jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Uriel Zagada Dominguez
Source: ChemRxiv (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Deep Learning Architectures for the Inverse Design of Covalent Organic Frameworks with Tailored Thermal Transport: A High-Throughput Materials Informatics Study The precise control of thermal conductivity (κ) in porous crystalline materials is a fundamental challenge for the next generation of thermoelectric and microelectronic cooling technologies. Covalent Organic Frameworks (COFs) offer a unique structural platform due to their modularity, yet the sheer dimensionality of their chemical space precludes exhaustive experimental or ab initio characterization. In this work, we present a robust Materials Informatics framework that integrates Neural Network Potentials (NNPs) with High-Throughput Screening (HTS) to explore the thermal landscape of 2,500+ COF topologies. Our model utilizes Behler-Parrinello symmetry functions to encode local atomic environments, achieving near-DFT accuracy (RMSE < 1.5 meV/atom) at a speedup factor of 105. We identify a novel class of ”thermal-insulating” COFs with κ < 0.05 W/mK, driven by synchronized phonon rattling in large-pore imine-linked networks. Conversely, we discover high-thermal conductivity candidates facilitated by stiffened azobenzene linkages. This study elucidates the scaling relations between pore geometry, linker anharmonicity, and lattice heat transport, providing a comprehensive library of thermal properties for the COF Materials Genome. Deep learning ; Materials informatics ; Inverse ; Informatics ; Thermal ; Deep neural networks Covalent Organic Framework Applications ; Thermal properties of materials ; Hydrogels: synthesis, properties, applications
Authors: Dmytro Tymoshchuk | Oleh Yasniy
Source:  (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

AutoML with Explainable AI Analysis: Optimization and Interpretation of Machine Learning Models for the Prediction of Hysteresis Behavior in Shape Memory Alloys This study presents an approach for predicting the hysteresis behavior of shape memory alloys (SMAs) based on automated machine learning (AutoML) integrated with explainable artificial intelligence (XAI). Experimental data from cyclic tests of NiTi wire under loading frequencies of 0.3, 0.5, 1, and 5 Hz were used for model development. The AutoML framework PyCaret enabled automated model selection, hyperparameter optimization, and performance comparison of regression algorithms. The highest prediction accuracy was achieved by the LightGBM model (for 0.3 Hz and 1 Hz) and the CatBoost model (for 0.5 Hz and 5 Hz), both demonstrating a coefficient of determination R2 > 0.997 and low MSE, MAE, and MAPE values. Integration of XAI through the SHAP method provided both global and local interpretability of the model’s behavior. The analysis revealed the dominant influence of the Stress parameter, the physically meaningful role of the loading or unloading stage (UpDown), and a gradual increase in the contribution of the Cycle parameter in later cycles, reflecting fatigue accumulation effects. The obtained results confirm the high accuracy, interpretability, and physical consistency of the developed models. Interpretability ; Hysteresis ; Hyperparameter ; Interpretation (philosophy) ; Consistency (knowledge bases) ; Experimental data ; Shape-memory alloy Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; High Temperature Alloys and Creep
Authors: Wenzong Tao | Qianyu Wu | Bensheng Yang | Chen Yu | Guangyi Lu | Qian Zhang | Haiming Wang | Wei Hong
Source: IEEE Antennas and Wireless Propagation Letters (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Synthesis of Heterogeneous Arrays via Active Geometric Design Mutual coupling often leads to discrepancies between intended and synthesized array patterns due to the distortion of active element patterns (AEPs). In array synthesis, existing machine learning methods are predominantly confined to predicting AEPs for fixed structures, imposing a passive constraint that limits optimization flexibility. A holistic framework is introduced for the active geometric design of heterogeneous arrays, in which the geometric parameters of individual elements are treated as degrees of freedom alongside traditional ones. To manage the complexity associated with mutual coupling, a variational autoencoder-based surrogate model is developed to efficiently capture the mapping between diverse element structures and their radiation patterns in a latent space. Numerical comparisons indicate a 9.8× speedup in prediction over traditional methods. Furthermore, under rigorous uniform amplitude and 1-bit phase constraints, sidelobe suppression improvements of over 8 dB are achieved at scanning angles compared to homogeneous baselines, validating the efficacy of incorporating element-level geometric design into the global optimization process. Geometric shape ; Solid modeling ; Topology (electrical circuits) ; Matrix algebra ; Electronic design automation ; Algorithm design Machine Learning in Materials Science ; Advanced Materials and Mechanics ; Quasicrystal Structures and Properties
Authors: JongHyeon Park | Takuji Oda
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

High-throughput generation of radiation damage structures in bcc-W using machine learning statistical and image recognition models trained on molecular dynamics Recoil ; Gaussian process ; Radiation damage ; Convolutional neural network ; Gaussian ; Kriging ; Classifier (UML) ; Support vector machine Graphite, nuclear technology, radiation studies ; Fusion materials and technologies ; Machine Learning in Materials Science
Authors: Suhail Ahmed Manroo | Suhail Khosa
Source: Next research. (article, 2026-03-05)
Field: Materials Science — Biomaterials

Image-processing-based multimodal machine learning framework for modeling corrosion behavior of FSPed Sn-reinforced ZE41 Mg alloys Corrosion ; Artificial neural network ; Feature (linguistics) Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloy Microstructure Properties
Authors: Zexi Li | Qian Wang | Jie Li | Xiao Tang | Congwei Xie | Fei Du | Yu Xie
Source: ACS Energy Letters (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Machine-Learning-Accelerated Discovery of High-Performance Na <sub>5</sub> X(Si/Ge) <sub>4</sub> O <sub>12</sub> (X = Y, Ln, Bi) Solid-State Electrolytes Developing high-performance solid-state electrolytes is essential for realizing safe, high-energy-density all-solid-state batteries. In this work, we systematically screen the Na5XSi4O12 and Na5XGe4O12 (X = Y, Ln, Bi) series using universal interatomic potentials, first-principles calculations, and specialized machine learning potentials. Most candidates exhibit favorable thermodynamic and electrochemical stability and robust electronic insulation, underscoring their promise as solid-state electrolytes. In the silicate series, ionic conductivity varies nonmonotonically with ionic radius, where Na5BiSi4O12 achieving an outstanding room-temperature conductivity of 13.74 mS/cm. This behavior is attributed to enhanced Bi–O bond covalency, which exerts an inductive effect that smoothens the migration landscape. By contrast, the germanate analogues display inferior properties due to stronger electrostatic stabilization and rougher energy landscapes. Geometric analysis further reveals that increasing the substituent ionic radius induces XO6 octahedral distortion and constricts migration bottlenecks in both series. These findings provide a comprehensive roadmap for the rational design of this solid-state electrolyte family. Electrolyte ; Germanate ; Ionic bonding ; Ionic conductivity ; Ionic radius ; Electrochemistry ; Conductivity ; Silicate Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Machine Learning in Materials Science
Authors: Jing Yi | Yuxin Zhan | Shuai Zhao | Huanhuan Li
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Applications of Machine Learning Force Fields in Inorganic Crystalline Materials: From Theoretical Foundations to Practical Exploration Benchmarking ; Field (mathematics) ; Generalization ; Interface (matter) ; Computational model ; Force field (fiction) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Ikponmwosa J. Iyinbor | Ken-ichi Nomura | Paulo S. Branicio
Source: The Journal of Computational Science Education (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Stacking Fault Energy in Steel Alloys Based on Chemical Composition Energy (signal processing) ; Stacking-fault energy ; Stacking ; Chemical composition ; Fault (geology) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Material Properties and Failure Mechanisms
Authors: Nicolaï Gouraud | Côme Cattin | Thomas Plé | Olivier Adjoua | Louis Lagardère | Jean-Philip Piquemal
Source: arXiv (Cornell University) (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces Following our previous work (J. Phys. Chem. Lett., 2026, 17, 5, 1288-1295), we propose the DMTS-NC approach, a distilled multi-time-step (DMTS) strategy using non-conservative (NC) forces to further accelerate atomistic molecular dynamics simulations using foundation neural network models such as FeNNix-Bio1. There, a dual-level reversible reference system propagator algorithm (RESPA) formalism couples a target accurate conservative potential to a simplified distilled representation optimized for the production of non-conservative forces. Despite being non-conservative, the distilled architecture is designed to enforce key physical priors, such as equivariance under rotation and cancellation of atomic force components. These choices facilitate the distillation process and therefore improve drastically the robustness of simulation, significantly limiting abnormal discrepancies between the two models, thus achieving excellent agreement with the forces data. Overall, the DMTS-NC scheme is found to be more stable and efficient than its conservative counterpart with additional speedups reaching 15-30\% over DMTS. Requiring no fine-tuning steps, it is easier to implement and can be pushed to the limit of the systems physical resonances to maintain accuracy while providing maximum efficiency. We obtain additional speedup by combining hydrogen mass repartitioning (HMR), High Hydrogen Friction (HHF) to further extended the largest timestep up to 10fs of our schemes while conserving stability and accuracy. As for DMTS, DMTS-NC is applicable to any neural network potential and can be applied to approaches that are computationally heavier than FeNNix-Bio1. We show a proof of principle applying the approach to the distillation of MACE-OFF23 with consequent speedups ranging from 3.66 to 5.64 compared to single timestep. Robustness (evolution) ; Formalism (music) ; Artificial neural network ; Limiting ; Molecular dynamics ; Fractionating column ; Distillation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Juan Felipe Castro‐Landinez | Tasmai Paul | Rodrigo Q. Albuquerque | Holger Schmalz | Andreas Greiner | Holger Ruckdäschel
Source: Polymer Engineering and Science (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Data‐Driven Design of Co‐Continuous Morphology in <scp>PS</scp> / <scp>PMMA</scp> Two‐Phase Polymer Blends: A Theoretical, Experimental, and Machine Learning Approach ABSTRACT The morphological control of immiscible polymer blends is critical for tailoring material properties, yet predicting phase structures remains challenging. This study combines theoretical modeling, experimental characterization, and machine learning to analyze morphologies in polystyrene/poly(methyl methacrylate) (PS/PMMA) blends. Phase inversion compositions were predicted using Utracki and Yu‐Bousmina‐Schreiber models at 60–68 wt% PMMA, correlating well with transmission electron microscopy observations. A PS‐ b ‐PMMA diblock copolymer compatibilizer effectively stabilized morphologies and broadened the co‐continuous region. A co‐continuity index (CCI * ) quantified morphological characteristics, revealing maximum co‐continuity (CCI* = 0.70–0.86) in the 40–60 wt% PMMA range. Bayesian optimization identified optimal processing windows with minimal experiments, while machine learning models, particularly random forest, successfully predicted co‐continuity indices. Compositional factors dominated morphology formation over processing conditions. This integrated methodology provides an efficient framework for accelerating polymer blend development with reduced experiments required. Polymer ; Copolymer ; Bayesian optimization ; Morphology (biology) ; Phase inversion ; Polymer blend ; Transmission electron microscopy Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Jie Shen | Chenyu Wang | Libin Chen | shaoqin Jiang | Jianhui Chen | Cuilian Wen | Bo Wu | Baisheng Sa | Lin-Wang Wang
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

DBMLFF: linear scaling machine learning force fields <i>via</i> electron density decomposition for molecular electrolytes DBMLFF enables modular, transferable force fields with ab initio accuracy for complex multi-component systems. Scaling ; Force field (fiction) ; Decomposition ; Field (mathematics) ; Work (physics) ; Molecular dynamics ; Electron density Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Physical and Chemical Molecular Interactions
Authors: L. B. Fu | Longfei Lv | Fan Zhang | Si Zhu Zhou | W. Gao | Jijun Zhao
Source: arXiv (Cornell University) (preprint, 2026-01-06)
Field: Materials Science — Materials Chemistry

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for excited states. In this study, we introduce a high-throughput screening framework that integrates a graph neural network (GNN) with multi-level validation to accelerate the discovery of SF-active molecules. Trained on a previously reported FORMED database, the GNN achieves state-of-the-art accuracy in predicting SF-relevant excited-state properties, demonstrating a mean absolute error of about 0.1 eV for S1, T1, and T2 excitation energies. This capability facilitates the efficient screening of over 20 million molecular structures from both OE62 and QO2Mol databases. Our framework significantly reduces the computational demand associated with Time-Dependent Density Functional Theory validation by four orders of magnitude and identifies 180 potential SF molecules along with more than 1000 conformers. Subsequent assessments regarding synthetic accessibility, GW approximation and Bethe-Salpeter equation calculations further highlight a subset of experimentally feasible candidates among these SF candidates. The approach presented herein exemplifies an effective strategy for accelerating the discovery of functional molecules with optoelectronic applications. Limit (mathematics) ; Artificial neural network ; Density functional theory ; Identification (biology) ; Fission ; Excited state ; Graph theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Magnetism in coordination complexes
Authors: Yu. D. Fomin | E. N. Tsiok | V. N. Ryzhov
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

Melting of thin silicon films: a molecular dynamics study with two machine learning potentials Thermal stability of silicene and thin silicon films is studied by molecular dynamics using two machine-learning potentials, SNAP and GAP. For SNAP potential, systems ranging from a single silicene layer to films of 36 layers are considered. Silicene is found to lose its structure at 500 K. The decomposition temperature increases with film thikness and reaches saturation at about 28 layers, corresponding to the bulk melting point of the SNAP model (1380 K). Thin films up to 8 layers exibit two-phase coexistence upon decomposition, while thicker films undergo surface melting followed by complete collapse into the liquid state. The GAP potential, although more accurate for bulk silicon, fails to describe the gas phase: silicene modelled with GAP decomposes into a set of small clusters. The results are compared with earlier data for the Stillinger-Weber potential. Silicene ; Silicon ; Molecular dynamics ; Thin film ; Thermal stability ; Melting point ; Layer (electronics) ; Thermal Thermal properties of materials ; Graphene research and applications ; Material Dynamics and Properties
Authors: Peng Gao | Zhongxiang Lv | Yanzhou Wang | Yuguo Sun | Ping Qian
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Micromechanical evolution of tensile nanoporous carbon: A machine−learning molecular dynamics study Ultimate tensile strength ; Nanoporous ; Elastic modulus ; Young's modulus ; Elasticity (physics) ; Molecular dynamics ; Modulus ; Microstructure Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Wa Ode Nur Fitriah Rajaelo | Harry Handoko Halim | M. Fadhlan Anshor | Yuki Yamada | Syahrul Ramadhani | Fikri Akbar Mubarok | Septia E. M. Putra | Atthar Luqman Ivansyah | Triati D. K. Wungu | Suprijadi | Yoshitada Morikawa | Fahdzi Muttaqien
Source: ACS Catalysis (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

Bridging the Pressure Gap in Hydrogenation of CO <sub>2</sub> to Formate on Cu(100) by Machine Learning Molecular Dynamics and First-Principles Microkinetic Modeling The discrepancy in the catalytic behaviors in ultrahigh vacuum vs realistic gas environments, known as “pressure gap”, has been a long-standing challenge in the field of surface science. The challenge lies in the limited knowledge of the surface morphology that might undergo reconstruction as a result of exposure to gas and adsorbates. In this study, we address the challenge by applying a set of multiscale simulations that included density functional theory (DFT), machine learning-based molecular dynamics (MLMD), and microkinetic modeling (MKM) simulations, taking the case of hydrogenation of CO2 into formate on the Cu(100) surface. MLMD simulations allow direct observation of hydrogen-induced reconstruction of the Cu(100) surface in the form of a rotated and line-shifted structure even at room temperature. This insight into the surface dynamics is used to provide more realistic structures of the active sites under experimental conditions. Our calculations reveal that this reconstruction significantly influences the CO2 hydrogenation rate on the Cu(100) surface. In particular, the line-shifted reconstruction on the Cu(100) surface brings the energy barrier and the hydrogenation rate much closer to those on the Cu(111) surface, matching the experimentally observed insensitivity of the Cu surface facets to the CO2 hydrogenation rate. On the other hand, the hydrogenation on the pristine Cu(100) surface exhibits a lower barrier compared to the Cu(111) surface, resulting in significant overestimation of the reaction rate compared to the experiment. This study demonstrates that adopting more realistic active sites from in situ observation of simulated catalytic reactions paves the way for a better connection between theory and experiment in the catalysis field. Bridging (networking) ; Formate ; Catalysis ; Molecular dynamics ; Density functional theory ; Surface reconstruction ; Surface (topology) ; Heterogeneous catalysis ; Nanostructure Machine Learning in Materials Science ; Catalysts for Methane Reforming ; Advanced Chemical Physics Studies
Authors: F.J. Dominguez-Gutierrez | T. Stasiak | G. Markovic | A. Kosinska | K. Mulewska
Source: International Journal of Refractory Metals and Hard Materials (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Nanoindentation induced plasticity in equiatomic MoTaW alloys by experimentally guided machine learning molecular dynamics simulations Nanoindentation ; Plasticity ; Molecular dynamics ; Finite element method ; Work (physics) ; Dynamics (music) Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Titanium Alloys Microstructure and Properties
Authors: Md. Efatuzzaman Efat | Md. Saiful Islam | Md. Saymon Hosen Polash | Sayed Md Fazle Rabbi
Source:  (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Band Gap Prediction in 2D Materials via Machine Learning and Explainable AI: A Physics Inspired Approach Two dimensional (2D) materials have become the leaders in the next generation semiconducting applications due to their outstanding electronic and optical characteristics. Predicting the electronic band gap which is a crucial property influencing device performance is computationally expensive if determined through traditional density functional theory (DFT) method. In this study, we present a machine learning (ML) framework that leverages rich features derived from the JARVIS-DFT database to predict DFT-computed band gaps for ~1000 monolayer 2D materials. Also, a high-quality physics-inspired feature engineering methodology and explainable AI (XAI) models are used to improve the accuracy of predictions and their interpretability. Then, we compare 12 kernel-based regression models on a pipeline of cross-validation (5-fold) and grid search optimization. ExtraTrees regressor is the best performing model achieving R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of around 0.80 and RMSE approximately 0.48 eV on a hold-out test set. In order to make scientific transparency, we use SHAP analysis to identify the main structural and electronic features behind predictions. The combination of informatics of materials and XAI provides a scalable and interpretable alternative to DFT that speeds up the discovery of materials in the 2D material design space. Band gap ; Training set ; Deep learning ; Field (mathematics) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks