Real-time artificial intelligence for solid-state lithium metal batteries

Domain: physics
Published: 2025-12-16
Editorial outcome: minor_revision

Abstract
Battery health often evolves from an initial regime of slow capacity decay to a fast decay regime, due to catastrophic electrochemical reactions to cause the battery failure. The signals of such failure, however, often emerge much earlier during cycling, an early detection of which may help prevent the failure through real-time adjustment of cycling procedures. Here we demonstrate the integration of machine learning module directly into electrochemical battery test stations to constantly detect the signals from battery current and voltage profiles, followed by reinforcement learning–based real-time adjustment of cycling procedures for greatly elongated battery lifetime. We use solid-state lithium metal batteries to demonstrate this proof-of-concept implementation of built-in intelligence for future battery management systems, leveraging their high current density and fast cycling to generate abundant data and greatly shorten the development feedback loop. This intelligent approach improves battery lifetime by 265% and accumulative specific energy based on active material by 250% at 80% state of health, which is achieved through an effective control of detrimental interface reactions. This work showcases a pathway toward real-time artificial intelligence control of batteries for longevity and safety beyond human expertise.

Introduction
The Li-ion battery is one of the most powerful technologies in our era for decarbonization and combating climate change 1–4. The longevity and safety concerns, however, constantly challenge its applications since the birth of the technology. One typical failure mode in Li-ion battery is the lithium dendrite induced short circuits or side reactions. The development of solid-state batteries shows that Li dendrite penetration may be prevented through hierarchical structure and interface designs of the negative electrode and electrolyte layers 5–8. However, Li dendrite can still cause various problems in solid-state batteries through its high reactivity with electrolyte and negative electrode materials 9–13, where fast capacity decay upon cycling can often be observed. For example, in Supplementary Figs. 1 and 2a, these solid-state batteries tested in our lab used relatively low negative to positive electrode capacity ratios (N/P ratio), which is preferred for practical batteries but is also a factor that can often boost Li dendrite growth or interface reaction, and hence battery failure. The capacity retention of these batteries is nonlinear, often with a “kneepoint” 14–16, beyond which rapid decline in capacity occurs. The origin of the kneepoint is complicated and lacks unified explanations 17,18. In Li-ion battery, it can often be caused by Li dendrite penetration induced internal short circuit, while in our solid-state batteries it is more likely related to the Li dendrite growth induced interface reactions. When the kneepoint appears in the battery cycling, both state of health (SOH) and remaining useful life (RUL) will drop quickly. SOH and RUL are two performance metrics widely used in the battery industry to describe the status of batteries. Previous modeling and artificial intelligence (AI) based methods also focused on the two metrics in their predictions 19–23. Nevertheless, the emergence of the kneepoint always challenges the prediction due to the dramatic change of capacity decay rate across the point 14,24.

For a given battery configuration, it was noticed that charging strategy can influence the lifetime 25,26. Derived from the primitive constant current (CC) and constant voltage (CV), the various fast charging strategies nowadays are mainly categorized into multi-stage CC-CV or pulse current based on charging waveform 27,28 and optimization control based on battery models 29,30. However, online modulation of charging strategies with battery cycling based on the real-time status of a running battery has not been implemented, as it requires real-time diagnosis of batteries and the decision-making capability of the program. In most previous cases, a charging strategy was firstly optimized and then the fixed strategy was executed to every cycle throughout the cycling test.

In this work, we develop a platform, as depicted in Fig. 1a, where the machine learning intelligence, consisting of two key modules of state perception and intelligent cycling, is connected with a running battery through the battery test station. The tester operates as both the experimental instrument and the physical main body for real-time integration of the AI agent running on the workstation. The platform can thus monitor the current battery state and then based on the detected data in previous N cycles decide how to change the battery cycling procedures for any following N cycles, where N is chosen to be 3 and the cycling procedures include various C-rates (1C = 150 mA/g) and voltage cutoffs in this particular work. As shown in the detailed workflow (Fig. 1b), the AI workstation directly communicates with the tester hardware via physical connection of COM APIs over TCP/IP. Every 3-cycle data is employed to evaluate the battery state and adaptively control the charging strategy. This implementation supports real-time closed-loop control without relying on proprietary vendor software.

As a proof-of-concept demonstration, we utilize the fast charging and discharging capability of our solid-state batteries to 20C-rate at low positive electrode loading 5, to greatly accelerate the data generation and reduce the feedback time in the proof-of-concept development. This enabled rapid lifetime cycling and efficient model iteration. While commercial Li-ion batteries offer better reproducibility, their limited rate capability would substantially prolong the experimental timeline, especially under AI-extended lifetimes. However, implementing our approach to commercial Li-ion battery and BMS system will be an obvious next step through the collaboration with the battery industry. Nonetheless, our strategy in principle could also be adapted to conventional Li-ion batteries or systems.

Results
Our results highlight the built-in intelligence for fast charging capability of solid-state lithium metal batteries, where both longevity and SOH stability of batteries are greatly improved. In comparison with the control batteries with conventional fixed strategies, our real-time AI for battery charging strategy significantly postponed or even prevented the occurrence of the kneepoint before the industrial standard of 80% SOH. It is worth noting that neither the authors, the research field as far as we know, nor our AI platform has any a priori knowledge of a clear microscopic failure mechanism related to the kneepoint of these batteries. Interestingly, however, our experimental characterization and computational modeling later show that AI did identify an optimal strategy within our framework to modulate the evolution of the most relevant interface reaction, with the mechanism being discussed. This suggests the potential of such AI platform to help achieve fundamental understandings in battery electrochemistry as well.

More specifically, the state perception module detects battery states evolution with cycling in three different states of stable, transitional, and decay ones (Fig. 2a). While 0.8 SOH is commonly used in industrial settings as the end-of-life (EOL) threshold, the definition of state segmentation is to isolate and examine the rapid degradation behavior around the knee point. The transitional state represents a temporary yet critical period where internal failures and side reactions accelerate and have not yet stabilized. That means a battery in the transitional state, especially in an early stage of the state, can possibly return to the stable state through intelligent cycling (e.g., Fig. 2d, e). Only after the fast degradation phase does the battery enter the final decay state, characterized by a slower rate of capacity loss at very low capacities. The transitional state contains the kneepoint, with the two boundaries decided by first and second derivatives in a capacity retention profile. The left boundary of the transitional state is defined as when the acceleration, or second derivative, becomes continuously negative, and the right boundary is when velocity, or the first derivative, reaches its minimum negative value. The state definition here is supported by our experimental characterization and reflects the evolution of dominant electrochemical reactions in the batteries.

These solid-state batteries follow a configuration of NMC811 as positive electrode, multilayer electrolytes as separator, and Li/Graphite as negative electrode, i.e., Li/Graphite|LPSCl|LGPS|LPSCl|NMC811, where NMC811 represents LiNi0.82Mn0.07Co0.11O2, LPSCl represents Li5.5PS4.5Cl1.5 and LGPS represents Li10GeP2S12. Scanning electron microscopy (SEM) cross-section images of the batteries at different cycling stages in Fig. 2b show the evolution of reactions at the LGPS|LPSCl interface. In the stable state, the interface remains overall intact, with only isolated dark spots occasionally observed, suggesting that the local area is with low Z number in chemical elements based on the contrast mechanism of the backscattered electron microscopy. As the battery progresses into the transitional state with cycling, more black points stay close to each other at the interface and eventually connect into a black line region in the decay state. Deep into the decay state, the interface region becomes thicker with voids but less dark 31,32. Energy dispersive X-Ray spectroscopy (EDS) mapping further shows that the dark regions is rich in O and Ge, deficient in P and S (Supplementary Fig. 2), indicating the evolution is dominated by interface reactions between Li metal dendrite and LGPS electrolyte, with Li-Ge-rich local decomposition products initially generated at the transitional state and more elements of S and Cl later enter the reaction region deep into the decay state. Note although Li cannot be directly detected by EDS, it can react with oxygen thus trap them locally to mark the location of Li.

A historical dataset in the research group of such Li||NMC811 solid-state batteries with the knee point feature in the capacity retention test (Supplementary Fig. 1) was further used to train the neural network in order to predict the state of a running battery from the voltage and current data (Methods). Notably, this dataset is very scalable as it is generated by diverse solid-state batteries by various group members for different projects previously, rather than coming from a dedicated collection for this particular project. That said, the consistent positive electrode/negative electrode configuration ensures a focused evaluation of the ability of our model to generalize across different degradation patterns induced by electrolyte variations. To construct the training samples, each battery is not treated as the data unit; instead, the model ingests abundant 3-cycle segmental data from the entire cycling of each cell as the data unit.

The state perception module can read the running battery on the voltage and current data of every 3 cycles. Since the geometrical shape details in voltage profiles as an ensemble reflect the collective behavior of the internal electrochemical reactions, the state perception module, equivalently speaking, monitor these reactions in situ for a working battery, providing sufficient nonlinearity and complexity for the input layer of deep neural network (DNN) models. The real-time state information of the past 3 cycles each time is then fed into the intelligent cycling module to evaluate how to change the charging strategies for the next 3 cycles. We employ reinforcement learning (RL) to provide dynamic decision-making for the charging action selection every 3 cycles, whereby the integration of battery aging states supports the reward function design as detailed in Methods, with the ultimate goal to extend the lifespan of solid-state batteries and attempt to eliminate the kneepoints.

To effectively leverage the hidden electrochemical information stored in the shape feature of battery voltage profiles, a method of treating them as 2-dimensional (2D) images is employed to combine with generalized image neural network (Fig. 2c), where the image includes current value, voltage shape, and capacity. To present the time-sequence information during cycling, the data of every 3-cycle is integrated into R, G, B channels of the shape image, respectively. Each data point here in the cycle has equal significance represented as pixels in the 2D image, which is more robust than the original 1D vector form as it is less affected by numerical values. With the augmentation of battery dataset in the future, more complex large models can also be employed to achieve generalization effects.

For the intelligence cycling module, a range of charging strategies is provided with different C-rates and voltage cutoffs (Fig. 2d). To avoid the additional complexity from optimized multi-stage CC-CV charging 27 and varying discharging current within each cycle 30, in our proof-of-concept demonstration here only the galvanostatic strategies within each cycle are adopted, although all these strategies are compatible with our methodology and can in principle be built into the platform in the future. C-rate (15~20C) and voltage cutoff (4.2~4.3V) are defined as parameters within a relatively small range of variation to exclude big change of specific capacity and related lifetime change directly caused by different charging strategies. A total of 18 available charging actions with a range of area capacity (0.309~0.438 mAh/cm2) can be combined from different C-rates and cutoffs, which are sorted for the intelligence cycling module. The AI agent then outputs the online selection of charging actions based on the real-time battery states and shape features, thereby dynamically achieving updated decisions of fast charging strategies between cycles. Through integrated control of solid-state batteries, closed-loop experiments are performed with acceptable timescales at the level of 15~20C-rate of charging speed. A single intelligent cycling experiment typically requires the occupancy of the AI channel for two to three weeks, during which these parameters of charging actions are fully utilized. Note that the area capacity can be further increased to 3 mAh/cm2 based on a different negative electrode configuration reported by us earlier 33, however, it will limit the C-rate of cycling within 5C, slow down the proof-of-concept demonstration here by around 5 times.

The solid-state batteries with intelligent cycling show significantly improved capacity retention and aging trajectory (Fig. 3). The batteries of both the intelligent cycling and the benchmark control with fixed CC cycling were tested multiple times for statistically significant comparison. The SOH range above 80% was selected for analyzing the longevity of batteries, where the advantages of intelligent cycling became increasingly evident. Above 90% SOH, there is no significant difference between intelligence and benchmark. As the SOH declined to 80%, the average total cycles under the intelligent cycling condition increases significantly, reaching 265% (1915 vs. 723) of the benchmark value (Fig. 3a), while the accumulated specific energy, based on the active material, achieves a 250% enhancement (Fig. 3b). A further segment analysis by every 5% SOH reveal that the average number of cycles within each segment consistently declined for the benchmark, while that of the intelligent cycling initially increased, with the highest capacity retention observed in the 90% to 85% SOH interval. This trend culminated in an over 550% increment in the cycle count from 90% to 80% SOH compared to the benchmark. (Supplementary Fig. 3a, b).

Figure 3c presents the cycling results of 7 benchmark batteries, whereby all exhibited the kneepoint feature in their capacity retention performance. The benchmark conditions were fixed within the regime of intelligent charging actions, where 18C-4.25V was selected as a representative midpoint. This allows a meaningful and quantifiable comparison with the intelligent cycling, which dynamically explores C-rates and voltage cutoffs both above and below this benchmark. Also, three of the benchmark cells were cycled at 20C-rate, and their performance was found to be comparable to those cycled at 18C-rate. This reinforces the representativeness and robustness of the chosen benchmark condition of 18C-rate and highlights that higher C-rates do not necessarily lead to inferior cycling performance in solid-state lithium metal batteries, which is consistent with our earlier findings 5.

The representative result of intelligent cycling is shown in Fig. 3d, where no kneepoint before 80% SOH was observed. Specifically, the charging actions dynamically adjust the C-rate and cutoff voltage, allowing the battery to oscillate between stable and transitional states (Fig. 3d, e). This behavior effectively delays or eliminates the emergence of kneepoints, which significantly extended the battery lifetime. Note that when monitoring the battery state during intelligent cycling, we find that different charging actions correspond to distinct changes in battery states, as reflected in statistical results (Supplementary Fig. 3c, d). The stable state is more frequently associated with higher C-rates, while the transitional state is primarily concentrated in the lower C-rate range.

To better understand the mechanisms underlying the intelligent cycling, the battery states were analyzed according to the corresponding selections of charging actions (Fig. 4a). With cycling, although the battery begins to enter the transitional state in the lower C-rate action region, the higher C-rate actions still maintain the stable state. Thereby when the intelligent cycling automatically jumps between high and low C-rates, the strategy seems to provide alternative pathways for the battery state evolution, where a transitional state can go back to the stable state. By dynamic navigation of actions, it can bypass irreversible degradation regimes and maintain the cycling stability of batteries, even under the well-known electrochemical stress of high C-rates. In contrast, the benchmark battery with fixed charging actions lacks such capability, quickly entering the sustained transitional state at an earlier cycle index, with the capacity kneepoint emerging accordingly (Fig. 4d). These results demonstrate that intelligent cycling significantly prolongs the stable operation phase. As cycling further progresses, the battery inevitably approaches a point where degradation becomes irreversible. Around 2000 cycles, the state perception output shifts to 1 across most action indices, indicating that the available charging actions to preserve the battery in a stable state are limited. This simultaneous onset of transitional states across large number of actions signals the transition of battery into an irreversible degradation. This behavior highlights the ultimate constraint of the electrochemical system, reflecting the capability limit of intelligent cycling to recover the battery to a stable state.

Shape images as the input for the neural network can be dimensionally reduced through principal component analysis (PCA). By integrating and reshaping the first three principal components (PC1, PC2, PC3) of all shape images, they largely capture the features such as capacity and voltage reduction with the cycling process (Supplementary Fig. 4). Specifically, PC1 reflects the global capacity fading trend (noted by horizontal shrinking of voltage plateaus), while PC2 and PC3 are linked to local fluctuations and distortions, likely representing internal resistance changes or interfacial instability. The distribution and motion trends of PC1-PC2 in the PCA space extracted from the voltage part of shape images reveal distinctions between intelligent cycling and the benchmark. The variability of charging actions in intelligent cycling inevitably introduces some confounding effects into the PCA results. Therefore, the PCA analysis should not be regarded as a direct indicator of state-of-health, but rather as a visualization tool to trace the evolution of battery state features. The PCA data points for intelligent cycling exhibit a divergent band pattern under charging actions, expanding over a larger area of PCA space (Fig. 4b). This suggests the intelligent cycling of dynamic strategy enables a broader and more flexible exploration of electrochemical states, allowing the battery to jump between states of little path dependence and hence access the principal component space that otherwise will be inaccessible in a normal cycling of fixed strategy, where unidirectional trajectory evolution in the principal component space shows a strong path dependence. Euclidean distances calculated between adjacent cycles in the PCA space quantify the state transitions induced by each action step composed of three consecutive cycles (Fig. 4c). For intelligent cycling, these distances reveal more frequent and larger shifts between consecutive steps, reflecting rapid and often reversible transitions between stable and transitional states, which effectively elongates the trajectory of the state evolution in the PCA space and slows down the degradation. In comparison, benchmark cycling exhibits narrow, unidirectional state trajectory (Fig. 4e) and minimal Euclidean displacement (Fig. 4f), corresponding to irreversible degradation and early emergence of knee points. The limited state variability under fixed charging actions restrict the ability to recover once degradation accelerates.

Regarding the interface reaction evolution shown in Fig. 2b, the cross-sections of batteries after the two different types of cycling were observed by SEM (Fig. 4g). The interface remained dense under intelligent cycling after 1700 cycles, whereas voids obviously present under the benchmark cycling condition after 1500 cycles. The evolution of electrochemical reactions inside the battery during cycling is often nonlinear. More specifically, the unique interface reaction pattern with a certain periodicity of reaction spots in Fig. 2b (ii) suggests the presence of long-range interactions between those spots. To simulate the effects of dynamic charging actions during intelligent cycling, we proposed a simple 2D grid model by assuming that the parameter determining the interaction field can fluctuate, corresponding to the perturbation introduced by varying charging actions (Methods). Simulations reveal that as this fluctuation ratio increases, the decomposition pattern can change from sparse degradation points with severe decomposition at each point to a more uniform and denser pattern with less severe degradation at each point (Supplementary Fig. 5). Additionally, we observed that the average degradation extent initially decreases and then increases as the fluctuation ratio grows. This behavior arises from a trade-off between the decreasing degradation extent at individual points and the increasing number of degradation points. These findings provide an intuitive picture to further discuss the effect of the varying charging actions, suggesting that although it can improve cycling longevity, excessive fluctuation may lead to diminishing rewards.

This prediction is supported by our experiment, where the impact of increasing number of charging actions was evaluated through manually pre-programmed battery testing procedures (Fig. 4h and Supplementary Fig. 6). Comparing those tests with fewer actions of 2 or 4 (Supplementary Fig. 6a–c) and with more actions of 6 (Supplementary Fig. 6d, e) with the action switch of every 50 cycles, it shows that increasing the number of actions helps the retention before the effect of action number converges at 6 actions. All the 18 charging actions were executed in the random action test (Supplementary Fig. 6h) and were updated every 3 cycles, matching the interval and actions used in intelligent cycling. The lower capacity retention in the random action test than the intelligent one and the 6-actions one suggest the importance of intelligent cycling when more actions (e.g. 18) are included, without the need to test and program the optimum number of actions and the frequency to switch them. Action number dependence (Fig. 4h) offers insight into the underlying mechanisms of increasing charging complexity. By comparing cycling performance up to 1500 cycles, a correlation emerges between the number of available charging actions and battery longevity. Varying both C-rate and cutoff can work, however, the action number is not linearly proportional to the performance improvement, tending to saturate beyond a certain point. Notably, under the same available action number, test with randomly varied charging actions exhibits no knee point, but the capacity decay is significantly faster than the average results achieved with the intelligent cycling. The 6 cutoff group shows slightly higher capacity retention at 1500 cycles compared to intelligent cycling. This outcome likely reflects the effect of diminishing rewards when increasing the number of available charging actions beyond a certain level. As also illustrated in the 2D grid model (Supplementary Fig. 5), a moderate level of fluctuation may strike the balance between the extent and distribution of local degradations, leading to temporarily favorable outcomes. The 6-cutoff group may happen to lie near such a marginal optimum. However, given the abrupt nature of capacity knee points, short-term advantages in capacity retention don't necessarily imply improved long-term performance. Intelligent cycling is thus designed to dynamically adapt to evolving battery states, aiming to delay the onset of irreversible degradation automatically.

In summary, this work opens a promising direction of real-time AI for battery management system, by integrating machine intelligence with real-time charging strategies to enhance the battery longevity and safety, as demonstrated with solid-state lithium metal batteries. By enabling real-time perception and regulation of battery states, the proposed intelligent cycling approach achieves significant improvements in cycle life and accumulated specific energy at high current density, minimizing dendrite growth related interface reaction issues. Our approach also fully utilizes the information stored in the voltage and current profiles, minimizing the reliance on physical sensors and thus reducing the device-level cost. The method is also compatible with advanced fast charging strategies, such as multi-stage CC-CV protocols, and varying operational external pressure or environmental temperature, etc., highlighting its adaptability to diverse application scenarios. Moreover, the realization of state perception enables the proactive prediction and mitigation of adverse battery states. Moving forward, the integration of this framework is expected to be broadly adaptable to commercial battery systems, and its compatibility with edge computing devices 34 will present opportunities for even smarter, decentralized battery management systems, ensuring enhanced reliability and safety in next-generation energy storage solutions.

Methods
Real-time connection platform
A custom real-time connection platform was developed to enable closed-loop cycling control between the AI module and the battery testing station. Recently developed modular architecture can enable rapid development of efficient hardware designs for various applications 35. The platform integrates a Python-based control on a computer workstation with the battery testing hardware (Solartron XM MTS) for real-time data exchange and parameter updates. Notably, this implementation does not rely on proprietary software modifications to the hardware. The control system was implemented using Component Object Model (COM) automation Application Programming Interface (API), which expose programmable interfaces for accessing and managing hardware operations, including experiment execution, instrument status monitoring, and data retrieval. An established Python environment running was used to interact with these COM interfaces, enabling direct control from the workstation. Physically, the workstation is connected to the Solartron system through a standard Registered Jack 45 (RJ-45) Ethernet interface over Transmission Control Protocol/Internet Protocol (TCP/IP). The instrument is visible on the local network and can be accessed through its assigned IP address. The latency introduced by the closed loop is negligible relative to the cycling timescale. Reliability is also ensured by leveraging the same standard interface used in routine experiments, with no observable disruption or delay during long-term operation. For broader applicability, commercial vendors generally confirmed that modern programmable battery testers support external integration via open interfaces. Therefore, the proposed platform is broadly transferable across different hardware systems.

State perception module
The relevant dataset includes 28 all-solid-state batteries with the knee point feature with the C-rate ranging from 1C to 25C. Despite the diversity in data sources, all solid-state cells share the same electrochemical configuration that NMC811 positive electrodes paired with lithium metal negative electrodes. These electrolyte differences among the cells introduce measurable variations in degradation behavior, which can evaluate the neural network’s generalization performance across different degradation pathways. Importantly, the model was not trained to target any specific failure mechanism, allowing it to capture broader degradation dynamics without overfitting to a particular pathway.

The batteries used in subsequent intelligence and benchmark tests were excluded from the training and validation of the state perception model, there by preserving the generality and independence of this dataset. We previously found that treating voltage curves during battery cycling as 2D images and then encoding them can enable the shape feature vectors to successfully uncover hidden battery information for machine learning-based predictions of commercial Li-ion batteries 36,37. Here, we fully retain both current and voltage information, and more importantly make the 2D image of each cycle equivalently represent each data point by pixels, rather than the previous method of using less robust numerical values in the 1D shape feature vector. According to the natural RGB channels of images, the data of every 3-cycle is integrated into one shape image for time-sequence information during cycling.

State perception module is based on residual network 50 (ResNet50) 38, which is fed with the above shape images as the input. A total of 83,422 such samples were used for training. The overall dataset was split into training, validation, and test sets at a 16:6:6 ratio, corresponding approximately to 60%, 20%, and 20% of the full 28 batteries. The detailed configurations of the batteries were masked during dataset preparation. Each battery’s data was included exclusively in one subset to prevent data leakage and ensure generalization to unseen batteries.

The overall prediction accuracies for validation and test reach 89% and 86%, respectively. The confusion matrix provides specific insight into the performance evaluation (Supplementary Fig. 7), with detailed metrics summarized in Supplementary Table 1. Low precision means a true battery state is more likely to be predicted erroneously than missed, while low recall implies that the true battery state is more likely to be missed than falsely predicted. Both stable and decay states consistently indicate high precision and high recall, ensuring reliable identification. For the transitional state, missing its truth will risk missing the warnings of rapid capacity decay caused by an approaching knee point, undermining the battery cycling stability and the protection by the intelligent cycling. Therefore, the overall prediction performance prioritizes high recall across all states. By the recall-focused F2-score metric, the analysis confirms that the online prediction is robust and reliable across different battery states.

Intelligent cycling framework
To optimize the adaptive fast charging strategies across cycles, we employed the reinforcement learning (RL) approach using a Deep Q-Network (DQN), as noted by long-timescale research 39. The model, implemented via Stable-Baselines3 40, was specifically tailored here for analyzing all-solid-state batteries. Here the intelligent cycling framework operated in a closed loop comprising the experimental environment, the state perception module, and the AI agent (Supplementary Fig. 8) 41. Every 3-cycle data was processed into shape image that served as the input to state perception module and the variable in observation space for the AI agent. Charging strategies are constituted of a range of action space, allowing the AI agent to utilize charging actions. As the outputs of the perception module represent the current battery states, the reward function is comprehensively designed by integrating three components: the current battery state, the selected action with C-rate and cutoff, and the coincided degree of shape feature across three consecutive cycles. For state rewards, when the perception module outputs a stable state, the state reward component is +1, activating the action-based rewards for the C-rate and cutoff. Within this framework, the reward favors high C-rate and cutoff values and penalizes lower values within a reward value of −0.5 to +0.5. When the battery state becomes transitional, the state reward is −1, and the state reward drops to −5 for the decay state. For the shape feature reward, a threshold is set to mitigate the influence of drastic capacity changes. Significant deviations in capacity curves across 3 cycles incurred penalties, while well-coincided shape features were rewarded, with values ranging from −0.4 to +0.4. Finally, the intelligent cycling framework selected optimal charging actions within the predefined range. This design balances the exploration of high-performance charging strategies while maintaining stability and avoiding rapid degradation. Multiple-experiment results of intelligent cycling and benchmark batteries are provided in Supplementary Figs. 9–14 and Supplementary Note 1.

Materials
The precursor of Li5.5PS4.5Cl1.5 (LPSCl) solid electrolyte was synthesized via planetary ball milling of Li2S (99.9%, Alfa Aesar), P2S5 (99%, Sigma-Aldrich) and LiCl (99%, Alfa Aesar) in a stoichiometric ratio. The starting materials were ball milled under argon atmosphere in 80 mL ZrO2 jars for 16 h at 460 rpm in a planetary ball mill PM200 (Retsch GmbH, Germany). Eighteen 10-mm Y-ZrO2 beads were paired with 2 g starting materials in total. The ball milling program was set to be a cyclic process of 10 min milling and 2 min resting. The resulting precursor was then sintered in a quartz tube at 550 °C for 1 h in an argon gas flow. The sintered product was ground with a mortar and pestle for subsequent applications. The Li10GeP2S12 (LGPS) solid electrolyte was a commercial product from MSE Supplies.

Battery assembly
We designed the positive electrode loading and N/P ratio of all-solid-state batteries with multi-layer electrolyte as separator, graphite covered lithium metal as negative electrode, and NMC as positive electrode for fast charging experiments by modifying our previous battery configuration 5. The positive electrode for the all-solid-state batteries was fabricated into a homogeneous dry film by mixing 70 wt% LiNi0.82Mn0.07Co0.11O2 (MSE Supplies), 30 wt% as-prepared LPSCl and 3 wt% extra PTFE powders and repetitive calendaring with stainless steel plate and stick. The positive electrode film was punched into 1/4” disks for battery assembly. The negative electrode was a graphite-protected lithium metal foil. A uniform graphite film was made by mixing natural graphite (MSE Supplies) and 5 wt% PTFE powders. The graphite film was punched into a 5/16” disk and carefully attached to a 1/4” thin lithium metal foil. During battery assembly, the negative electrode film, 30 mg of LPSCl, 110 mg of LGPS, 30 mg of LPSCl and the positive electrode film were stacked in sequence in a PTFE casing under an argon atmosphere. A formation pressure of 387 MPa was exerted and maintained for 1 min to fulfill sufficient contact and a stacking pressure of around 150 MPa was kept during battery cycling. Positive electrode loading is correlated with battery aging trajectory at fixed layer thickness of graphite and lithium layers (Supplementary Fig. 15). As the loading increases and N/P ratio decreases in this battery system, knee point occurs earlier and eventually disappears with severe capacity decay at the beginning of cycling. Accordingly, we selected the positive electrode loading of 2.58 mg/cm2 and fixed the graphite film weight as 2 mg and lithium metal foil weight as 0.8 mg, which makes the knee point occurs stably in a cycling index range that is meaningful to the current project. Note that further reducing the positive electrode loading below 2.58 mg/cm2 and increasing the N/P ratio will make the battery too stable and severely delay the occurrence of the knee point, impeding a rapid development for this proof-of-concept test and demo. The battery measurements were conducted on Arbin BT2000 and Solartron XM MTS respectively for benchmark and intelligence tests, in 55 °C environment chambers. It is worth noting that the positive electrode loading can be increased above 15 mg/cm2, and the operational pressure can be reduced below 25 MPa in a pouch cell, although the cycling C-rate must be reduced to 5C 33. Although the reduced C-rate will slow down the proof-of-concept test by 5 times, the more practical pouch cell is also compatible with our AI platform of more complicated charging procedures in the future.

Scanning electron microscopy & energy-dispersive spectroscopy
Solid-state batteries were transferred to an argon-filled glovebox after cycling to various stages and disassembled for characterization. Samples for morphological and elemental measurements were prepared by blade cutting multilayer pellets, exposing flat cross sections. Samples were then sealed in aluminum bags under argon atmosphere and sent to the electron microscope. The entire sampling process was conducted at a temperature of 25 ± 0.5 °C. Cross-sectional images of cycled batteries were captured under the secondary electron mode of ZEISS Gemini SEM 560. An acceleration voltage of 5.00 kV, an aperture of 20.00 μm, and a working distance ~6 mm were used. For elemental mapping of sulfur, phosphorus, oxygen, and others in the cross sections, the backscattered electron mode of the instrument was employed with an acceleration voltage of 15.00 kV, an aperture of 60.00 μm, and a working distance of around 10 mm. Cross-sectional images emphasizing atomic-number differences were captured with such settings as well. All samples were sealed and transferred while maintaining an argon atmosphere to minimize air exposure.

Grid simulation
A 2D grid model is developed to simulate the influence of varying charging actions on the growth of decomposition spots as observed in SEM. Although the exact origin of the long-range interactions among these spots still remains unidentified, they are most likely the result of plastic strain field caused by local decompositions and the interaction of the field with ion conductions. We hypothesize that these fields govern the growth rate of existing decomposition points and the formation of additional ones. The interaction field generated by a decomposition point suppresses decomposition near its core, potentially due to current redistribution or strain field effects. At moderate distances, the field promotes decomposition, which could arise from increased current shunted from the core. Each point on a 2D grid (x, y) represents the extent of decomposition, u(x, y). A point is considered undecomposed if u(x, y) = 0, and decomposed otherwise, with the value of u(x, y) > 0 indicating the extent of decomposition at that point. The decomposition extent is irreversible, meaning that u(x, y) can only increase over time. Decomposition at each point is influenced by an interaction field generated by other decomposed points. The interaction field at a point (x, y) due to a decomposed point i with decomposition extent ui is assumed as: fi(u, r) = ui(r−3 − r−4), where r is the distance from point i to (x, y), computed as: r = sqrt((x − xi)2 + (y − yi)2). The decay proportional to r−3 is analogous to the solution of Laplace’s equation under a no-flux boundary condition for a spherical geometry. The parameter fluctuates as a(t) = a0(1 + rf cos t) where rf is called the fluctuation ratio. The total field at each point (x, y) combines the contributions from the interaction fields of all decomposed points and a base field B: F(x, y) = sum(fi(ui, r)) + B. At a point with existing decomposition (u(x, y) > 0), the decomposition grows as du(x, y)/dt = max(0, λgrowth * F(x, y)) where λgrowth is a constant controlling the growth rate. At an undecomposed point (u(x, y) = 0), the probability of forming a new decomposition center (u(x, y) is set to u0) in each time step dt is given by: P = λnew * F(x, y) * dt, where λnew is a constant determining the likelihood of nucleation.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, the authors employs real-time AI-driven state perception and reinforcement learning approaches to\nanalyze the critical role of intelligent charging strategies in suppressing dendrite growth and delaying the \"knee point\"\nphenomenon of solid-state lithium metal batteries. Furthermore, the effectiveness of the strategy and the evolution path of\nbattery states are validated through SEM/EDS characterization.\n1. Line 24: Suggest changing \"to decarbonization\" to \"for decarbonization.\"\n2. Line 34: Suggest changing \"In Li-ion battery it can...\" to \"In Li-ion battery, it can...\"\n3. Line 86: The different solid-state batteries in the dataset should be explained. Different electrode and electrolyte materials\noften exhibit distinct electrochemical degradation pathways. How can the universality of the network trained on different\nbattery systems be demonstrated?\n4. Line 128: The cycling results of the 7 benchmark batteries mentioned, specifically the cycling condition of 18C-4.25V,\nshould be evaluated for representativeness. It is generally assumed that lower rates lead to longer lifespan. When the\nbenchmark group is cycled at a lower rate, does the intelligent cycling strategy still maintain its advantage? Suggest\nsupplementing experiments.\n5. Please explain the supporting role of the analysis in Fig. 4b, c, e, and f for the main contributions of this article. The PCA\nanalysis demonstrates differences between intelligent cycling and benchmark testing in state space, but the analysis\nremains superficial and does not explain the specific physical meaning of the principal components or their relationship to\nbattery performance. Suggest providing a detailed explanation.\n6. In Fig. 4 h, it is evident that the capacity retention of the 6 cut-off group is higher than that of intelligent cycling.\nPlease provide an explanation.\n7. Line 172: The evaluation of the impact of adding charging actions to manually programmed battery test procedures should\ncompare the degradation of the two groups under the same rate and cut-off voltage frequency as in intelligent cycling.\nSuggest supplementing this comparison.\n8. Line 448: \"dacays\" should be corrected to \"decays\".", "reviewer_2": "This work presents a closed-loop battery testing platform incorporating AI for real-time adaptive cycling. The authors propose\na system in which a deep learning–based state perception module and a reinforcement learning (RL) agent interact with a\ncommercial battery tester to optimize cycling conditions (battery C-rate and voltage cutoff) based on recent cell behavior.\nThe approach is demonstrated on solid-state lithium metal batteries (SS-LMBs), and the results show significant\nimprovement in battery longevity and accumulated energy throughput compared to fixed-strategy testing benchmarks.\nThe work’s main conceptual innovation integrating AI models into the experimental loop of battery cycling—is timely and\nhighly relevant. It aligns well with emerging trends in intelligent BMSs, where ML could extend cycle life and reduce safety\nrisks, and potentially uncover hidden, incubating aging mechanisms to prevent failure or avoid knee point. This conceptual\nframework is a promising direction and represents a necessary evolution beyond static, model-based or heuristic cycling\nstrategies.\nStill, in reviewers view, the work as presented does not fully attain the proposed goals and objectives, and several critical\nissues limit its impact, reproducibility, and generalizability. While the paper introduces interesting elements, it falls short in\nseveral areas, which shall be commented on below:\n1. System architecture, battery testing infrastructure and reproducibility\nA key concern relates to the implementation of the AI–hardware integration, which is insufficiently described given that the\nmanuscript aims to present a hardware-software control platform.\nThe authors state that adaptive cycling was conducted using a Solartron XM MTS system, while fixed benchmark testing\nwas performed on an Arbin BT2000. The Solartron, however, is a commercial battery testing machine, but not a standard\nplatform for high-throughput or standard programmable BMS-level testing. It remains unclear:\n- A comprehensive description of the protocol(s) or software layer(s) used to enable real-time communication between the\nPython-based AI control and the Solartron system and Windows Operating System that controls the Solartron\n- Whether the feedback control was synchronous or batch-based, and how latency and reliability were evaluated and\nhandled\n- Is the AI module running on a separate workstation and received the Solartron data and then sends updates the control\nparameters? How?\n- How the system could be generalized to commercially relevant test stations (e.g., BioLogic, Maccor, Neware) or\nimplemented in edge computing\nIn addition, without a detailed system diagram (Fig. 1 is too general) or communication/control workflow, the platform's\nportability and reproducibility cannot be assessed. If the authors intend to present a novel AI-powered battery control system,\nthis aspect must be central in the manuscript. Currently, the AI agent appears more like an external script that interfaces with\na test station, rather than an integrated, deployable BMS architecture.\n2. Data sufficiency and model generalizability\nThe developed AI models rely on a dataset of just only 28 solid-state cells, collected from historical lab experiments, to train\nthe ResNet50-based state classifier. While the authors attempt to validate the classifier on independent cells, this dataset is\nextremely limited in terms of size, diversity, and representativeness—especially for training deep learning models and RL\nagents.\nIt is also unclear:\n- How the dataset was split (train/validation/test)\n- Whether cells from similar configurations were grouped across splits, risking data leakage\n- If any data augmentation or uncertainty quantification was applied to improve robustness\nFurthermore, all data appears to originate from lab made LMBs, which to this stage suffer from high variability and low\nreproducibility (as shown in Ext. Fig. 1). The use of such limited and variable data raises substantial questions about model\ngeneralizability, especially across different chemistries or battery formats.\nWhat remains strange to the reviewer is that, had the authors used commercial, off-the-shelf lithium-ion batteries—which are\nmore reproducible and come in diverse chemistries—they could have better demonstrated the robustness and wide\napplicability of the AI platform. And saving time wouldn’t be an issue, either (i.e., cycling at high temperatures, high rates,\nplating, etc.). This makes the case that solid-state LMBs were not necessary for this demonstration, and in fact, their use may\nhave constrained both the model’s performance and its relevance and add too much information not needed to present the\nplatform.\n3. Title and scope\nThe manuscript title emphasizes “real-time AI-powered solid-state lithium metal batteries”, which is misleading. The\ninnovation lies not in the materials science of solid-state batteries, but in the AI-based control methodology. Also, if the AI\nlogic isn’t embedded but is a software agent controlling a lab instrument externally, then calling this “real-time AI-powered”\ncontrol requires qualification. It's more of a scripted closed-loop AI-supervised platform.\nSimilarly, the manuscript devotes extensive space to microstructural evolution, SEM/EDS imaging, and even modeling of\ninterfacial degradation using a 2D grid simulation. While informative, these analyses appear secondary to the main\ncontribution. The platform, which should be central, is lightly described, lacking architectural detail, block-level diagrams, or\nalgorithm–hardware flowcharts. This mixing of material characterization with high-level systems and AI objectives creates\nconceptual fragmentation, detracting from the manuscript’s clarity and weakening the scientific message. If the core novelty\nis the intelligent cycling framework, then the focus must shift more decisively towards the architecture description, the\nexperimental integration of control and validation under diverse scenarios and the comparison with other heuristic and\nconventional adaptive methods.\nIn total, this manuscript aims to introduce a novel methodology that combines data-driven state classification with\nreinforcement learning for adaptive cycling, which is a novel and attracting idea. However, the work as presented lacks\ncritical validation, experimental detail, and architectural clarity to support the strength of its claims.\nThe main issues are:\n- The integration of AI into battery control is more conceptual than demonstrable at this point\n- The limited dataset and battery format restricts the generalizability of the conclusions\n- The overemphasis on solid-state lithium metal batteries is misaligned with the core innovation, which is platform-level\n- The system engineering aspects, particularly the AI–hardware interface, are under-described and difficult to reproduce\nwhich in reviewers’ opinion weakens the case for publication in Nature Communications on its current form.", "reviewer_3": "This work presents a novel approach leveraging AI and reinforcement learning to both estimate the state of solid-state\nlithium metal batteries and to adjust their cycling protocols to delay accelerated degradation and extend energy throughput.\nThis work appears to represent a considerable contribution to the space of battery diagnostics and operation, however,\npresentation issues mean that the work should not be published in its current state. Specifically, the language and figures\nmust be improved prior to publication.\nThe following comments are presented in no particular order:\nThe readability of the article is considerably degraded by the clarity of the language and grammar. There are many tools and\nresources available to refine the language. I encourage the authors to leverage them.\nFor the title, I would suggest instead, \"Real-time AI for solid-state lithium metal batteries.\" The current title seems to indicate\nthat the batteries are AI powered in real time, which doesn't make a lot of sense.\nIn the first line of the abstract, \"battery cycling lifetime\" should be replaced by \"battery degradation\" or \"battery health\nevolution\"\nFigure 1 is difficult to interpret. Especially the battery images on the top left of the figure that seem to indicate the battery state\nof health dramatically improving and then degrading in time, a characteristic not seen in real cells.\nFigure 2 indicates that the normalized capacity in the transitional state ranges between 0.9 and 0.5 SOH. However, in\nindustrial settings, 0.8 is typically selected as the threshold for end of life. Why is this transitional period defined to cross\nsuch a dramatic change in SOH? Intuitively, once the battery exhibits roll-over, you are no longer in a transitional period, you\nare deep into decay.\nThe interpretation of Fig 4a is unclear. For example, upwards of cycle 2000, it appears that the state perception is 1 across a\nlarge number of action indices simultaneously. I suggest that the figure be explained in greater detail, or another approach is\nused to represent the concept.\nIn all figures and in the text, Crate should be changed to C-rate\nFigure 4 plots the \"Euclidean distance\" for the intelligent and benchmark case, but it is unclear what this distance is\nmeasured to and from.\nIt is unsurprising that the AI input features are scattered in input space for the intelligence case versus the benchmark due to\nthe constantly changing charging protocols. This does not necessarily indicate anything about the state of health as these\nchanges in cycling protocol are not deconvolved. I am not convinced of the value of this PCA analysis.\nThe analogy between the battery and neural networks is suspect. I suggest removing it as it does not help explain the\nphenomena nor is it well supported by evidence.\nShould the data and code not be made publicly and easily accessible, the authors should make a reasonable\njustification. Not making these accessible significantly decreases the impact on the community and the chance of reproducing these\nresults."}, "author_response": "II. RESPONSE TO REVIEWERS.\nReviewer #1:\nIn this manuscript, the authors employs real-time AI-driven state perception and reinforcement learning\napproaches to analyze the critical role of intelligent charging strategies in suppressing dendrite growth and\ndelaying the \"knee point\" phenomenon of solid-state lithium metal batteries. Furthermore, the effectiveness of\nthe strategy and the evolution path of battery states are validated through SEM/EDS characterization.\n1. Line 24: Suggest changing \"to decarbonization\" to \"for decarbonization.\"\n2. Line 34: Suggest changing \"In Li-ion battery it can...\" to \"In Li-ion battery, it can...\"\nResponse: Thank you for your suggestion. We have revised the manuscript accordingly and corrected the\nrelevant descriptions in Lines 24 and 34 as recommended.\n3. Line 86: The different solid-state batteries in the dataset should be explained. Different electrode and\nelectrolyte materials often exhibit distinct electrochemical degradation pathways. How can the universality of\nthe network trained on different battery systems be demonstrated?\nResponse: Thank you for your insightful comment. We would like to clarify that although the dataset was\ncollected from different group members, all the solid-state cells used in this study were based on a consistent\nbattery chemistry system—namely, NMC811 cathodes paired with lithium metal anodes. The primary\nvariation among the cells lies in the electrolyte formulations, which were intentionally preserved to capture\nrealistic diversity in degradation behaviors within the same electrochemical system.\nImportantly, we did not tailor the model to any specific failure mechanism or degradation mode, thereby\npreserving its ability to generalize. The proposed data processing and network training were originally\ndeveloped in our previous machine learning studies, where it was successfully applied to commercial Li-ion\nbattery datasets [36], and Na-ion battery datasets [Ref. R1]. This consistency in methodology further supports\nthe model’ s universality across different battery systems.\nWe acknowledge that different cathode/anode materials would require retraining or transfer learning with data\nfrom the respective systems. Actually, the current results demonstrate promising robustness and adaptability\nof the model within the lithium-metal battery family. We have added a clarifying note in the revised manuscript\nto prevent any confusion regarding the battery systems used.\n[36] Chen, Xi, Jeesoon Choi, and Xin Li. \"Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle\nApplications.\" ACS Energy Letters 7.12 (2022): 4362-4367.\n[R1] Xi Chen, Luhan Ye, Yichao Wang, Xin Li†, “Beyond expert-level performance prediction for rechargeable batteries by\nunsupervised machine learning” Advanced Intelligent Systems, 1, 1900102 (2019)\n“This consistent cathode/anode configuration ensures a focused evaluation of the model's ability to generalize\nacross different degradation patterns induced by electrolyte variations.”\n“Despite the diversity in data sources, all solid-state cells share the same electrochemical configuration that\nNMC811 cathodes paired with lithium metal anodes. These electrolyte differences among the cells introduce\nmeasurable variations in degradation behavior, which can evaluate the neural network’s generalization\nperformance across different degradation pathways. Importantly, the model was not trained to target any\nspecific failure mechanism, allowing it to capture broader degradation dynamics without overfitting to a\nparticular pathway.”\n4. Line 128: The cycling results of the 7 benchmark batteries mentioned, specifically the cycling condition of\n18C-4.25V, should be evaluated for representativeness. It is generally assumed that lower rates lead to longer\nlifespan. When the benchmark group is cycled at a lower rate, does the intelligent cycling strategy still maintain\nits advantage? Suggest supplementing experiments.\nResponse: Thank you for this important question. While it is true that lower C-rates can extend battery lifespan\nin many liquid electrolyte batteries, recent studies have shown that such assumptions do not universally apply\nto solid-state lithium-metal batteries. For instance, as demonstrated in our previous work [5], low C-rates such\nas 1.5 C can result in inferior cycling stability compared to higher C-rates like 15 C and 20 C, and 15 C shows\nworse cycling stability than 20 C.\nIn this work, the benchmark cycling rate of 18C was intentionally selected as a representative midpoint within\nthe high C-rate regime from 15 C to 20 C. This choice allows us to meaningfully quantify and compare the\nperformance of intelligent cycling, which dynamically explore a range of rates both above and below 18C.\nMeanwhile, among the 7 benchmark batteries, three were cycled at 20C, and their stability was found to be\ncomparable to those at 18C (see Figure3c and Supplementary Figure S4). The action of more frequent attempts\ntoward 15 C itself after 500 cycles in our intelligent cycling thus will not lead to better cycling stability, because\nit should have moved toward the opposite C-rate direction of 20 C instead for better cycling stability, as 15 C\nshows worse cycling stability than 20 C. Therefore, the complete avoid of the decay state (state #2) by our\nintelligent cycling has to be the result of the combination of low and high C-rates in consecutive cycles. This\nalso further supports the representativeness and robustness of our benchmark selection across a range of high\nC-rates.\nWe therefore believe that the current experimental setup sufficiently supports the proof-of-concept nature of\nthis study, especially given the unique behavior of solid-state lithium-metal batteries under high-rate conditions.\n[5] Ye, Luhan, and Xin Li. \"A dynamic stability design strategy for lithium metal solid state batteries.\" Nature 593.7858 (2021): 218-\n222.\n“The benchmark conditions were fixed from within the regime of charging actions, which 18C-4.25V was\nselected as a representative midpoint. This allows a meaningful and quantifiable comparison with the\nintelligent cycling, which dynamically explores C-rates and voltage cutoff both above and below this\nbenchmark. Also, three of the benchmark cells were cycled at 20C, and their performance was found to be\ncomparable to those cycled at 18C. This reinforces the representativeness and robustness of the chosen 18C\ncondition, and highlights that higher C-rates do not necessarily lead to inferior cycling performance in solid-\nstate lithium-metal batteries, which is consistent with our earlier findings [5].”\n5. Please explain the supporting role of the analysis in Fig. 4b, c, e, and f for the main contributions of this\narticle. The PCA analysis demonstrates differences between intelligent cycling and benchmark testing in state\nspace, but the analysis remains superficial and does not explain the specific physical meaning of the principal\ncomponents or their relationship to battery performance. Suggest providing a detailed explanation.\nResponse: Thank you for this thoughtful comment. We appreciate the opportunity to clarify the physical and\nanalytical roles of the PCA-based analysis.\nFirst, the shape-based features used in this work were derived from cycling profiles (voltage and capacity over\ntime), and their relevance has been demonstrated in our prior works [36, R1], where shape images were shown\nto encode information-rich electrochemical characteristics.\nIn our current study, PCA was applied to these shape images to distill dominant degradation patterns. Extended\nData Fig. 4 illustrates the reconstructed contributions of PC1, PC2, and PC3, revealing that:\n• PC1 captures global capacity fading trends by horizontal compression of voltage plateaus; PC2 and PC3\nare linked to localized fluctuations and distortions in voltage curves, which may be related to internal\nresistance changes or interfacial instability.\nBased on these interpretations, we analyze how intelligent cycling and benchmark traverse the PCA-defined\nstate space:\n• Fig. 4b shows that intelligent cycling induces broader and more divergent motion in the PC1-PC2 space\nunder charging actions, implying greater dynamical exploration of battery states through jumping\nbetween states to access the PC space that otherwise will not be accessible at all in a normal cycling,\nwhich significantly slows down the evolution (and thus degradation) speed of the state trajectory in the\nPCA space for the intelligent cycling. Intelligent cycling leverages the dynamics to effectively enable\nrecovery from transitional states back into stable states by dynamically varying rates and voltage ranges,\nthough jumping out of the routine battery evolution pathway in the PCA space of a normal cycling with\nfixed rate and voltage range. Also, Fig. 4c quantifies this effect by showing larger Euclidean distances\nthat signal jumping and reversible state evolution pathways.\nIn contrast, Fig. 4e and 4f show that benchmark cycling follows a narrow and unidirectional trajectory in PCA\nspace of strong path dependence and minimal Euclidean movement, consistent with irreversible degradation\nand the early emergence of capacity knees (Fig. 4d)\nWe have revised the main text and Methods section accordingly to enhance these explanations.\n[36] Chen, Xi, Jeesoon Choi, and Xin Li. \"Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle\nApplications.\" ACS Energy Letters 7.12 (2022): 4362-4367.\n[R1] Xi Chen, Luhan Ye, Yichao Wang, Xin Li†, “Beyond expert-level performance prediction for rechargeable batteries by\nunsupervised machine learning” Advanced Intelligent Systems, 1, 1900102 (2019)\n“Specifically, PC1 reflects the global capacity fading trend (noted by horizontal shrinking of voltage plateaus),\nwhile PC2 and PC3 are linked to local fluctuations and distortions, likely representing internal resistance\nchanges or interfacial instability.”\n“This suggests the intelligent cycling of dynamic strategy enables a broader and more flexible exploration of\nelectrochemical states, allowing the battery to jump between states of little path dependence and access the\nprincipal component space that otherwise will not be accessible in a normal cycling of fixed strategy and\nunidirectional trajectory evolution in the principal component space of strong path dependence. Euclidean\ndistances calculated between adjacent cycles in the PCA space quantify the state transitions induced by each\naction step (Fig. 4c). For intelligent cycling, these distances reveal more frequent and larger shifts between\nconsecutive steps, reflecting rapid and reversible transitions between stable and transitional states, which\neffectively elongates the trajectory of the state evolution in the PCA space and slows down the degradation. In\ncomparison, benchmark cycling exhibits narrow, unidirectional state trajectory (Fig. 4e, Supplementary Fig. 6\nand animations) and minimal Euclidean displacement (Fig. 4f), corresponding to irreversible fast degradation\nand early emergence of knee points. The limited state variability under fixed charging actions restricts the\nability to recover once degradation accelerates.”\n6. In Fig. 4 h, it is evident that the capacity retention of the 6 cut-off group is higher than that of intelligent\ncycling. Please provide an explanation.\nResponse: Thank you for pointing this out. While it is true that the 6-cutoff group in Fig. 4h exhibits slightly\nhigher capacity retention than the intelligent cycling group at 1500 cycles, we would like to clarify it.\nThis phenomenon aligns with our main conclusion that increasing the number of available charging actions\nyields diminishing returns in improving battery longevity beyond a certain level. As shown in Fig. 4h and\nExtended Data Fig. 6, the correlation between action number and performance is non-linear, and beyond a\ncertain threshold, additional actions may not further improve, even slightly reduce performance. The 6-cutoff\ngroup may fall into such a marginally optimal point. Meanwhile, insights from our 2D grid simulations\n(Extended Data Fig. 5) support this interpretation. In the model, we define a fluctuation ratio of interaction\nfield to represent the extent of the perturbation introduced by varying charging actions. This nonlinear trend\nprovides a conceptual explanation for why a group with limited but well-distributed actions like 6 cutoff group\nmight perform well within a specific range.\nOn the other hand, it is important to emphasize that knee points exhibit abrupt capacity change, which is not\ndetermined by the current SOH. The intelligent cycling framework aims to proactively detect and avoid these\ntransitions through real-time state perception. Therefore, a slightly higher capacity retention of 6 cut-off group\nat 1500 cycles does not guarantee longer lifetime.\nWe have added additional clarification in the revised manuscript to better reflect these insights.\n“The 6 cutoff group shows slightly higher capacity retention at 1500 cycles compared to intelligent cycling.\nThis outcome likely reflects the diminishing returns effect when increasing the number of available charging\nactions beyond a certain level. As also illustrated in the 2D grid model (Extended Data Fig. 5), a moderate\nlevel of fluctuation may strike a balance between the extent and distribution of local degradations, leading to\ntemporarily favorable outcomes. The 6-cutoff group may happen to lie near such a marginal optimum.\nHowever, given the abrupt nature of capacity knee points, short-term advantages in capacity retention do not\nnecessarily imply superior long-term performance. Intelligent cycling is designed to dynamically adapt to\nevolving battery states, aiming to delay the onset of irreversible degradation.”\n7. Line 172: The evaluation of the impact of adding charging actions to manually programmed battery test\nprocedures should compare the degradation of the two groups under the same rate and cut-off voltage\nfrequency as in intelligent cycling. Suggest supplementing this comparison.\nResponse: We appreciate the reviewer’s valuable comment and suggestion. Regarding the frequency of rate\nand cut-off voltage, in our study, both the intelligent cycling and random testing (ED Fig. 6h) groups were\noperated under the same frequency of switching the charging actions every three cycles, where intelligent\ncycling shows obviously better capacity retention than the random cycling using the same ranges of C-rates\nand voltage cutoffs as the intelligent cycling (Fig. 4h).\nWe fully agree that ideally it will be great if we can run long-cycle tests with random or other programmed\nchanging actions every 3 cycles for the conditions in ED Fig. 6a-e as well. However, due to practical\nconstraints including the long duration required for these cycling tests, limited channels of the testing\ninstrument, and high instrument occupancy from other long-term battery studies, these tests could not be\nperformed.\nThat said, we hope that our explanation and analysis of the existing data could lift the concerns from the\nreviewer. ED Fig. 6a-e are at the same frequency of changing actions every 50 cycles, while they show very\ndifferent capacity retention, suggesting that frequency is not the most dominant factor here. Instead, there is a\ngeneral trend that the group of 6 actions (6 cutoffs or 6 rates) shows better retention than 4 actions ( 2 cutoffs\nand 2 rates), which is better than the group of 2 actions (2 cutoffs or 2 rates), suggesting that increasing the\nnumber of different actions is more important to better retention when the number of actions is smaller than 6.\nIn contrast, further increasing the action number to 18 with the random switch every 3 cycles reduced the\ncapacity retention, suggesting the importance of intelligent cycling when more actions are included.\nWe would like to emphasize that Figure 3ab presents statistically meaningful results across multiple batteries,\nconfirming the effectiveness of intelligent cycling, without the need to manually test and program the number\nand frequency of actions to include. The action umber dependence in Fig. 4h also helps probe finer aspects of\nthe charging strategies, and the observed diminishing returns support a deeper mechanistic understanding that\nis consistent with our modeling work (ED Fig. 5).\nWe have clarified these points in the revised manuscript to avoid any potential confusion and highlight the\nexperimental rationale.\n“Comparing those tests with fewer actions of 2 or 4(ED. Fig. 6a-c) and with more actions of 6 (ED. Fig. 6d-e)\nwith the action switch of every 50 cycles, it shows increasing the number of actions helps the retention before\nthe effect of action number converges at 6 actions. All the 18 charging actions were executed in the random\naction test (ED Fig. 6h) and were updated every 3 cycles, matching the interval and actions used in intelligent\ncycling. The lower capacity retention in the random action test than the intelligent one and the 6-actions one\nsuggests the importance of intelligent cycling when more actions (e.g. 18) are included, without the need to\ntest and program the optimum number of actions and frequency to switch. Action number dependence (Fig.\n4h) also offers insight into the underlying mechanisms of increasing charging complexity (ED. Fig. 5).”\n8. Line 448: \"dacays\" should be corrected to \"decays\".\nResponse: Thank you for the careful reviewing. We have corrected this typo.\nReviewer #2:\nThis work presents a closed-loop battery testing platform incorporating AI for real-time adaptive cycling. The\nauthors propose a system in which a deep learning–based state perception module and a reinforcement\nlearning (RL) agent interact with a commercial battery tester to optimize cycling conditions (battery C-rate\nand voltage cutoff) based on recent cell behavior. The approach is demonstrated on solid-state lithium metal\nbatteries (SS-LMBs), and the results show significant improvement in battery longevity and accumulated\nenergy throughput compared to fixed-strategy testing benchmarks.\nThe work’ s main conceptual innovation integrating AI models into the experimental loop of battery cycling—\nis timely and highly relevant. It aligns well with emerging trends in intelligent BMSs, where ML could extend\ncycle life and reduce safety risks, and potentially uncover hidden, incubating aging mechanisms to prevent\nfailure or avoid knee point. This conceptual framework is a promising direction and represents a necessary\nevolution beyond static, model-based or heuristic cycling strategies.\nStill, in reviewers view, the work as presented does not fully attain the proposed goals and objectives, and\nseveral critical issues limit its impact, reproducibility, and generalizability. While the paper introduces\ninteresting elements, it falls short in several areas, which shall be commented on below:\nResponse: We sincerely thank the reviewer for the thorough and constructive feedback. We are delighted that\nthe reviewer recognizes the value and potential of this research direction. The insightful comments have been\ninstrumental in improving the clarity and quality of our manuscript to better align with Nature Communications\nstandards. We have carefully addressed each concern and revised the manuscript accordingly.\n1. System architecture, battery testing infrastructure and reproducibility\nA key concern relates to the implementation of the AI–hardware integration, which is insufficiently described\ngiven that the manuscript aims to present a hardware-software control platform.\nThe authors state that adaptive cycling was conducted using a Solartron XM MTS system, while fixed\nbenchmark testing was performed on an Arbin BT2000. The Solartron, however, is a commercial battery\ntesting machine, but not a standard platform for high-throughput or standard programmable BMS-level testing.\nIt remains unclear:\nResponse: We thank the reviewer for pointing out this concern. In fact, no proprietary software modifications\nto the battery testing hardware were required. The integration of AI with the battery tester was realized by us\nentirely from the workstation side by leveraging standard communication protocols and application\nprogramming interfaces (APIs).\nThe Solartron XM MTS system used in this study is an electrochemical battery test station available in our lab,\nand it served the purpose of validating real-time communication and control with the AI agent. While it is not\na high-throughput testing system, it served well for our proof-of-concept demonstration. Importantly, we have\nalso consulted with commercial vendors such as Arbin and confirmed that their modern programmable BMS-\nlevel testing platforms also support integration with external control agents via open APIs. Unfortunately, the\nArbin BT200 system in our lab is too old to support this functionality.\nIn light of the reviewer’s comment, we have revised the manuscript to better clarify this point and emphasize\nthat the proposed platform is readily transferable and scalable across other compatible battery testing systems.\n“The platform integrates a Python-based control on a computer workstation with the battery testing hardware\n(Solartron XM MTS) for real-time data exchange and parameter updates. Notably, this implementation does\nnot rely on proprietary software modifications to the hardware.”\n- A comprehensive description of the protocol(s) or software layer(s) used to enable real-time communication\nbetween the Python-based AI control and the Solartron system and Windows Operating System that controls\nthe Solartron\nResponse: We appreciate the reviewer’s request for a more comprehensive description of the real-time\ncommunication architecture.\nFor software layer, real-time control was implemented using Component Object Model (COM) automation\nAPIs officially provided by Solartron. COM is Windows-based technology for inter-process communication\nand dynamic object creation, enabling different software components to interact through well-defined\ninterfaces. Here we deployed a Python environment on Windows 10 Operating System and accessed COM\nobjects exposed by the Solartron APIs. These interfaces allow us to manage instrument connections and status\nevents, execute experiments, and retrieve real-time measurement data directly.\nPhysically, communication between the workstation and the Solartron XM MTS is achieved via an RJ-45\nEthernet interface operating over TCP/IP. The instrument is visible on the local network and can be accessed\nthrough its assigned IP address.\nThis architecture enables real-time communication: the AI agent monitors the cycling data streams, makes\ndecisions on the next charging action, and updates control commands accordingly. The streamline does not\nrequire proprietary software modification.\nWe have revised the Methods section to describe this architecture in more detail and to highlight the\ngeneralizability of this approach to other programmable battery testing systems with open API support.\n“The control system was implemented using Component Object Model (COM) automation APIs, which\nexpose programmable interfaces for accessing and managing hardware operations, including experiment\nexecution, instrument status monitoring, and data retrieval. An established Python environment running was\nused to interact with these COM interfaces, enabling direct control from the workstation. Physically, the\nworkstation is connected to the Solartron system through a standard RJ-45 Ethernet interface over TCP/IP.\nThe instrument is visible on the local network and can be accessed through its assigned IP address.”\n- Whether the feedback control was synchronous or batch-based, and how latency and reliability were\nevaluated and handled\nResponse: We thank the reviewer for this important question. Real-time experimental data are continuously\nstreamed to the AI agent, which makes decisions based on the most recent 3-cycle data. The update frequency\nof the executed charging action is therefore every 3 cycles, forming the intelligent cycling framework.\nData acquisition is conducted via the same communication interface used by Solartron’s commercial software,\nwithout introducing any additional layers or delay. The stability and availability of the interface are evidenced\nby its use in conventional cycling tests. On the other hand, each set of 3 cycles typically takes on the order of\n10 minutes, while the AI agent's inference and command dispatch takes less than one second. This large\ndifference in timescales ensures that the latency introduced by the control system is negligible.\nThe uninterrupted operation of the system and its stable performance demonstrate that latency and reliability\nare not limiting factors. We have added the relevant description to make this point clearer.\n“The latency introduced by the closed loop is negligible relative to the cycling timescale. Reliability is also\nensured by leveraging the same standard interface used in routine experiments, with no observable disruption\nor delay during long-term operation.”\n- Is the AI module running on a separate workstation and received the Solartron data and then sends updates\nthe control parameters? How?\nResponse: We thank the reviewer for this question. As previously detailed, the AI module is deployed on the\nWindows-based workstation that does not rely on the Solartron commercial control software. Our control\nsystem bypasses the vendor software entirely by using a Python-based environment that communicates directly\nwith the hardware via COM automation APIs over a standard RJ-45 Ethernet connection using TCP/IP.\nThis design enables real-time bidirectional communication: experimental data is streamed directly from the\ninstrument to the AI module, which then updates control parameters based on its decisions without the\ninvolvement of any proprietary vendor software. It ensures seamless integration with minimal latency, and\noffers a path toward future deployment in edge-intelligent battery management systems.\n- How the system could be generalized to commercially relevant test stations (e.g., BioLogic, Maccor, Neware)\nor implemented in edge computing\nResponse: We appreciate the reviewer’s insightful question. Traditionally, users rely on vendor-provided\ncontrol software when operating commercial battery test stations, which often imposes constraints on control\nlogic and automation.\nWe have actively communicated with several major vendors, including Arbin, BioLogic, and Neware, and\nconfirmed that their modern systems support open communication protocols and API access. In fact, vendors\nhave indicated that enhanced API accessibility is becoming a key competitive feature in the development of\ntheir instruments. Therefore, as long as the battery tester offers open communication interfaces, our framework\ncan be readily deployed on either conventional computer workstations or embedded edge-computing devices.\nWe have added a statement in the revised manuscript to highlight this point.\n“For broader applicability, commercial vendors generally confirmed that modern programmable battery testers\nsupport external integration via open interfaces. Therefore, the proposed platform is broadly transferable across\ndifferent hardware systems.”\nIn addition, without a detailed system diagram (Fig. 1 is too general) or communication/control workflow, the\nplatform's portability and reproducibility cannot be assessed. If the authors intend to present a novel AI-\npowered battery control system, this aspect must be central in the manuscript. Currently, the AI agent appears\nmore like an external script that interfaces with a test station, rather than an integrated, deployable BMS\narchitecture.\nResponse: We thank the reviewer for this important comment. We fully agree that the communication/control\nworkflow is essential to assess the platform’s portability and reproducibility. The original Fig. 1 was intended\nto serve as a high-level conceptual overview for general readability.\nTo address this concern, we have added a new Fig. 1b that explicitly illustrates the communication, data\nexchange, and control workflow between the AI agent and the battery testing hardware. Fig. 1b provide\nimplementation-level clarity to complements the original schematic.\nWe sincerely appreciate this feedback and believe that the improved illustration strengthens the manuscript’s\ncontribution in terms of platform's portability and reproducibility.\nBesides, our current work primarily serves as a proof-of-concept to demonstrate the feasibility and\neffectiveness of real-time AI integration into battery testing/controlling platforms. While it is not a full-featured\ndeployable BMS at this stage, the proposed framework validates a scalable control mechanism that we envision\nbeing embedded into future edge-computing-based BMS hardware.\nWe have revised the manuscript text accordingly to clarify this positioning and avoid potential\nmisinterpretation as a commercial-grade BMS product.\n“As shown in the detailed workflow (Fig. 1b), the AI workstation directly communicates with the tester\nhardware via COM APIs over TCP/IP connection. Every 3-cycle data is employed to evaluate the battery state\nand adaptively control the charging strategy. This implementation supports real-time closed-loop control\nwithout relying on any proprietary vendor software.”\nFig. 1 | Real-time AI for intelligent cycling system of batteries. a, Schematic of the integrated platform. A\ntrained state perception module is implemented based on the real-time cycling data from every 3 cycles, serving\nas an intelligent in-situ monitor to identify the state of the operating battery, including stable, transitional, and\ndecay states. Reinforcement learning (RL) further enables intelligent cycling by adaptively optimizing fast\ncharging strategies for solid-state batteries across cycles based on the battery state. An electrochemical test\nstation is used to cycle actual batteries controlled by the above AI workstation. b, Engineering-oriented\nworkflow showing the real-time communication, data and control loop. The AI workstation enables\nbidirectional communication with the tester hardware. Every 3-cycle data is continuously collected to evaluate\nthe battery state, which is then used to adaptively control the charging strategy for the next segment. The goal\nof the current work is to demonstrate the effective longevity enhancement by intelligent cycling.\n2. Data sufficiency and model generalizability\nThe developed AI models rely on a dataset of just only 28 solid-state cells, collected from historical lab\nexperiments, to train the ResNet50-based state classifier. While the authors attempt to validate the classifier\non independent cells, this dataset is extremely limited in terms of size, diversity, and representativeness—\nespecially for training deep learning models and RL agents.\nResponse: We appreciate the reviewer’s comment on dataset size and generalizability. While the dataset is\nbuilt from 28 solid-state cells, we would like to clarify that in our state perception module, the unit of training\ndata is not the battery itself but the 3-cycle segmental data from each cell that can often run from ~ 1,000 to ~\n10,000 cycles. Each such segment produces a structured shape image input, and thus a total of 83,422 training\nsamples were used. This dataset size is sufficient for the training and convergence of the current AI model, as\nsupported by the validation and test performance.\nMoreover, the long lifespan and fast-charging capability of our solid-state batteries enable efficient generation\nof large volumes of available data. This is a key motivation behind choosing this battery chemistry system for\nproof-of-concept demonstration. The 28 cells varied in electrolytes and cycling parameters, which intentionally\ncapture realistic diversity in degradation behaviors within a controlled system.\nThese steps will be meaningful for moving from a working prototype toward a general-purpose intelligent\nbattery management system. In future work, it can be planned to expand the dataset to include more battery\nsystems to improve generalizability and robustness.\nThe relevant revision has been made in the updated manuscript to clarify the data structure used for model\ntraining.\n“To construct the training samples, the battery itself is not treated as the data unit; instead, the model ingests\nabundant 3-cycle segmental data from each cell.”\n“A total of 83,422 such samples were used for training.”\nIt is also unclear:\n- How the dataset was split (train/validation/test)\nResponse: We thank the reviewer for raising this point regarding data splitting. In our study, we adopted a\n16:6:6 split ratio for training, validation, and test sets, corresponding roughly to 60%, 20%, and 20% of the\nfull 28 batteries. Importantly, the split was performed at the cell level, data from each battery only appears in\none subset, thereby preventing data leakage and ensuring a fair evaluation to previously unseen cells. This\nsplitting strategy adheres to commonly accepted practice.\nThe corresponding clarification has been added in the revised manuscript.\n“The overall dataset was split into training, validation, and test sets at a 16:6:6 ratio, corresponding\napproximately to 60%, 20%, and 20% of the full 28 batteries.”\n“Each battery's data was included exclusively in one subset to prevent data leakage and ensure generalization\nto unseen batteries.”\n- Whether cells from similar configurations were grouped across splits, risking data leakage\nResponse: We appreciate the reviewer’s concern regarding potential data leakage. To avoid such risks, our\ngroup deliberately masked the detailed configuration information of each cell during dataset preparation. The\nresearchers conducting this study were thus blinded to the historical configuration specifics.\nA corresponding clarification has been added to the revised manuscript.\n“The detailed configurations of the batteries were masked during dataset preparation.”\n- If any data augmentation or uncertainty quantification was applied to improve robustness\nResponse: We appreciate the reviewer’s insightful question. In this study, we did not apply explicit data\naugmentation techniques. However, the data originated from real-world battery cycling experiments, which\ninherently include substantial variability such as measurement noise, voltage fluctuations, and differences in\ndegradation rates. This natural diversity contributes to the robustness of the model. As noted in the manuscript,\n“with the augmentation of battery dataset in the future, more complex large models can also be employed to\nachieve generalization effects.”\nThe current ResNet50-based state classifier generates deterministic predictions. Although uncertainty\nquantification was not incorporated in this version, we acknowledge it is worth trying in the future for battery\nstate estimation. In principle, methods such as Bayesian neural networks or ensemble learning are compatible\nwith out method and can be employed to enable reliable uncertainty estimation and improve model\ninterpretability.\nFurthermore, all data appears to originate from lab made LMBs, which to this stage suffer from high variability\nand low reproducibility (as shown in Ext. Fig. 1). The use of such limited and variable data raises substantial\nquestions about model generalizability, especially across different chemistries or battery formats.\nResponse: We appreciate the reviewer’s professional comment. We choose solid-state batteries as we think\nits property is very suitable to a fast and reliable proof-of-concept demonstration of real-time AI research on\nbatteries.\nWhile Extended Data Fig. 1 shows certain degrees of variability, such variability serves as a valuable source\nof perturbations for enhancing model robustness. Our primary motivation for selecting solid-state LMBs lies\nin their long-term application potential and their ability to support high-rate charging, which significantly\nshortens the time required to generate large-scale cycling data. It’s an important advantage for developing and\nevaluating AI-driven methods. Furthermore, all these batteries show similar three states of stable, transitional,\nand decay, as shown in Fig. 2.\nTo complete this study, we conducted multiple rounds of experiments with long-term cycling and statistical\nanalysis, which required substantial time and experimental effort. Without solid-state LMBs, it would be\ndifficult to manage the time cost associated with exploring real-time AI concept with the resource from a\nuniversity lab.\nActually, we do not claim that the developed model is directly applicable to all battery chemistries or systems,\nalthough we believe the general framework and methodology are transferrable to other battery systems. Our\nstudy validates the feasibility of state perception and intelligent cycling for LMBs. Particularly, the knee point\nphenomenon is common across multiple battery systems, and their degradation pathways share certain\nuniversal patterns. Thus, we believe that the conceptual framework and AI workflow can be extended to\nbroader battery systems in future work.\nWhat remains strange to the reviewer is that, had the authors used commercial, off-the-shelf lithium-ion\nbatteries—which are more reproducible and come in diverse chemistries—they could have better demonstrated\nthe robustness and wide applicability of the AI platform. And saving time wouldn’t be an issue, either (i.e.,\ncycling at high temperatures, high rates, plating, etc.). This makes the case that solid-state LMBs were not\nnecessary for this demonstration, and in fact, their use may have constrained both the model’s performance\nand its relevance and add too much information not needed to present the platform.\nResponse: We thank the reviewer for this thoughtful comment. While commercial lithium-ion batteries offer\nhigh reproducibility and chemical diversity, their use in this specific study would not necessarily reduce the\nexperimental time as suggested. In fact, our solid-state lithium-metal batteries support ultrafast cycling C-rates\nup to 20C [5], allowing a full lifetime test (from pristine state to below 80% SOH) within in a significantly\nshorter timeframe. Moreover, this approach preserves a large number of usable cycling data, making it\nparticularly valuable for real-time AI control to extend battery lifespan\nDue to safety constraints and performance limitations, commercial lithium-ion batteries typically cannot\nsupport such high-rate operation with sufficient cycles. Consequently, AI-driven experiments would require\nmonths or even years of continuous cycling, especially once the AI control begins to extend the cell’s life. It\nis not practical for concept validation and model iteration.\nTherefore, the choice of solid-state LMBs reflects a designed trade-off between experimental tractability and\nconceptual ambition. It not only facilitates timely and safe experimentation but also supports the feasibility\ndemonstration of real-time AI control.\nWhile we agree that commercial batteries represent an important direction, especially for broader deployment,\nsuch an extension is beyond the scope of this work and is planned for future studies. Specifically, the group\nhas a long-term collaboration with LG Energy Solutions [36], and thus we understand that to work on\ncommercial batteries, it would be a better idea to directly collaborate with battery industry, such as LG, to\nobtain big data from them for training, and implement the AI modulation approach to their BMS system.\nHowever, such work may be limited by certain regulations to publish.\nA corresponding clarification has been added in the revised manuscript.\n[5] Ye, Luhan, and Xin Li. \"A dynamic stability design strategy for lithium metal solid state batteries.\" Nature 593.7858 (2021): 218-\n222.\n[36] Chen, Xi, Jeesoon Choi, and Xin Li. \"Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle\nApplications.\" ACS Energy Letters 7.12 (2022): 4362-4367.\n“As a proof-of-concept demonstration, we utilize the extremely fast charging capability of our solid-state\nbatteries to 20 C-rate at low cathode loading [5], to greatly accelerate the data generation and reduce the\nfeedback time in our development. This enabled rapid lifetime cycling and efficient model iteration. While\ncommercial Li-ion batteries offer better reproducibility, their limited rate capability would substantially\nprolong the experimental timeline, especially under potential AI-extended lifetimes. However, implementing\nour approach to commercial Li-ion battery and BMS system will be an obvious next step through the\ncollaboration with the battery industry.”\n3. Title and scope\nThe manuscript title emphasizes “real-time AI-powered solid-state lithium metal batteries”, which is\nmisleading. The innovation lies not in the materials science of solid-state batteries, but in the AI-based control\nmethodology. Also, if the AI logic isn’t embedded but is a software agent controlling a lab instrument\nexternally, then calling this “real-time AI-powered” control requires qualification. It's more of a scripted\nclosed-loop AI-supervised platform.\nSimilarly, the manuscript devotes extensive space to microstructural evolution, SEM/EDS imaging, and even\nmodeling of interfacial degradation using a 2D grid simulation. While informative, these analyses appear\nsecondary to the main contribution. The platform, which should be central, is lightly described, lacking\narchitectural detail, block-level diagrams, or algorithm–hardware flowcharts. This mixing of material\ncharacterization with high-level systems and AI objectives creates conceptual fragmentation, detracting from\nthe manuscript’s clarity and weakening the scientific message. If the core novelty is the intelligent cycling\nframework, then the focus must shift more decisively towards the architecture description, the\nexperimental integration of control and validation under diverse scenarios and the comparison with other heuristic and\nconventional adaptive methods.\nResponse: We sincerely thank the reviewer for the thoughtful feedback. We regret that our original\npresentation may have conveyed a sense of conceptual ambiguity. We have revised the manuscript title and\nclarified related expressions throughout the text. The updated title is “Real-time AI for Solid-State Lithium\nMetal Batteries”\nRegarding the phrase “real-time AI-powered”, we agree that clarification is warranted. Although our AI\nmodule is not embedded within the battery hardware, it operates in tightly synchronized communication with\nthe experimental instruments with minimal latency. The real-time interaction is essential for the system to\nperceive and control during cycling. We have added a new workflow diagram (Fig. 1b) illustrating the\ncommunication, data, and control loops. Also, we have revised the main text to more prominently highlight\nthe design and realization of the overall platform.\nWe acknowledge that materials characterization and interfacial modeling are described specifically, but they\nare not intended as parallel contributions. Other reviewers also expressed interest in and asked more questions\nabout the materials characterization presented in our work, which aligns with our original intent. While\nintelligent cycling is the central contribution, we believe that the scientific validity of its outcomes must be\ngrounded in physical interpretation. Otherwise, the real-time AI control would risk becoming a “black box\noptimization”.\nWhen AI proves effective in scientific research, it is often because it captures or leverages underlying physical\nmechanisms that conventional strategies fail to address. As such, we believe it is our responsibility to\ninvestigate and discuss those mechanisms underpinning the AI actions. The materials analyses in our work are\ntherefore not auxiliary, but essential for providing interpretability and theoretical grounding. We consider this\ncoupling between AI-driven charging strategies and material-level insight to be a key scientific contribution\nof this study.\nIn total, this manuscript aims to introduce a novel methodology that combines data-driven state classification\nwith reinforcement learning for adaptive cycling, which is a novel and attracting idea. However, the work as\npresented lacks critical validation, experimental detail, and architectural clarity to support the strength of its\nclaims.\nResponse: We thank the reviewer for the overall assessment and constructive criticisms. We have carefully\naddressed each specific comment raised above. For the main issues summarized below, we would also like to\nprovide the following point-by-point response.\nThe main issues are:\n- The integration of AI into battery control is more conceptual than demonstrable at this point\nResponse: We are sorry for the misunderstanding caused by our previous version of the manuscript. In this\nproof-of-concept study, we choose solid-state LMBs as a validation platform to demonstrate the real-time AI\npotential over complex systems. The experiments are already conducted as a full-process, closed-loop control,\nrather than just simulations or isolated module tests. This represents an important step toward adaptive, AI-\ndriven battery management. While further engineering efforts are certainly needed, this does not diminish the\nscientific innovation of the current work.\nAccording to the reviewer’s professional suggestions, we have revised the manuscript to more clearly present\nthe positioning of this work with experimental grounding and forward-looking value.\n- The limited dataset and battery format restricts the generalizability of the conclusions\nResponse: We appreciate the reviewer’s comment on dataset size and generalizability. As clarified in a\nprevious response, while the dataset originates from 28 batteries, the training unit is not full battery lifetimes\nbut every 3-cycle data, each converted into structured shape images. This yielded 83,422 samples, which are\nsufficient for model training through validation and test performance.\nMoreover, the long lifespan and ultrafast charging capability (up to 20C) of the selected solid-state batteries\nenabled efficient generation of cycling data. This was a key motivation for choosing this battery system as a\ntesting ground for our proof-of-concept study.\nWe emphasize that the current work aims to demonstrate the feasibility of real-time AI-based control in\ncomplex battery systems, rather than claiming universal generalization across all chemistries or commercial\nformats. That said, the knee point behavior and degradation patterns observed are common across battery\nchemistries, and the overall framework can be extended in future studies to other battery formats, including\nthe collaboration with battery industry for commercial batteries. We have revised the manuscript to clarify this\nissue.\n- The overemphasis on solid-state lithium metal batteries is misaligned with the core innovation, which is\nplatform-level\nResponse: We thank the reviewer for this important observation. The solid-state lithium metal batteries were\nselected as a challenging but efficient platform due to its ultrafast cycling capability, which significantly\nreduces experimental time and enables rapid model iteration.\nWhile we acknowledge that solid-state battery chemistry is not the focus of this study, material-level\ncharacterization was included to provide physical interpretation and avoid black-box optimization. These\nanalyses support scientific soundness of the AI decisions. We have revised the manuscript to clarify this\ndistinction.\n- The system engineering aspects, particularly the AI-hardware interface, are under-described and difficult to\nreproduce\nResponse: We appreciate the reviewer’s concern. As clarified in our previous responses, the AI-hardware\ninterface is fully implemented using standard communication protocols available on modern programmable\nbattery testers, enabling real-time data and control loops with minimal latency. Although the AI workstation\nis not embedded, it operates in a closed-loop manner with the instrument and can be reproduced using\ncommercially accessible equipment and open APIs. A workflow diagram (Fig. 1b) and these implementation\ndetails have been added in the revised manuscript to improve reproducibility and transparency of the system\ndesign.\nwhich in reviewers’ opinion weakens the case for publication in Nature Communications on its current form.\nResponse: We acknowledge the reviewer’s assessment and have carefully addressed each of the concerns in\nour revision. We hope that the revised manuscript is substantially strengthened and more aligned with the\njournal’s expectations.\n(Remarks on code availability):\nThe computer code generated and used during this study would be available from the corresponding author on\nreasonable request.\nResponse: We thank the reviewer for pointing this out. In fact, we had uploaded the source code in the public\nrepository in our initial submission to the journal: https://github.com/wmy1994/Real-time-AI-powered-solid-\nstate-batteries\nNotably, we would like to clarify that the full closed-loop operation requires integration with specific battery\ntesting instruments via their communication interfaces. Therefore, while the core code is available, executing\nthe complete system workflow requires compatible hardware.\nReviewer #3:\nThis work presents a novel approach leveraging AI and reinforcement learning to both estimate the state of\nsolid-state lithium metal batteries and to adjust their cycling protocols to delay accelerated degradation and\nextend energy throughput. This work appears to represent a considerable contribution to the space of battery\ndiagnostics and operation, however, presentation issues mean that the work should not be published in its\ncurrent state. Specifically, the language and figures must be improved prior to publication.\nResponse: We thank the reviewer for the positive assessment of the technical contribution. We fully\nacknowledge the concern regarding language clarity and figure quality, and we have undertaken a thorough\nrevision of the manuscript to address these comments.\nThe following comments are presented in no particular order:\nThe readability of the article is considerably degraded by the clarity of the language and grammar. There are\nmany tools and resources available to refine the language. I encourage the authors to leverage them.\nResponse: We thank the reviewer for the helpful suggestion. We have carefully revised the manuscript and\nimproved the language throughout to enhance clarity and readability.\nFor the title, I would suggest instead, \"Real-time AI for solid-state lithium metal batteries.\" The current title\nseems to indicate that the batteries are AI powered in real time, which doesn't make a lot of sense.\nResponse: Based on your suggestion, we have revised the title to: “Real-time AI for solid-state lithium metal\nbatteries”.\nIn the first line of the abstract, \"battery cycling lifetime\" should be replaced by \"battery degradation\" or \"battery\nhealth evolution\"\nResponse: Thank you for the helpful suggestion. We have revised the corresponding description in the abstract\nto improve clarity.\nFigure 1 is difficult to interpret. Especially the battery images on the top left of the figure that seem to indicate\nthe battery state of health dramatically improving and then degrading in time, a characteristic not seen in real\ncells.\nResponse: We appreciate the reviewer’s helpful observation. In response, we have revised the top left of the\nfigure to reflect a gradual SOH decline, and added textual SOH labels to clarify the misunderstanding.\nAdditionally, we have included a new figure (Fig. 1b) to illustrate the workflow of communication, data,\nand control loop of our AI platform, thereby improving the technical clarity of the system architecture.\nFig. 1 | Real-time AI for intelligent cycling system of batteries. a, Schematic of the integrated platform. A\ntrained state perception module is implemented based on the real-time cycling data from every 3 cycles, serving\nas an intelligent in-situ monitor to identify the state of the operating battery, including stable, transitional, and\ndecay states. Reinforcement learning (RL) further enables intelligent cycling by adaptively optimizing fast\ncharging strategies for solid-state batteries across cycles based on the battery state. An electrochemical test\nstation is used to cycle actual batteries controlled by the above AI workstation. b, Engineering-oriented\nworkflow showing the real-time communication, data and control loop. The AI workstation enables\nbidirectional communication with the tester hardware. Every 3-cycle data is continuously collected to evaluate\nthe battery state, which is then used to adaptively control the charging strategy for the next segment. The goal\nof the current work is to demonstrate the effective longevity enhancement by intelligent cycling.\nFigure 2 indicates that the normalized capacity in the transitional state ranges between 0.9 and 0.5 SOH.\nHowever, in industrial settings, 0.8 is typically selected as the threshold for end of life. Why is this transitional\nperiod defined to cross such a dramatic change in SOH? Intuitively, once the battery exhibits roll-over, you\nare no longer in a transitional period, you are deep into decay.\nResponse: We appreciate the reviewer’s insightful comment. From a conventional battery engineering\nstandpoint, an SOH of 0.8 is commonly used in industry as a threshold to mark end-of-life. While in this study,\nthe definition of the “state segmentation” is designed to capture and dissect the knee point and its surrounding\nevolution.\nFollowing the onset of the knee point, the battery typically undergoes a rapid degradation. This sharp change\nin capacity marks a transitional behavior, which we aim to characterize in detail. We view this region as a\ntemporary regime where internal failure and side reactions are actively progressing but have not yet stabilized.\nOnly after these sharp changes subside does the battery enter the final “decay state”, characterized by slower\ndegradation. Therefore, our segmentation is not based solely on SOH values, but rather on the first and second\nderivatives of capacity evolution, reflecting changes in the underlying physical mechanisms.\nWe have added clarifying text in the revised manuscript to avoid potential confusion.\n“While 0.8 SOH is commonly used in industrial settings as the end-of-life (EOL) threshold, the definition of\nstate segmentation is to isolate and examine the rapid degradation behavior around the knee point. The\ntransitional state represents a temporary yet critical period where internal failures and side reactions accelerate\nsharply but have not yet stabilized. That means a battery in the transitional state can still go back to the stable\nstate through intelligent cycling (e.g., Fig. 2d, 2e). Only after this sharp degradation phase does the battery\nenter the final decay state, characterized by a slower rate of capacity loss.”\nThe interpretation of Fig 4a is unclear. For example, upwards of cycle 2000, it appears that the state perception\nis 1 across a large number of action indices simultaneously. I suggest that the figure be explained in greater\ndetail, or another approach is used to represent the concept.\nResponse: We appreciate the reviewer’s valuable observation and suggestion. In Fig. 4a, the \"action index\"\nrefers to different choices of charging actions. The plotted state perception output reflects the battery state\nunder the specific action applied during cycling (0 = stable, 1 = transitional, 2 = decay).\nIntelligent cycling significantly prolongs battery lifetime, extending the stable state well beyond what can be\nachievable under benchmark testing. However, the battery still eventually reaches a limit where degradation\nbecomes unavoidable. Beyond approximately cycle 2000, the state perception output becomes state 1 across\nalmost all action indices, indicating that the battery has thoroughly entered the transitional state regardless of\nwhich action is applied. In other words, many of the explored charging actions cannot maintain the battery in\na stable state anymore, reflecting a transition into irreversible degradation. It marks the limit of intelligent\ncycling's ability to recover the battery to a stable state.\nWe have clarified this point in the main text to avoid any potential misunderstanding.\n“As cycling progresses, the battery inevitably approaches a point where degradation becomes irreversible.\nAround cycle 2000, the state perception output shifts to 1 across most action indices, indicating that the\navailable charging actions to preserve the battery in a stable state are limited. This simultaneous onset of\ntransitional states across large number of actions signals the transition into irreversible degradation. This\nbehavior highlights the ultimate constraint of the electrochemical system, reflecting the limit of intelligent\ncycling's ability to recover the battery to a stable state.”\nIn all figures and in the text, Crate should be changed to C-rate\nResponse: We thank the reviewer for the careful attention to detail. We have corrected all instances in the\nfigures and text.\nFigure 4 plots the \"Euclidean distance\" for the intelligent and benchmark case, but it is unclear what this\ndistance is measured to and from.\nResponse: Thank you for raising this important point. Here, Euclidean distance represents the distance\nbetween two consecutive data points in the PCA space, where each point corresponds to a step composed of\nthree consecutive cycles. This distance quantifies the magnitude of change in the battery’s state from one step\nto the next.\nAs also suggested by Reviewer #1, we have revised the manuscript to explicitly clarify this definition.\n“Euclidean distances calculated between adjacent cycles in the PCA space quantify the state transitions\ninduced by each action step composed of three consecutive cycles.”\nIt is unsurprising that the AI input features are scattered in input space for the intelligence case versus the\nbenchmark due to the constantly changing charging protocols. This does not necessarily indicate anything\nabout the state of health as these changes in cycling protocol are not deconvolved. I am not convinced of the\nvalue of this PCA analysis.\nResponse: We thank the reviewer for raising this professional concern. We agree that the scatter observed in\nPCA space under the intelligent cycling scenario is partly influenced by the variability of charging protocols,\nand thus does not directly indicate battery state-of-health (SOH). However, that was not the main point we\nwanted to argue.\nIn our work, the purpose of PCA is to track the evolution of state-related features as influenced by the applied\ncharging actions. PCA processing was performed on shape images derived from charging-discharging profiles.\nThese shape features have been previously validated to contain rich information corelated with battery\ndegradation [36]. Therefore, while PCA does not explicitly deconvolve the effects of varying charging\nprotocols, it provides a low-dimensional visualization of how the battery state presentations evolve under\nintelligent cycling, which is hidden in shape images.\nThe observation that intelligent cycling induces a more exploratory and reversible trajectory in the PCA space\npotentially reflects how the changing charging protocols modulates the battery’s accessible state pathways. In\ncontrast, the benchmark exhibits a narrow, monotonic progression, corresponding to irreversible degradation.\nWhile the influence of varying protocols is a valid confounding factor, the trajectories in PCA space are not\ninterpreted in isolation. The consistency of PCA-based observations with other independent characterizations\nstrengthens the value of this PCA analysis. Namely, SEM characterizations and 2D grid simulations both reveal\nsimilar patterns of state divergence, which correlate with the PCA-based depiction. These cross-validated\ninsights suggest that the PCA analysis offers indirect but meaningful support for understanding how intelligent\ncycling interacts with battery degradation dynamics.\nIn response to the reviewer’s comment, we have revised the manuscript to explicitly acknowledge the potential\nconfounding effects of charging protocol variations and to better frame this PCA analysis as an auxiliary,\nvisualization-driven tool, rather than a standalone indicator of SOH.\n[36] Chen, Xi, Jeesoon Choi, and Xin Li. \"Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle\nApplications.\" ACS Energy Letters 7.12 (2022): 4362-4367.\n“The variability of charging actions in intelligent cycling inevitably introduces some confounding effects into\nthe PCA results. Therefore, the PCA analysis should not be regarded as a direct indicator of state-of-health,\nbut rather as a visualization tool to trace the evolution of battery state features.”\n“This suggests the intelligent cycling of dynamic strategy enables a broader and more flexible exploration of\nelectrochemical states, allowing the battery to jump between states of little path dependence and access the\nprincipal component space that otherwise will not be accessible in a normal cycling of fixed strategy and\nunidirectional trajectory evolution in the principal component space of strong path dependence. Euclidean\ndistances calculated between adjacent cycles in the PCA space quantify the state transitions induced by each\naction step (Fig. 4c). For intelligent cycling, these distances reveal more frequent and larger shifts between\nconsecutive steps, reflecting rapid and reversible transitions between stable and transitional states, which\neffectively elongates the trajectory of the state evolution in the PCA space and slows down the degradation. In\ncomparison, benchmark cycling exhibits narrow, unidirectional state trajectory (Fig. 4e, Supplementary Fig. 6\nand animations) and minimal Euclidean displacement (Fig. 4f), corresponding to irreversible fast degradation\nand early emergence of knee points. The limited state variability under fixed charging actions restricts the\nability to recover once degradation accelerates.”\nThe analogy between the battery and neural networks is suspect. I suggest removing it as it does not help\nexplain the phenomena nor is it well supported by evidence.\nResponse: We agree with the reviewer’s suggestion. We have removed the analogy between the battery and\nneural networks from the manuscript. This revision does not affect the core methodology or conclusions of the\nwork.\nShould the data and code not be made publicly and easily accessible, the authors should make a reasonable\njustification. Not making these accessible significantly decreases the impact on the community and the chance\nof reproducing these results.\nResponse: Thank you for your suggestion. The source code has been made publicly available on GitHub in\nour initial submission.\nRegarding the dataset, we regret that it cannot be released publicly at this stage due to institutional restrictions\nassociated with ongoing research in solid-state batteries and the related commercialization activity. However,\nwe welcome collaboration and data-sharing inquiries. Access can be granted to qualified researchers upon\nreasonable request to the corresponding author.\n(Remarks on code availability):\ncode was not available\nResponse: We thank the reviewer for pointing this out. We had uploaded the source code in the public\nrepository in our initial submission a few months ago in fact at: https://github.com/wmy1994/Real-time-AI-\npowered-solid-state-batteries\nNotably, we would like to clarify that the full closed-loop operation requires integration with specific battery\ntesting instruments via their communication interfaces. Therefore, while the core code is available, executing\nthe complete system workflow requires compatible hardware.\nRESPONSE TO REVIEWERS.\nReviewer #3:\n(Remarks to the Author):\nI thank the authors for properly addressing most of my presentation and technical concerns.\nI would encourage the authors to improve the grammar throughout the article. For example, the first line of\nthe introduction should be \"The Li-ion battery is one of the most powerful technologies in our era for\ndecarbonization and combating climate change\" instead of \"Li-ion battery is one of the most powerful\ntechnologies in our era for decarbonization and combat climate change.\" Please either leverage a native English\nspeaker at one of your institutions, or even ask AI to review the document and provide suggestions on\ngrammatical changes with the reasoning for these changes.\nResponse: We thank the reviewer for highlighting the need for improved grammar and for providing specific\nexamples. In response, the text is revised for grammatical accuracy and clarity.\n(Remarks on code availability):\nI did not run and install the code, as this would require the requisite dataset and hardware. The necessary code\nappears to be present with comments. The codebase would be of much greater value to the research community\nif it came with a detailed demonstration notebook with associated datasets and instrument/battery simulators.\nResponse: We thank the reviewer for this valuable suggestion. Actually, the released codebase already\nincludes a virtual instrument interface, which can be directly executed via Exp_Pyctrl_solartron/ Exp_ctrl.py\nscript to launch a simulated communication environment. We have updated the repository README to\nexplicitly describe this functionality at https://github.com/wmy1994/Real-time-AI-for-solid-state-batteries.\nHowever, it will require the future readers to download our code, modify and connect to their own hardware,\nincluding their own running batteries and dataset, to run our AI system.\nRegarding the dataset that we used for training, we regret that it cannot be released publicly due to various\nrestrictions. However, we welcome collaboration and data-sharing inquiries, and access can be granted to\nqualified researchers upon reasonable request to the corresponding author.\nThese clarifications have been added to the “Code availability” and “Data availability” sections of the\nrevised manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made necessary revisions.", "reviewer_2": "Dear authors,\nI have carefully reviewed the revised version of the manuscript and I would like to thank the authors for the substantial\nimprovements made in response to the reviewers' comments.\nThe revised manuscript addresses the concerns raised during the review process in a thorough and satisfactory manner.\nThe quality of the presentation, clarity of the arguments, and scientific rigor have all been significantly enhanced.\nThank you,", "reviewer_3": "I thank the authors for properly addressing most of my presentation and technical concerns.\nI would encourage the authors to improve the grammar throughout the article. For example, the first line of the introduction\nshould be \"The Li-ion battery is one of the most powerful technologies in our era for decarbonization and combating climate\nchange\" instead of \"Li-ion battery is one of the most powerful technologies in our era for decarbonization and combat climate\nchange.\" Please either leverage a native English speaker at one of your institutions, or even ask AI to review the document\nand provide suggestions on grammatical changes with the reasoning for these changes."}, "author_response": ""}]

Decision letter