS phase\n2 2\n(JCPDS 37-1492), without peak broadening or low-angle expansion that would indicate\nthe presence of 1T or intercalated phases. Raman spectra similarly exhibit only the E 1\n2g\nand A modes characteristic of 2H-MoS , and no J /J /J vibrational signatures\n1g 2 1 2 3\nassociated with 1T structures are observed. These results confirm that Co and Fe doping\ndoes not induce a 2H → 1T transition. XPS measurements further verify the successful\nincorporation of both Co and Fe into the MoS lattice with stable chemical\n2\nenvironments.\nElectrochemical measurements show that Co,Fe-MoS delivers a reversible capacity\n2\nof 867 mAh g-1 after 100 cycles, which is higher than that of Co,V-MoS (842 mAh g-\n2\n1) but lower than that of Co,Mn-MoS (914 mAh g-1) (Figure R3). This performance\n2\ntrend is consistent with DFT predictions, which place Co,Fe-MoS at an intermediate\n2\n37\nlevel in its ability to lower the Gibbs free-energy barrier for the Li S → Li S\n2 2 2\nconversion. This alignment between theoretical prediction and experimental\nobservation further supports the proposed correlation between spin polarization and\ncatalytic activity.\nFigure R2. a, The XRD pattern of the Co,Fe-MoS catalyst. The XPS spectra analysis\n2\nof the Co,Fe-MoS catalyst, b, Mo 3d, c, S 2p, d, Co 2p, and e, Fe 2p XPS spectra. f,\nthis work? The introduction does not explain clearly. It is necessary to clarify the\nadvantages of bimetallic atoms in regulating spin states compared to single-atom,\nheterojunction, etc.\nResponse: Thank you for your constructive comment. In this work, the primary\nmotivation for using bimetallic doping is to deliberately modulate spin polarization\n38\nwithin the stable 2H-MoS lattice through electronic reconstruction.\nexchange interactions between two adjacent transition-metal sites, which provides a\ncooperative mechanism to generate stronger and more extended spin polarization. A\nsingle dopant atom can only contribute localized unpaired electrons, resulting in limited\nspin splitting and confined electronic disturbance. In contrast, a pair of electronically\ncomplementary dopants (e.g., Co-Ni) can form double-exchange or ferromagnetic\ncoupling pathways that delocalize spin density across the Mo-S lattice. Such lattice-\nlevel spin polarization effectively reduces the activation barrier of the Li S to Li S step\n2 2 2\ncompared with isolated single-atom centers.\nIn comparison to heterostructure-based strategies, which regulate electronic states\nthrough interfacial charge redistribution, bimetallic doping modifies the electronic\nstructure within the bulk lattice rather than at a limited interface. As a result, the induced\nspin polarization is not constrained to an interfacial region but can extend through the\ncatalyst lattice, enabling stronger coupling with LiPSs intermediates. Furthermore, the\nmagnitude and sign of the spin interactions can be rationally tuned via the choice of\nmetal pairs, providing a more designable and scalable pathway for spin modulation than\ninterface-dependent heterojunctions.\nTherefore, dual-metal doping provides a unique balance. It preserves the structural\nstability of the 2H lattice, while enabling stronger lattice-wide spin interactions than\nsingle-atom doping or heterostructure engineering. More importantly, the magnetic\ncoupling of two metal species, such as Ni-O-Fe or Ti-O-Fe, has recently been suggested\nand demonstrated to trigger quantum spin exchange interactions, thereby establishing\nelectronic transfer channels in the lattice and providing additional spin electron transfer\npathways for spin-dependent electrocatalytic reactions. This strategy has been\nsuccessfully applied in other electrocatalytic fields, not only helping to increase\nreaction rates but also stabilizing key intermediates in the reaction process, significantly\nimproving the electrocatalytic performance.\nWe sincerely thank you again for the insightful suggestion, which helped us greatly\n39\nimprove the clarity and foundation of our study. We have clarified this mechanistic\nrationale in the revised manuscript (Introduction, page 6).\n“Proper manipulation of the spin states of metal active sites in electrocatalysts is an\neffective strategy to accelerate the spin-state transition between intermediate products,\nbecause spin-polarized metal active sites can provide spin electrons to increase the\nconversion efficiency of Li S to Li S, as proposed by Li et al39,40. More importantly,\n2 2 2\nthe magnetic coupling of two metal species, such as Ni-O-Fe41 or Ti-O-Fe42, has\nrecently been suggested and demonstrated to trigger quantum spin exchange\ninteractions, thereby establishing electronic transfer channels in the lattice and\nproviding additional spin electron transfer pathways for spin-dependent electrocatalytic\nreactions36,43-45. Dual-metal doping preserves the structural stability of the pristine\nlattice, while enabling stronger lattice-wide spin interactions than single-atom doping\nor heterostructure engineering. This strategy has been successfully applied in other\nelectrocatalytic fields, not only helping to increase reaction rates but also stabilizing\nkey intermediates in the reaction process, significantly improving the electrocatalytic\nperformance36,42,46. Unfortunately, previous studies have not yet provided a\ncomprehensive and in-depth understanding of the fundamental mechanisms behind the\ncatalysis of Li S to Li S reduction. The interaction between the properties of the metal\n2 2 2\nsites of a catalyst and the conversion efficiency of Li S to Li S also remains\n2 2 2\ninadequately understood, and this could provide a standard descriptor for the\ndevelopment of transition metal-based catalysts. Theoretical calculations combined\nwith machine learning (ML) enable systematic feature analysis to uncover the\ncorrelation between spin states and solid-solid conversion, which can provide a\npowerful platform for the rational design of catalysts for high-performance Li-S\nbatteries (Fig. 1a)”\nComment #4: In the article, STEM and XAFS have been utilized to demonstrate the\nsuccessful doping of Co and Ni before cycling, but the structural characterization of the\ncorresponding catalysts after cycling is lacking. Is the catalyst poisoned? Will Co and\nmagnetic moment of Co/Ni have changed?\nResponse: We sincerely appreciate your constructive comment. Evaluating the\nstructural and electronic stability of the catalyst after cycling is essential to determine\nwhether Co/Ni remain active and whether poisoning, dissolution, or electronic\nreconstruction has occurred. Following your suggestion, we performed post-cycling\ncharacterizations on the Co,Ni-MoS catalyst to examine the structural integrity,\n2\nchemical environment, and magnetic behavior.\nXRD analysis of the electrode after discharge/charge cycles reveals that the 2H-MoS\n2\ndiffraction peaks remain unchanged, without evidence of peak disappearance and\nbroadening (Supplementary Fig. 43). This indicates that no structural decomposition or\nphase disruption occurs during cycling. In addition, TEM characterization\n(Supplementary Fig. 44) shows nearly identical lattice fringes and selected-area\ndiffraction patterns before and after cycling, demonstrating that the crystal structure is\nwell preserved and that neither amorphization nor nanoparticle aggregation occurs.\nTo further evaluate the chemical environment of the doped metal sites, XPS spectra\nof Co 2p and Ni 2p (Supplementary Fig. 45) were collected after cycling. The binding\nenergies and peak shapes exhibit no noticeable shifts or additional features, confirming\nthat the oxidation state and coordination environment of Co/Ni remain nearly\nunchanged. This result strongly suggests that Co and Ni do not dissolve from the MoS\n2\nlattice and do not undergo catalyst poisoning via chemical state alteration.\nRegarding the preservation of spin-related activity, we measured the magnetic\nresponse (Supplementary Fig. 46). The magnetic susceptibility profiles before and after\ncycling are nearly identical, indicating that the spin-active Co/Ni sites retain their\nlocalized magnetic moments. This result supports that the spin polarization responsible\nfor accelerating the Li S to Li S conversion remains active after extended cycling.\n2 2 2\nFinally, to verify the practical catalytic stability, the cycled Co,Ni-MoS was\n2\nreassembled into fresh Li-S cells. The restored cells display clear and well-defined\ncathodic/anodic peaks in CV curves (Supplementary Fig. 47). These results verify that\n41\nthe catalyst maintains efficient kinetic regulation without poisoning or deactivation\nafter long-term cycling.\nTaken together, XRD, TEM, XPS, M-T measurements, and electrochemical re-\nevaluation jointly confirm that the Co and Ni atoms remain embedded in the MoS\n2\nlattice, preserve their chemical and spin states, and sustain strong catalytic activity\nwithout dissolution or poisoning during battery operation. These findings demonstrate\nthe stability of the spin-regulated catalytic sites and verify the durability of the proposed\ncatalyst.\nThank you once again for your comment. According to your suggestion, we have\nadded some related explanatory content on page 31 of the revised manuscript:\n“Post-cycling characterizations were conducted to evaluate the structural, coordination\nenvironment, and spin-state stability of the Co,Ni-MoS catalyst. XRD and TEM\n2\nanalyses confirm that the 2H-MoS phase, lattice fringes, and crystal structure remain\n2\nwell preserved after prolonged cycling, indicating the absence of structural degradation\nor phase transformation (Supplementary Figs. 43 and 44). XPS spectra of Co 2p and Ni\n2p show nearly unchanged binding energies and peak features, suggesting stable\noxidation states without metal dissolution or chemical poisoning (Supplementary Fig.\n45). Moreover, magnetic susceptibility measurements reveal that the localized magnetic\nmoments of the spin-active Co/Ni sites are retained after cycling (Supplementary Fig.\n46). Consistently, Li-S cells reassembled with the cycled catalyst exhibit well-defined\nredox features and preserved electrochemical kinetics (Supplementary Fig. 47).\nTogether, these results demonstrate that the Co and Ni dopants remain structurally\nanchored, chemically stable, and spin-active during long-term operation, confirming\nthe durability of the spin-regulated catalytic.”\nSupplementary Fig. 43 | XRD pattern of the Co,Ni-MoS catalyst, a, before cycling and\n2\n42\nb, after cycling.\nSupplementary Fig. 44 | The morphology and structural composition analysis, a, TEM\nimage, b, High-resolution TEM image, and c, SAED pattern image of the Co,Ni-MoS\n2\ncatalyst before cycling, and corresponding morphology and structural composition\nanalysis, d, TEM image, e, High-resolution TEM image, and f, SAED pattern image of\nthe Co,Ni-MoS catalyst after cycling.\n2\nand after cycling, a, Co 2p, and b, Ni 2p XPS spectra.\nSupplementary Fig. 46 | 1/χ versus temperature plots and the calculated μ of Co-Ni-\nm eff\nMoS at a, before cycling and b, after cycling.\n2\n2\ncycling and b, after cycling.\nComment #5: The innovation of this paper is that the spin moment of the catalyst\nregulates the spin state of the reaction intermediate, thereby lowering the energy barrier.\nHowever, all the evidence (such as DOS calculations and magnetic susceptibility tests)\nis a characterization of the static and initial state. There is a lack of experimental data\nfor directly detecting spin state changes during electrochemical reactions.\nResponse: We sincerely thank the reviewer for raising this insightful and technically\nimportant question, which raises an important issue regarding the experimental\ndetection of spin-related processes during electrochemical reactions.\nIn response to this comment, we conducted additional magnetic measurements on\nelectrode materials during electrochemical cycling. Specifically, cathodes from\nbatteries without a catalyst and those incorporating Co,Ni-MoS were retrieved after\n2\ndischarge to different potentials (2.0 V and 1.7 V), and their magnetic moments were\nsubsequently measured. The experimental results show that, in the absence of the\ncatalyst, no detectable magnetic moment is observed at different discharge potential\n(Figure R4). In contrast, cathodes containing Co,Ni-MoS exhibit a clear and\n2\nmeasurable magnetic moment even after electrochemical cycling. These observations\nindicate that the introduction of Co,Ni-MoS enables the system to maintain a spin-\n2\npolarized reaction environment throughout cycling process.\nThis finding is highly relevant because the Li S → Li S conversion intrinsically\n2 2 2\ninvolves a spin-state change: both Li S and Li S are non-spin-polarized species,\n2 2 2\nwhereas the reaction intermediates is spin-polarized, as revealed by our theoretical\n44\ncalculations. In a non-magnetic environment, such a spin-state mismatch requires an\nadditional spin-flip process, leading to intrinsically sluggish reaction kinetics. By\ncontinuously providing a spin-active catalytic environment, Co,Ni-MoS effectively\n2\nmitigates this spin-related kinetic penalty and thereby accelerates sulfur conversion.\nIt should be emphasized that the present magnetic measurements cannot directly\nobtain the spin states of individual sulfur species (Li S , the spin-polarized intermediate,\n2 2\nor Li S) during the reaction. This limitation arises from two fundamental experimental\n2\nconstraints. First, the transient intermediates involved in the solid-solid sulfur\nconversion cannot be isolated or directly probed under electrochemical conditions.\nSecond, during battery operation, Co,Ni-MoS and sulfur species (Li S or Li S) are\n2 2 2 2\nstrongly coupled, making it experimentally infeasible to decouple and independently\nquantify their magnetic contributions.\nBased on the above considerations, the combination of ex situ magnetic\ncharacterization after discharge and theoretical calculations provides strong and\nmutually consistent evidence that a spin-polarized catalytic environment plays a critical\nrole in lowering the energy barrier of the rate-determining step and promoting sulfur\nconversion kinetics. We sincerely appreciate your comment, which highlights an\nimportant frontier in the field and provides valuable guidance for our future\ninvestigations into spin-regulated electrochemical reactions.\nFigure R4. Magnetic hysteresis (M-H) loops of cathodes in cells without Co,Ni-MoS\n2\ncatalyst after discharged to a) 2.0 V and b) 1.7 V, and in cells with Co,Ni-MoS catalyst\n2\ndischarged to c) 2.0 V and d) 1.7 V.\nelectronic conductivity and lattice strain be further considered among the existing 29\ndimensions?\nResponse: We sincerely appreciate your insightful suggestion regarding the inclusion\nof electronic conductivity and lattice strain as additional features. To address this\nconcern, we conducted a supplementary screening and evaluated these properties\nthrough feature engineering and model performance assessment.\nFirst, to represent the electronic conductivity more explicitly, we calculated the Fermi\nlevel, the electronic density within ±0.3 eV of the Fermi level, and the band gap for\nall samples. Meanwhile, lattice strain was also obtained. All newly introduced features\nwere subsequently evaluated within the same correlation-analysis framework, as\nillustrated in the updated Pearson matrix (Figure R5). The Fermi level, electronic\ndensity, and the band gap exhibit strong pairwise correlations with several existing\nfeatures, indicating significant features redundancy. SHAP-based interpretability\nanalysis further revealed that both lattice strain and the newly added electronic features\ncontribute negligibly to model performance (Figure R6). Moreover, their inclusion\nslightly reduces the generalization ability of the predictive model.\nAlthough electronic conductivity and lattice strain were not explicitly labeled as\nindependent features in the final feature set, their physical effects are inherently\nencoded within the retained variables. Electronic conductivity is already represented\nthrough features such as d-/p-band centers, spin polarization, electronegativity and\nionization potential, while lattice strain is quantitatively reflected by atomic distance\nrelated parameters, covalent radii, and mismatch parameters. Therefore, we believe that\nthe final feature space sufficiently captures both electronic transport and structural-\nstrain effects that govern catalytic behavior.\nIn summary, we did test the incorporation of electronic conductivity and lattice strain\nrelated features, but their redundancy and marginal contribution suggest that explicit\n46\ninclusion is unnecessary. The existing features already encode these physical effects in\na comprehensive manner, ensuring model robustness without feature inflation. Thank\nyou once again for your comment.\nFigure R5. Correlation heatmap of newly added electronic conductivity features (Fermi\nlevel, electronic density, and band gap) along with lattice strain, evaluated against the\nfull feature space.\nFigure R6. SHAP interpretability analysis of the model after incorporating the newly\n47\nintroduced electronic conductivity and lattice strain related features.\nComment #7: The following literature in the same direction may be helpful:\nAngew. Chem. Int. Ed. 2025, 64, e202416176.\nResponse: Thank you for the careful evaluation of the manuscript and for\nrecommending this relevant reference (Angew. Chem. Int. Ed. 2025, 64, e202416176).\nWe have carefully studied this work and agree that it is closely aligned with the\nemerging research direction of spin regulation in Li-S batteries. In that study, ligand-\nfield engineering via fluorine coordination enables the controllable transition of Co2+\n(3d7) from a low‐spin to a high‐spin configuration, thereby significantly accelerating\nsulfur redox reactions through enhanced Co-S and Li-F interactions. This work\nprovides important insights into field effect driven spin state modulation.\nThis study focuses on elucidating the fundamental origin of the sluggish kinetics\nof the rate-determining step in sulfur conversion and on revealing the deeper\nmechanistic role of spin regulation in promoting solid-solid transformations. We find\nthat both Li S and Li S are non-spin-polarized species, whereas the reaction\n2 2 2\nintermediate exhibits pronounced spin polarization. This spin‐state transition\nintroduces an additional kinetic penalty associated with spin conversion, which\nseverely limits the reaction kinetics. In contrast to strategies that alter the spin state of\na single metal center through ligand-field modulation, our work concentrates on\nachieving local spin polarization within the 2H-MoS framework through bimetallic\n2\ndoping. Specifically, we designed a series of dual-transition-metal dopants and\nincorporated them into the MoS lattice to systematically investigate the structure-\n2\nactivity relationship between the local spin environment and catalytic performance at\nthe rate‐determining step of the SRR. DFT calculations reveal a clear negative\ncorrelation between the spin density at the active sites and the thermodynamic energy\nbarrier for Li S → Li S conversion, indicating that higher spin density facilitates faster\n2 2 2\nLi S deposition kinetics. In Co,Ni-MoS , dual-metal doping induces a spin-polarized\n2 2\ncatalytic environment that allows the Li S → Li S conversion to proceed via a spin-\n2 2 2\n48\nto-spin matched reaction pathway. Such spin alignment facilitates quantum spin-\nexchange interactions and opens an efficient channel for spin‐dependent electron\ntransfer, thereby significantly accelerating the rate‐determining step. Among all\ninvestigated catalysts, Co,Ni-MoS exhibits the highest spin magnetic moment,\n2\nenabling effective regulation of the spin states of the reactants and products at the active\nsites, forming efficient spin‐electron transfer channels and substantially lowering the\nreaction barrier for the Li S → Li S conversion.\n2 2 2\nTherefore, whereas the cited work primarily emphasizes single-site spin-state\nengineering through field effects, our study establishes a spin-kinetics correlation and\nproposes a lattice-level spin-polarization mechanism induced by dual-atom doping.\nThese two strategies, operating at localized and lattice-cooperative levels, respectively,\ntogether highlight the broad potential of spin engineering for advancing Li-S battery\nchemistry.\nWe again thank you for pointing out this important work, which has been cited and\nbriefly discussed in the revised manuscript (reference 44).\n“44. Li, Q. et al. High Spin-State Modulation of Catalytic Centers by Weak Ligand\nField for Promoting Sulfur Redox Reaction in Lithium-Sulfur Batteries. Angew. Chem.,\nInt. Ed. 64, 2416176 (2024).”\nThanks to the editor and reviewers’ comments, we have made extensive\nmodifications to our manuscript and supplemented extra data to make our results\nconvincing. Thank you again for your positive comments and valuable suggestions\nto improve the quality of the manuscript.\n49"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my comments. The manuscript is acceptable now", "reviewer_2": "The authors have made extensive investigations in detail to elaborate the points and strengthen the revised manuscript according to the comments and suggestions from the Reviewers. Therefore, I recommend the revised version of the manuscript for publication.", "reviewer_3": "The review comments were effectively answered and replied. Considering the innovation and workload of this work, it is considered acceptable for publishing on Nature Communications."}, "author_response": ""}]

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