jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexey Tsyganov | Olga Yu. Grapenko | Е. V. Korotaev | Alexander A. Shindrov | Andrei A. Alferov | Alexander Gorokhovsky | Nikolay Gorshkov
Source: Nanomaterials (article, 2025-10-19)
Field: Materials Science — Materials Chemistry

Pseudocapacitive Behavior of Blade-Coated Mo1.33CTx i-MXene Electrodes in Aqueous Electrolytes Two-dimensional molybdenum carbide (Mo<sub>1.33</sub>CT<sub>x</sub> MXene) with ordered vacancies is one of the most promising materials for electrochemical energy storage. However, the high defectivity and tendency to aggregate of nanosheets hinders the large-scale fabrication of highly efficient Mo<sub>1.33</sub>CT<sub>x</sub> -based electrodes. In this study, Mo<sub>1.33</sub>CT<sub>x</sub>/carbon nanotubes (CNTs) electrodes of varying thicknesses were fabricated using a scalable doctor blade technique. Their electrochemical performance was studied in H<sub>2</sub>SO<sub>4</sub>, H<sub>3</sub>PO<sub>4</sub>, LiCl and KCl electrolytes using cyclic voltammetry and galvanostatic charge-discharge methods. Electrodes with an active material mass loading of 1.6 mg/cm<sup>2</sup> exhibited specific capacitances of 352, 287, 172, and 107 F/g in H<sub>2</sub>SO<sub>4</sub>, H<sub>3</sub>PO<sub>4</sub>, LiCl, and KCl electrolytes, respectively, at a scan rate of 2 mV/s. Increasing the mass loading of the electrode material to 3.5 mg/cm<sup>2</sup> resulted in a specific capacitance of 349, 260, 162 and 98 F/g in the same electrolytes. The incorporation of CNTs enabled rapid electrolyte ion transport throughout the electrode bulk, maintaining high capacitance values even at high scan rates. These results open new avenues for the development of high-performance electrode materials for supercapacitors. Electrode ; Electrolyte ; Cyclic voltammetry ; Capacitance ; Electrochemistry ; Horizontal scan rate ; Fabrication MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advanced Memory and Neural Computing
Authors: María Esteve-Rochina | Jennifer Faro-Guara | Enrique Ortı́ | Aron Walsh | Joaquín Calbo
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Origins of electrical conductivity in 3D iron-tetrazole-based metal–organic frameworks First-principles calculations reveal protonic ordering and mixed valence enhance charge transport in Fe 2 (BDT) 3 MOF polymorphs via distinct conduction pathways. Tetrazole ; Materials science ; Metal-organic framework ; Electrical resistivity and conductivity ; Metal ; Conductivity ; Chemical engineering ; Inorganic chemistry ; Metallurgy ; Physical chemistry ; Organic chemistry ; Adsorption ; Electrical engineering Metal-Organic Frameworks: Synthesis and Applications ; Advanced Memory and Neural Computing ; Conducting polymers and applications
Authors: Hannes Gustafsson | Fabian Schwarz | Thijs Smolders | Senja Barthel | Amber Mace
Source: ChemRxiv (preprint, 2025-06-04)
Field: Materials Science — Materials Chemistry

Computationally Efficient DFT-based Sampling of Ion Diffusion in Crystalline Solids We present a method for large scale DFT-based screening of ion diffusion in crystalline solids. This is accomplished by extending the Ionic TuTraSt method to sample the Potential Energy Surface (PES) using single-point DFT calculations. To drastically reduce the number of single-point DFT calculations, symmetry, interpolation and the exclusion of high-energy regions are employed. The method, together with the strategies to reduce the number of DFT calculations are tested on a large dataset. This allows to optimize the interpolation and high-energy exclusion with respect to balancing computational efficiency and accuracy of the diffusion properties. Furthermore, Ionic TuTraSt is validated by comparison with ab initio molecular dynamics (AIMD) simulations on a set of known Li-ion superconducting materials. Diffusion ; Ion ; Sampling (signal processing) ; Materials science ; Chemical physics ; Statistical physics ; Computational chemistry ; Computer science ; Chemistry ; Thermodynamics ; Physics ; Organic chemistry ; Telecommunications X-ray Diffraction in Crystallography
Authors: Jakub Pawelko | Éric Furet | Gwenael Duplaix-Rata | N. Perrin | Xavier Rocquefelte
Source: Journal of Chemical Theory and Computation (article, 2025-05-27)
Field: Materials Science — Ceramics and Composites

Multistep Approach for Simulating Raman Spectra of Amorphous Materials: The Case of Li<sub>3</sub>PS<sub>4</sub> Glass Electrolyte Glasses are widely used for their various applications, which arise from their inherent lack of long-range ordering. This characteristic makes it challenging to describe their atomic properties. To facilitate and accelerate glass research, computational simulations, such as molecular dynamics or Monte Carlo simulations, are commonly employed to model the structure of these amorphous materials. However, verifying and later refining the models require comparing these simulations with spectroscopic data, which can be quite computationally challenging due to the number of atoms needed in the unit cell to account for the disorder. In this paper, we propose a multistep approach for simulating specifically Raman spectra that accounts for long-range interactions in amorphous materials. This approach, going from molecular dynamics to embedded cluster calculations, allows us to define the Raman signature of each structural unit and reconstruct the Raman spectrum of the glass electrolyte Li<sub>3</sub>PS<sub>4</sub>, achieving low computational cost and high agreement with existing spectroscopic experimental data. Raman spectroscopy ; Amorphous solid ; Electrolyte ; Materials science ; Spectral line ; Chemical engineering ; Analytical Chemistry (journal) ; Chemistry ; Physical chemistry ; Physics ; Crystallography ; Organic chemistry ; Optics ; Engineering ; Electrode Glass properties and applications ; Advanced Battery Materials and Technologies ; Phase-change materials and chalcogenides
Authors: Fahimeh Ahmadi | Mohsen Hajihassani | Tryfon Sivenas | Stefanos Papanikolaou | Panagiotis G. Asteris
Source: Technologies (article, 2025-05-25)
Field: Materials Science — Ceramics and Composites

Advanced Machine Learning Methods for the Prediction of the Optical Parameters of Tellurite Glasses This study evaluates the predictive performance of advanced machine learning models, including DeepBoost, XGBoost, CatBoost, RF, and MLP, in estimating the Ω2, Ω4, and Ω6 parameters based on a comprehensive set of input variables. Among the models, DeepBoost consistently demonstrated the best performance across the training and testing phases. For the Ω2 prediction, DeepBoost achieved an R2 of 0.974 and accuracy of 99.895% in the training phase, with corresponding values of 0.971 and 99.902% in the testing phase. In comparison, XGBoost ranked second with an R2 of 0.929 and accuracy of 99.870% during testing. For Ω4, DeepBoost achieved a training phase R2 of 0.955 and accuracy of 99.846%, while the testing phase results included an R2 of 0.945 and accuracy of 99.951%. Similar trends were observed for Ω6, where DeepBoost obtained near-perfect training phase results (R2 = 0.997, accuracy = 99.968%) and testing phase performance (R2 = 0.994, accuracy = 99.946%). These findings are further supported by violin plots and correlation analyses, underscoring DeepBoost’s superior predictive reliability and generalization capabilities. This work highlights the importance of model selection in predictive tasks and demonstrates the potential of machine learning for capturing complex relationships in data. Machine learning ; Generalization ; Artificial intelligence ; Predictive modelling ; Computer science ; Reliability (semiconductor) ; Training set ; Set (abstract data type) ; Phase (matter) ; Test set ; Model selection ; Mathematics Glass properties and applications ; Phase-change materials and chalcogenides ; Luminescence Properties of Advanced Materials
Authors: Muye Niu | Zuoshuai Xi | Chenhui He | Wenting Ding | Shanshan Cheng | Juntao Zhang | Hongyi Gao
Source: Molecules (article, 2025-04-28)
Field: Chemistry — Inorganic Chemistry

Investigating the Physical Adsorption of DCPD/Furfural and H2 Adsorption–Dissociation Behaviors in RE-MOFs Metal-organic frameworks (MOFs) have emerged as promising catalysts in the hydrogenation of bicyclopentadiene (DCPD) and furfural. The physical adsorption behaviors of substrate molecules and H<sub>2</sub> within the pore structures of MOFs significantly influence the efficacy of subsequent catalytic reactions. This study employs molecular dynamics (MD) simulations to identify the optimal temperature and pressure conditions for the adsorption of DCPD and H<sub>2</sub>, as well as furfural and H<sub>2</sub>, within rare-earth-element-based MOFs (RE-MOFs). By analyzing the physical adsorption characteristics of 1538 RE-MOFs, we investigate the correlation between pore structures and adsorption capabilities. This exploration has led to the identification of 10 RE-MOF structures that demonstrate superior physical adsorption performance for both DCPD and furfural. Following this initial evaluation, density functional theory (DFT) calculations were conducted to determine the chemisorption energies of H<sub>2</sub> molecules on these 10 selected RE-MOF structures. Notably, the structure identified as "JALLEQ_clean" exhibited the most optimal overall adsorption performance. This study elucidates the quantitative relationship between the pore structure of RE-MOFs and their physical adsorption performance, clarifying the influence of porosity parameters on adsorption capacity and highlighting the advantages of cluster-type structures in mass transfer and adsorption. The findings provide theoretical guidance for developing high-performance RE-MOF catalysts and offer new insights for the rational design of MOF-based catalytic materials. Adsorption ; Furfural ; Chemistry ; Dissociation (chemistry) ; Chemical engineering ; Organic chemistry ; Inorganic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Alessandro Landi | Francesco Ambrosio | Anna Leo | Daniele Padula | Giacomo Prampolini | Andrea Peluso
Source: ChemRxiv (preprint, 2025-04-16)
Field: Chemistry — Physical and Theoretical Chemistry

Effect of Thermal Disorder on the Electronic Structure and the Charge Mobility of Acenes The effect of thermal disorder on the electronic structure and transport properties of two prototypical organic semiconductors, naphthalene and pentacene has been studied by combining molecular dynamics simulations with ab initio electronic structure calculations. Our approach explicitly account for the influence of thermal disorder on the site energy and on the intermolecular interactions. It is shown that thermal disorder shifts the polaronic energy levels with respect to the perfectly ordered crystal and stabilize charge localization, even in the case of pentacene, with a significant contribution provided by local rearrangement of the first neighbours. Evaluation of charge mobilities, carried out by kinetic Monte Carlo simulations, shows that site energy fluctuations play the most significant role in slowing down charge diffusion, leading to computed mobilities which are in very good agreement with the experimental ones. Charge (physics) ; Thermal ; Condensed matter physics ; Materials science ; Chemical physics ; Physics ; Thermodynamics ; Quantum mechanics Various Chemistry Research Topics ; Advanced Chemical Sensor Technologies ; Chemical Thermodynamics and Molecular Structure
Authors: К. А. Терещенко | Rustem F. Ismagilov | Н. В. Улитин | Yana L. Lyulinskaya | Alexander S. Novikov
Source: Computation (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Effect of Monomer Mixture Composition on TiCl4-Al(i-C4H9)3 Catalytic System Activity in Butadiene–Isoprene Copolymerization: A Theoretical Study Divinylisoprene rubber, a copolymer of butadiene and isoprene, is used as raw material for rubber technical products, combining isoprene rubber’s elasticity and butadiene rubber’s wear resistance. These properties depend quantitatively on the copolymer composition, which depends on the kinetics of its synthesis. This work aims to theoretically describe how the monomer mixture composition in the butadiene–isoprene copolymerization affects the activity of the TiCl4-Al(i-C4H9)3 catalytic system (expressed by active sites concentration) via kinetic modeling. This enables development of a reliable kinetic model for divinylisoprene rubber synthesis, predicting reaction rate, molecular weight, and composition, applicable to reactor design and process intensification. Active sites concentrations were calculated from experimental copolymerization rates and known chain propagation constants for various monomer compositions. Kinetic equations for active sites formation were based on mass-action law and Langmuir monomolecular adsorption theory. An analytical equation relating active sites concentration to monomer composition was derived, analyzed, and optimized with experimental data. The results show that monomer composition’s influence on active sites concentration is well described by a two-step kinetic model (physical adsorption followed by Ti–C bond formation), accounting for competitive adsorption: isoprene adsorbs more readily, while butadiene forms more stable active sites. Isoprene ; Copolymer ; Monomer ; Catalysis ; Composition (language) ; Polymer chemistry ; Chemistry ; Organic chemistry ; Materials science ; Polymer ; Philosophy Thermal and Kinetic Analysis ; Carbon dioxide utilization in catalysis ; Catalysis and Oxidation Reactions
Authors: Qia Ke | N. Scott Bobbitt | Janardhan Rao Doppa | Huazheng Wang | Cory M. Simon
Source: ChemRxiv (article, 2025-08-30)
Field: Chemistry — Inorganic Chemistry

Adaptive allocation of Monte Carlo samples for efficient, multi-fidelity computational screening of metal-organic frameworks For applications in gas sensing, purification, and capture, we often wish to search a large set of metal-organic frameworks (MOFs) for the top-K in terms of their Henry coefficient of an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample consists of inserting an adsorbate in the MOF at a random position and orientation then calculating the MOF-adsorbate interaction energy. Our idea is to leverage top-K arm identification algorithms, developed for the multi-armed bandit problem in reinforcement learning, to sequentially and adaptively allocate adsorbate-insertions among the MOFs in a data-driven manner to obtain the most accurate top-K subset under a fixed insertion budget. By analogy, each MOF is a slot machine in a casino that, upon pulling its arm (inserting an adsorbate), offers a stochastic reward (a noisy estimate of its Henry coefficient) sampled from a static, unknown probability distribution. Each adaptive allocation algorithm (1) proceeds in a feedback loop of (i) allocate adsorbate-insertions to MOF(s), (ii) update the running estimates of the Henry coefficients of the MOF(s), then (iii) judiciously allocate adsorbate insertions to the next MOF(s); (2) sequentially dials-up the fidelities of ongoing molecular simulations in the MOFs, giving a multi-fidelity computational screening; (3) circumvents the need for hand-crafting structural or chemical features of the MOFs. As a case study, we implement, benchmark, and analyze the sequential halving, successive accepts and rejects, and narrowing exploration (our proposed heuristic) algorithms to adaptively allocate xenon insertions to screen a set of ca. 300 MOFs for the top-K Xe Henry coefficient subset over differing insertion budgets. Provided with a sufficient budget, we find that these adaptive insertion algorithms can significantly reduce (by a factor of two to three) the simple regret (sum of true minus empirical top-K true Henry coefficients) and error in the top-K subset of MOFs output by a computational screening. By another metric, adaptive insertion allocation provided a 35% discount on the computational cost to identify the top-K MOFs with less than 5% error. We thereby demonstrate that top-K arm identification algorithms may generally be useful for more efficiently screening materials for various properties via Monte Carlo molecular simulations and enabling the adoption of more sophisticated force fields or even ab initio calculations for the potential energy of configurations to lend higher-fidelity screenings. Monte Carlo method ; Fidelity ; Computer science ; High fidelity ; Statistical physics ; Physics ; Mathematics ; Statistics ; Telecommunications Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Matan Oliel | Yitzhak Mastai
Source: CrystEngComm (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Chiral selectivity in lithium niobate crystals: investigating the role of <scp>l</scp> - and <scp>d</scp> -alanine as chiral inducers LiNbO 3 is an achiral crystal that exhibits properties typically associated with chiral crystals such as nonlinear polarization and piezoelectricity. Interestingly, chiral induction of LiNbO 3 with alanine enantiomers also exhibits chiral selectivity. Lithium niobate ; Selectivity ; Alanine ; Lithium (medication) ; Inducer ; Materials science ; Chemistry ; Inorganic chemistry ; Stereochemistry ; Organic chemistry ; Optoelectronics ; Catalysis ; Amino acid ; Biochemistry Coordination Chemistry and Organometallics ; Solid-state spectroscopy and crystallography
Authors: Shigeru Yamaguchi
Source: ChemRxiv (preprint, 2025-05-26)
Field: Materials Science — Materials Chemistry

Computational Study on Data Integration in Chiral Catalyst De-sign: Case Study Using Michael Addition Reactions Data science in molecular catalysis often relies on the idea that molecules with similar structures show similar properties. This concept underlies many successful examples of regression analysis based on free energy relationships, typically using datasets in which substituents are varied on a single catalyst or substrate scaffold. Here, we present data integration for chiral catalyst design using MFA (molecular field analysis), with voxel descriptors derived from 718 TS (transition-state) struc-tures calculated through DFT methods, along with their corresponding computed ΔΔG‡ values (energy differences between TSs leading to each enantiomer). These datasets covered seven reaction types, including organocatalysis, transition-metal catalysis, and both Michael addition and Diels–Alder reactions. MFA using integrated datasets from seven pairs of distinct reaction systems enabled the design of chiral catalysts with improved computed enantioselectivity in all the seven catalytic systems. To facilitate applications, we have released a web platform (https://mcds.riken.jp) that offers an MFA-based design tool and a database of DFT-computed TS data. Sign (mathematics) ; Catalysis ; Michael reaction ; Computer science ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science
Authors: Shengzhao Wang | Jin-Fan Song | Bin Liu
Source: Colloids and Interfaces (article, 2025-06-19)
Field: Materials Science — Materials Chemistry

Study on the Optical Properties and Lattice Thermal Conductivity of Ti2AlB2 In this paper, the optical properties and lattice thermal conductivity of Ti2AlB2 were studied by first-principles calculations. The real part of the dielectric constant, ε1, attains a significant value of 47.26 at 0.12 eV, indicating strong polarization capabilities and energy storage capacity. Regarding optical properties, Ti2AlB2 exhibits significant absorption peaks at photon energies of 4.19 eV, 6.78 eV, and 10.61 eV, and 14.32 eV, with absorption coefficients of 184,168.1 cm−1, 228,860.8 cm−1, 366,350.8 and 303,440.6 cm−1, indicating a strong absorption capacity. The loss function exhibits peaks at 19.80 eV and the refractive index reaches a maximum of 8.30 at 0.01 eV. Reflectivity is notably higher in the 0–5 eV range, exceeding 44%, which demonstrates excellent reflective properties. This suggests that Ti2AlB2 has potential as an optical coating material across certain frequency bands. The lattice thermal conductivity of Ti2AlB2 is obtained at 27.2 W/(m·K). The phonon relaxation time is greater in the low-frequency region, suggesting that phonons have a longer duration of action during the heat transport process, which may contribute to higher thermal conductivity. Although the phonon group velocity is generally low, several factors influence thermal conductivity, including phonon relaxation time and Grüneisen parameters. The high Grüneisen parameter of Ti2AlB2 indicates strong anharmonic vibrations, which may enhance phonon scattering and consequently reduce thermal conductivity. However, Ti2AlB2 still exhibits some lattice thermal conductivity, suggesting that the contributions of phonon relaxation time and group velocity to its thermal conductivity may be more significant. The unique optical properties and thermal conductivity of Ti2AlB2 indicate its potential applications in optical coatings and high-temperature structural materials. Thermal conductivity ; Phonon ; Anharmonicity ; Materials science ; Dielectric ; Condensed matter physics ; Refractive index ; Scattering ; Relaxation (psychology) ; Optics ; Physics ; Optoelectronics MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; Thermal properties of materials
Authors: Rama El-khawaldeh | Connor R. Bourgonje | Bowen Wang | J. C. Scaiano
Source: Catalysts (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

A Scoring Model for Catalyst Informatics Based on Real-Time High-Throughput Fluorogenic Assay for Catalyst Discovery and Kinetic Profiling In this work, we propose an automated, real-time optical scanning approach to assessing catalyst performance in the process of nitro-to-amine reduction using well-plate readers to monitor reaction progress. This approach takes advantage of a simple on–off fluorescence probe that gives a shift in absorbance and strong fluorescent signal when the non-fluorescent nitro-moiety is reduced to the amine form. The combination of an affordable probe and a low barrier-to-entry technique provides an accessible approach to high-throughput catalyst screening. Under this paradigm, we screened 114 different catalysts and compared them in terms of reaction completion times, material abundance, price, recoverability, and safety. Using a simple scoring system, we plotted the catalysts in terms of cumulative scores, along with some intentional biases, including an emphasis on preference for catalysts with potential as green catalysts, considering environmental issues and possible geopolitical preferences. Profiling (computer programming) ; Catalysis ; Throughput ; Chemistry ; Computer science ; Biochemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science