jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ying Xu | Jintao Guo | Xiaoyan Liu | Chuan Ma | Xin Nie
Source:  (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Construction of a predictive model for cross scale graphene composite phase change materials based on machine learning and study on its heat transfer mechanism Graphene, as a high thermal conductivity enhancing filler, can significantly improve the thermal conductivity of phase change materials. The microscopic nature of its enhancement mechanism and cross scale prediction models can reveal the heat transfer laws of phase change. This article uses molecular dynamics simulation to regulate the doping content of graphene in alkane based composite phase change materials, and proposes and constructs a cross scale thermal conductivity prediction model with box constraint factor. The experiment shows that the prediction error of the model is within 5%. The simulation results show that there is a strong C-H···π specific interaction between the graphene interface and alkane molecules. The characteristic peak of the radial distribution function at 1.77 Å and the nonlinear change in mean square displacement indicate that the interface structure not only does not suppress the diffusion of liquid-phase molecules, but also fundamentally reduces the interface thermal resistance by constructing efficient phonon transmission channels. Based on the above simulation results, an innovative SCA-TCN deep learning prediction model was developed, with a determination coefficient R 2 of 0.99762, and the main error indicators were reduced by about 76% to 97% compared to the benchmark model. Scale (ratio) ; Composite number ; Mechanism (biology) ; Heat transfer ; Phase change ; Phase (matter) Machine Learning in Materials Science ; Thermal properties of materials ; Phase Change Materials Research
Authors: Junwei Che | Xuezhi Wang
Source: Computational Materials Science (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Revisiting phonon thermal transport in penta-graphene via a machine-learning potential-driven large-scale molecular dynamics simulation Phonon ; Anharmonicity ; Thermal conductivity ; Molecular dynamics ; Atmospheric temperature range ; Scaling ; Lattice (music) Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Nitesh Kumar | Jianwei Lai | Casey S. Mezerkor | Jiaqi Wang | Kamila M. Wiaderek | J. David Bazak | Samuel M. Blau | Ethan J. Crumlin
Source: arXiv (Cornell University) (preprint, 2026-03-20)
Field: Materials Science — Materials Chemistry

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse datasets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000 times faster than DFT. While previous MLIP training datasets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) dataset provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pre-trained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation-solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion-correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations. Solvation ; Electrolyte ; Molecular dynamics ; Ion ; Battery (electricity) ; Molecule ; Function (biology) ; Multiscale modeling Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Nitesh Kumar | Jianwei Lai | Casey S. Mezerkor | Jiaqi Wang | Kamila M. Wiaderek | J. David Bazak | Samuel M. Blau | Ethan J. Crumlin
Source: ArXiv.org (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse datasets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000 times faster than DFT. While previous MLIP training datasets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) dataset provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pre-trained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation-solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion-correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations. Solvation ; Electrolyte ; Molecular dynamics ; Ion ; Battery (electricity) ; Molecule ; Function (biology) ; Multiscale modeling Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Ying Xu | Jintao Guo | Gang Liu | Chuan Ma | Xin Nie
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Construction of a predictive model for cross scale graphene composite phase change materials based on machine learning and study on its heat transfer mechanism Thermal conductivity ; Graphene ; Heat transfer ; Phase (matter) ; Interface (matter) ; Scale (ratio) ; Mean squared error ; Diffusion ; Phonon ; Displacement (psychology) Thermal properties of materials ; Phase Change Materials Research ; Machine Learning in Materials Science
Authors: Tao Guo | Ying Liu | Xinrui Guan | Weiwei Zhang | Donghai Mei
Source: The Journal of Physical Chemistry C (article, 2026-03-27)
Field: Chemistry — Inorganic Chemistry

O <sub>2</sub> -Mediated Evolution of Pt Nanoclusters in Silicalite-1 Using Artificial Neural Network Potential-Based Molecular Dynamics Simulations The aggregation and structural evolution of platinum (Pt) nanoclusters within zeolites are strongly influenced by the gaseous reaction atmosphere, which critically affects their catalytic performance and stability. In this study, we develop a global neural network potential to simulate the dynamics of Pt and its oxides (PtOx) confined in silicalite-1, enabling efficient large-scale molecular dynamics simulations. The G-NN potential is validated against density functional theory calculations including adsorption energies and dissociation barriers. Using this framework, we systematically investigated the effects of Pt oxidation on cluster migration. With increasing oxidation, the interaction between Pt and the silicalite-1 framework weakens and migration energy barriers decrease, enhancing the mobility of oxidized species. Elevated temperatures further promote oxygen adsorption and dissociation on Pt surfaces, driving progressive oxidation and fragmentation into chain-like Pt–O structures. Radial distribution function analyses show a decrease in Pt–Pt coordination and a simultaneous increase in Pt–O coordination, consistent with experimental observations of PtO and PtO2 phases. These results provide atomic-level insight into how oxygen and temperature govern Pt cluster evolution within confined zeolite frameworks, offering a theoretical basis for designing highly active, sinter-resistant catalysts in the field of confined metal cluster zeolite systems. Nanoclusters ; Molecular dynamics ; Dissociation (chemistry) ; Cluster (spacecraft) ; Catalysis ; Zeolite ; Adsorption ; Density functional theory ; Platinum Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Kausik Roy
Source: ChemRxiv (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

ReactionForge: Temporal Graph Networks with Cross-Attention and Evidential Learning Surpass State-of-the-Art in Suzuki-Miyaura Yield Prediction Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we introduce ReactionForge, a novel Temporal Graph Network architecture specifically designed for Suzuki-Miyaura cross-coupling yield prediction that addresses these challenges through five key innovations. First, we implement persistent temporal memory mechanisms using Gated Recurrent Units to track catalyst evolution and reagent dynamics across reaction sequences. Second, we develop cross-attention layers that explicitly compare reactant and product molecular graphs, learning which structural changes most influence reaction outcomes. Third, we incorporate hierarchical graph pooling via Self-Attention Graph Pooling to automatically discover functional group patterns. Fourth, we employ evidential deep learning to provide calibrated epistemic and aleatoric uncertainty in a single forward pass. Fifth, we use multi-task learning with yield, selectivity, and reaction time as joint objectives to improve generalization. Evaluated on 5,760 Suzuki-Miyaura reactions spanning five metal catalysts and diverse substrates, ReactionForge achieves R² = 0.968 ± 0.004 (RMSE = 5.12 ± 0.18%, MAE = 3.89 ± 0.12%), representing statistically significant improvements over YieldGNN (R² = 0.957 ± 0.005, paired t-test p = 0.002) and YieldBERT (R² = 0.810 ± 0.010, p < 0.001). The model provides well-calibrated uncertainty estimates (Expected Calibration Error = 0.031) that enable uncertainty-guided active learning, achieving 37% improved data efficiency over random sampling. Systematic ablation studies reveal that each architectural component contributes measurably to performance, with cross-attention and temporal memory each adding approximately ΔR² = 0.005. Interpretability analysis shows that learned attention weights successfully recover known structure-reactivity relationships, including chloride activation challenges and ligand-substrate compatibility patterns. Despite architectural complexity, ReactionForge trains 28% faster than YieldGNN. This work demonstrates that chemically motivated architectural innovations in graph neural networks can meaningfully advance reaction prediction when properly grounded in mechanistic understanding. Interpretability ; Pooling ; Graph ; Deep learning ; Baseline (sea) ; Raw data ; Face (sociological concept) ; Product (mathematics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; CO2 Reduction Techniques and Catalysts
Authors: Ye Qing Li | Qinyang Zhao | Yi Xing | Hanlin Wang | Yuanlin Yan | Bosen Li | Qinfen Gu | Haitao Hu | Limin Zhou | Mingzhe Chen
Source: Energy storage materials (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Multiscale simulation for electrolyte design: From microstructure-property decoding to macroscopic performance prediction Solvation ; Electrolyte ; Density functional theory ; Battery (electricity) ; Molecular dynamics ; Multiscale modeling ; Ab initio ; Implicit solvation Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Kirill Kulaev | Bogdan Protsenko | W. P. Cheng | Q. L. Liu | Д. Н. Горбунов | Mikhail Lifar | Valery Vlasenko | А. С. Бурлов | Sergey Guda | Alexander Guda | Mikhail A. Soldatov | Alexander Soldatov
Source: The Journal of Physical Chemistry Letters (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

DeepFit: Physically and Chemically Informed XAS-Structure Fitting Made Simple X-ray absorption spectroscopy (XAS) reveals local structures in catalysts and materials under <i>operando</i> conditions but faces ill-posed challenges, with high computational costs and ambiguous solutions. DeepFit pioneers a universal deep learning remedy, deploying a differentiable minimization of both a spectral mismatch and quantum-chemical energy. The model is based on an E(3)-equivariant neural network trained on the original database of 67,000 theoretical K-edge spectra from diverse 3d/4d transition metal complexes. The approach ensures physically equivariant and chemically plausible refinement of 3D structure and is validated on the experimental case studies for homogeneous catalysts. The benchmark for the series of 3d compounds shows agreement of DeepFit refined bond lengths with respect to XRD/EXAFS (MAE 0.06 Å) and 0.73 ROC-AUC selectivity over local motifs. By merging machine learning spectra approximation and quantum chemical constraints, DeepFit transforms quantitative XAS analysis toward a black-box, routine tool. Artificial neural network ; Series (stratigraphy) ; Benchmark (surveying) ; Spectral line ; Minification ; Differentiable function ; Simple (philosophy) ; Homogeneous ; Equivariant map Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; CO2 Reduction Techniques and Catalysts
Authors: Shihong Yu | Peng Bi | Jingsong Liu | enzhu li | Yong Yi
Source: Solid State Ionics (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Machine learning molecular dynamics investigation on cation diffusion mechanisms in Ni0.2Cu0.3Zn0.5Fe2O4 ferrite and its homogeneous interfaces Diffusion ; Molecular dynamics ; Ferrite (magnet) ; Homogeneous ; Anisotropy ; Lattice diffusion coefficient ; Ion ; Anisotropic diffusion Magnetic Properties and Synthesis of Ferrites ; Metallic Glasses and Amorphous Alloys ; Chemical and Physical Properties of Materials
Authors: Ivo Neefjes | Jakub Kubecka | Jonas Elm
Source:  (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials with accurate long-range interactions for molecular dynamics collision simulations of atmospherically-relevant molecules Recent advances in machine learning interatomic potentials have enabled the simulation of cluster formation from precursor vapor at a high level of theory. However, performing these simulations requires verifying that the models accurately describe cluster formation dynamics, particularly collision processes. In this work, we study the performance of two distinct machine learning (ML) architectures, AIMNet2 and PaiNN, against GFN1-xTB and ωB97X-3c reference data for atmospherically relevant collision systems (H2SO4–H2SO4, H2SO4–HSO4-, and H2SO4–NH(CH3)2).We evaluate the models' ability to reproduce one-dimensional potentials of mean force (PMFs) and collision probabilities. Both models achieve excellent agreement with reference PMFs, yielding RMSEs at least an order of magnitude lower than chemical accuracy (1 kcal mol-1). Notably, PaiNN achieves lower errors in the binding region.However, we observe significant differences in collision probabilities. While AIMNet2 accurately reproduces these probabilities, PaiNN fails to capture long-range interactions beyond its local cutoff (10 Å). For the charged H2SO4–HSO4- system, this leads to a complete loss of collision probability beyond 14 Å and an underestimation at shorter distances.Our results demonstrate a clear trade-off: while PaiNN offers superior accuracy for equilibrium properties and binding energies, its local nature makes it unsuitable for collision kinetics in systems with strong long-range interactions. Conversely, AIMNet2's ability to model these long-range interactions makes it the necessary choice for simulating collisions in such systems. Collision ; Cluster (spacecraft) ; Molecular dynamics ; Cutoff ; Interatomic potential ; Collision detection Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Gaussian Processes and Bayesian Inference
Authors: Hiroto Yokoyama | Takahiro Umemoto | Akiko Kumada | Masahiro Sato
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Integration of Multiscale Simulations and AI Models for the Design of High-Performance Dielectric Polymers Polymers are widely used as electrical insulating materials in the power and energy sector. As data-driven approaches to materials development are attracting increasing attention, two major challenges remain: the limited availability of polymer data and the complex hierarchical structures of polymers. In this study, we conduct high-throughput quantum chemistry calculations and molecular dynamics (MD) simulations using a single force field for approximately 5,500 homopolymers to construct a dataset of computed polymer properties. We develop machine learning models to predict MD-calculated polymer properties using quantum chemistry-derived physical quantities and MD force field parameters as descriptors. We also compare their performance with models using structural descriptors. While interpolation performance is comparable between molecular structure–based models, including a fingerprint model and a graph convolutional neural network, and models using quantum chemistry and MD force-field descriptors, the latter shows superior extrapolation performance in our evaluated setting for MD-computed density. In addition, we confirm that increasing the amount of data improves the accuracy of both interpolation and extrapolation predictions. The polymer dataset and predictive approach proposed in this study may help inform computational screening toward the development of high-performance polymer materials; experimental validation remains necessary. Extrapolation ; Interpolation (computer graphics) ; Polymer ; Quantum ; Field (mathematics) ; Dielectric ; Molecular dynamics ; Force field (fiction) Machine Learning in Materials Science ; Dielectric materials and actuators ; Model Reduction and Neural Networks
Authors: Anis Attiaoui | Shunda Chen | J. C. Woicik | J. Zach Lentz | Liliane M. Vogl | Jarod Meyer | Kunal Mukherjee | Andrew Minor | Tianshu Li | Paul C. McIntyre
Source: arXiv (Cornell University) (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Shining light on short-range atomic ordering in semiconductors alloys The functional properties of semiconductors are typically controlled by tailoring their chemical composition and their state of strain, and by controlling their long-range structural order, including the presence of extended defects such as dislocations. In addition to these approaches, theoretical predictions suggest that short-range order (SRO) of atoms in group-IV semiconductor alloys can modify the bandgap, a defining property of any semiconductor. Herein, a new machine learning enabled, computation-guided methodology for extended X-ray absorption fine structure (EXAFS) analysis of SRO is used to quantify the effects of local atomic order on the bandgap of germanium-tin (GeSn) alloy single crystal nanostructures with well-controlled strain and composition. Correlative analysis of EXAFS and photoluminescence (PL) establishes the relationship between bandgap and the Warren-Cowley short-range order (WC-SRO) parameter of the GeSn alloys. It is further demonstrated that SRO can be tuned over a broad range by post-deposition annealing of the alloy crystals. This work establishes control of SRO as an important design parameter for semiconducting properties and suggests the potential for quantitative measurement and tuning of SRO in other semiconductor alloy systems. Semiconductor ; Alloy ; Photoluminescence ; Band gap ; Annealing (glass) ; Nanostructure ; Extended X-ray absorption fine structure ; Wide-bandgap semiconductor Machine Learning in Materials Science ; Photonic and Optical Devices ; Nanowire Synthesis and Applications
Authors: Yugandhar Kasala Sreenivasulu | Isshu Lee | John W. Merickel | Fei Xu | Yalei Tang | Joshua Rittenhouse | Aleksandar Vakanski | Rongjie Song
Source: Scientific Reports (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Machine learning-based correlation of charpy impact properties between sub-sized and standard-sized specimens for nuclear structural materials Reliable correlations of Charpy impact test results between sub-sized and full-sized specimens are essential for structural integrity assessments, particularly in nuclear applications, where spatial constraints and limited material volume restrict specimen size. Although standards such as ASTM A370 and BS 7910 provide guidance on conversion methodologies, and numerous analytical correlation methods have been proposed in prior studies, these approaches generally have limited accuracy and their applicability is often constrained to specific materials, treatment conditions, and specimen geometries. In this study, a Machine Learning (ML)-based framework is proposed for correlating Charpy impact properties across specimen sizes. The proposed approach maps absorbed energy values across the full ductile-to-brittle transition region by applying a temperature shift combined with scaled residual projection, to align sub-sized test data with full-sized response. From the resulting temperature-energy profiles, the correlated values for upper shelf energy (USE) and ductile-to-brittle transition temperature (DBTT) are extracted by fitting data with a hyperbolic tangent model. The framework is validated using a dataset comprising 389 matched sub-sized and full-sized Charpy impact tests on SA533B steel. This ML-based approach demonstrates an improved correlation performance relative to conventional analytical methods, achieving R² values of 0.942 for USE and 0.892 for DBTT. The trained ML models do not require access to full-sized Charpy data during inference, making this approach suitable for material surveillance programs, accelerated irradiation testing, and other applications involving small-size Charpy impact testing. Charpy impact test ; Structural integrity ; Correlation ; Materials testing ; Structural material High-Velocity Impact and Material Behavior ; Machine Learning in Materials Science ; Graphite, nuclear technology, radiation studies