jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fei Shuang | Penghua Ying | Kai Liu | Zixiong Wei | Fengxian Liu | Zheyong Fan | Minqiang Jiang | Poulumi Dey
Source: ArXiv.org (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are important for atomistic simulations of compositionally complex materials such as high-entropy alloys. Here, we study two state-of-the-art MLIP frameworks, the neuroevolution potential (NEP) and the graph atomic cluster expansion (GRACE), for 16 elemental metals and multicomponent alloys. GRACE potential with Finnis-Sinclair type shows substantially higher training efficiency and consistently, though only slightly, better accuracy for mechanical properties, thermal stability, and chemical extrapolation. In contrast, NEP achieves an approximately 60-fold higher inference speed, making it attractive for million-atom molecular dynamics simulations. We further examine uncertainty quantification strategies and find that ensemble-based uncertainty correlates robustly with model error, whereas D-optimality is less reliable for the systems considered here. Large-scale nonequilibrium molecular dynamics simulations of shock propagation further show that NEP, combined with ensemble-based uncertainty quantification, enables efficient and reliable simulations under extreme dynamic conditions. Molecular dynamics ; Interatomic potential ; Inference ; Non-equilibrium thermodynamics ; Flexibility (engineering) ; Thermal ; Uncertainty quantification ; Graph Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Xiaotian Xing | Fu-ming DENG | Hengchen Xu | Chang Dong | Houzhen Chen | Yonghai Lai | Jiale Niu | Lei Zhang | Diaoyu Wang | Ziyi Liu | Qiang Chen | Jianghe Liao
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Temperature Field Prediction Model for the Powder Catalyst Diamond Synthesis Assembly Finite element method ; Temperature measurement ; Diamond ; Field (mathematics) ; Core (optical fiber) ; Temperature control ; Computation ; Thermal Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Phase Equilibria and Thermodynamics
Authors: Elaheh Kazemi-Khasragh | Rocío Mercado | Carlos Gonzalez | Maciej Haranczyk
Source: Computational Materials Science (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning Copolymer ; Polymer ; Molecular descriptor ; Artificial neural network ; Representation (politics) ; Molecular dynamics ; Monomer ; Set (abstract data type) ; Molecular graph Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Matthew A. McDonald | K. G. Jensen
Source: Annual Review of Analytical Chemistry (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Machine Learning and Autonomous Systems for Accelerated Synthesis Autonomous systems integrating machine learning (ML) and laboratory automation are transforming synthetic chemistry by enabling closed-loop experimentation and discovery. In this review, we examine the state-of-the-art in autonomous systems for organic synthesis, with a focus on the components, configurations, and ML algorithms that enable automated reaction planning, execution, and optimization. We survey representative systems that span applications from reaction discovery to molecular optimization, comparing flow and batch configurations and identifying trends in system design. Emphasis is placed on the critical bottlenecks of purification and analytical measurement, particularly structural elucidation of unexpected products-areas that currently constrain autonomous platforms. We describe recent advances in chromatographic method development, structural elucidation from mass spectrometry and nuclear magnetic resonance, and novel ML-based approaches to quantify complex mixtures without calibration. By focusing on enabling technologies in chemical analysis, we identify opportunities for ML and automation to expand beyond domain-specific platforms and accelerate the pace of synthetic discovery. Automation ; Pace ; Laboratory automation ; Focus (optics) ; Synthetic data ; Expert system Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Toshifumi Mori | Kei-ichi Okazaki | Kang Kim | Nobuyuki Matubayasi
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates In complex molecular systems, the reaction coordinate (RC) that characterizes transition pathways is essential to understand underlying molecular mechanisms. This review surveys a framework for identifying the RC by applying deep learning to the committor, which provides the most reliable measure of the progress along a transition path. The inputs to the neural network are collective variables (CVs) expressed as functions of atomic coordinates of the system, and the corresponding RC is predicted as the output by training the network on the committor as the learning target. Because deep learning models typically operate in a black-box manner, it is difficult to determine which input variables govern the predictions. The incorporation of eXplainable Artificial Intelligence (XAI) techniques enables quantitative assessment of the contributions of individual input variables to the predictions. This approach allows the identification of CVs that play dominant roles and demonstrates that the committor distribution on the surface using important CVs is separated by well-defined boundaries. The framework provides an explainable deep learning strategy for assigning a molecular mechanism from the RC and is applicable to a wide range of complex molecular systems. Deep learning ; Artificial neural network ; Identification (biology) ; Range (aeronautics) ; Mechanism (biology) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Alan George | Marek Sierka
Source: Advanced Theory and Simulations (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Machine Learning Meets Molecular Dynamics: Accurate Prediction of Polymer Solubility Parameters ABSTRACT The Hildebrand solubility parameter is a cornerstone descriptor for predicting thermodynamic compatibility in polymer‐solvent systems, yet its determination for polymers remains experimentally challenging, and traditional group contribution estimates lack accuracy and transferability. Here, we present an integrated framework that combines atomistic molecular dynamics (MD) simulations with machine learning (ML) to predict solubility parameter values across diverse molecular and polymeric systems. Solubility parameter values derived from MD simulations provide training targets for ML models built on molecular, quantum‐chemical, and chain‐specific descriptors. By systematically applying feature‐selection strategies, Pearson correlation filtering, principal component analysis, and recursive feature elimination, we construct optimized descriptor subsets tailored to each learning algorithm. Benchmarking across regression methods reveals that Gaussian process regression with recursive feature elimination achieves the best performance, with R 2 = 0.94, MAE = 0.54, and RMSE = 0.96 MPa 1/2 on the test set, consistently outperforming both empirical group‐contribution methods and conventional regressors. Our results highlight the critical role of descriptor curation and nonlinear models in capturing the complexity of polymer thermodynamics. This hybrid MD‐ML strategy establishes a generalizable and computationally efficient pathway for predicting solubility parameters, enabling rapid screening of solubility and miscibility for both known and novel molecules and polymers. Solubility ; Hildebrand solubility parameter ; Principal component analysis ; Regression ; Molecular descriptor ; Mean squared error ; Kriging ; Molecular dynamics ; Linear regression ; Polymer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Jie Li | Xintong Qu | Wenlong Zhang | Shifa Zhong
Source: Journal of Cheminformatics (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry The success of machine learning in chemistry is fundamentally underpinned by the information fidelity of molecular representations. Despite their widespread adoption for efficiency and interpretability, Morgan fingerprints harbor a long-overlooked and fundamental flaw: bit collisions. This phenomenon erroneously maps distinct chemical substructures to identical positions, systematically corrupting structure-property relationships and severely compromising model interpretability. To address this challenge, we introduce Collision-Free Morgan Fingerprints (CF-MF), a principled framework that guarantees the integrity of substructure information through an adaptive, data-driven sizing mechanism. Through a comprehensive evaluation across 25 diverse datasets (> 50,000 molecules) and multiple machine learning paradigms, we demonstrate that CF-MF delivers consistent and significant performance gains up to 16.81% RMSE reduction in regression and 11.1% accuracy increase in classification. More critically, by eliminating attribution errors caused by collisions, CF-MF fundamentally restores chemical interpretability and expands the reliable prediction domains of models by 60-100%. Our information-theoretic analysis reveals a strong correlation between collision-induced entropy loss and performance degradation (R2 = 0.854, p < 0.001), establishing information fidelity as a fundamental design principle for next-generation molecular representations. It also achieves performance competitive with state-of-the-art deep learning models while retaining the simplicity and intuitiveness of traditional fingerprints. This work provides a more reliable and trustworthy foundation for AI-driven drug discovery, materials science, and environmental assessment.Scientific contributionWhile bit collisions in Morgan fingerprints have been acknowledged for decades, this study is the first to systematically quantify their impact on machine learning performance and provide a principled, reproducible solution applicable to any molecular dataset. We establish a novel information-theoretic framework that directly links collision-induced entropy loss to predictive degradation, offering the field a quantitative criterion for evaluating molecular representation fidelity. Beyond performance gains, our work uniquely demonstrates that eliminating collisions restores chemically valid SHAP attributions-addressing a critical but previously unrecognized barrier to trustworthy AI interpretation in chemistry. Interpretability ; Fidelity ; Cheminformatics ; Deep learning ; Applicability domain ; Workaround ; Credibility ; Ranking (information retrieval) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Lei Wang | Feiyu Zhou | Penghua Ying | Yan Zhou Chen | Yilun Liu
Source: Journal of Materials Informatics (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated transition state prediction for strain-engineered high-entropy alloy catalysts The hydrogen evolution reaction represents a critical bottleneck in renewable energy conversion, with transition state (TS) identification being essential for rational catalyst design. While strain engineering offers powerful pathways to modulate catalytic activity in high-entropy alloys (HEAs), conventional density functional theory (DFT) calculations face prohibitive computational costs due to inherent atomic-level disorder and expanded configurational space. Here, we present a fine-tuned graph neural network EquiformerV2 (eqV2) framework that dramatically accelerates TS discovery in strain-engineered Ir-Pt-Rh-Pd-Ru HEA catalysts. Our lightweight 31-million-parameter model achieves pathway prediction from hours-scale DFT calculations to second-scale predictions while maintaining exceptional accuracy: mean absolute errors below 0.1 eV for reaction energies and structural predictions within 0.1 Å root mean square deviation for 88.8% of configurations across Volmer, Heyrovsky, and Tafel pathways under biaxial strain. This methodology establishes a scalable computational framework that overcomes traditional limitations in high-dimensional catalyst screening, offering a generalizable strategy for artificial intelligence-accelerated next-generation electrocatalyst discovery. Bottleneck ; Density functional theory ; Catalysis ; Tafel equation ; Artificial neural network ; Scalability ; Electrocatalyst Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Eshkulov Bekzod | Jurayev Rozimurod
Source: INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

APPLICATION OF MACHINE LEARNING IN PREDICTING ELECTROCHEMICAL REACTION OUTCOMES Machine learning (ML) is increasingly used to predict and optimize electrochemical reactions by analyzing complex datasets and revealing hidden patterns. This study reviews ML applications in catalyst performance, reaction selectivity, and mechanism prediction, emphasizing challenges in data quality and model interpretability. Quality (philosophy) ; Mechanism (biology) ; Experimental data ; Training set ; Data quality Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Le Huu Nghia | Phạm Thị Bích Thảo | Truong Do Anh Kha | Vo Khuong Dien | Nguyen Thanh Tien
Source: ArXiv.org (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Efficient molecular dynamics simulation of 2D penta-silicene materials using machine learning potentials Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that allows achieving near-quantum mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations. In this work, we use MLIP from DeepMD package and the classical Tersoff potential for SiC (Tersoff.SiC potential) to fully and accurately describe atomic interactions and apply them to molecular dynamics simulations of penta silicene sheet. The results show that the melting points (T$_g$) temperatures of the system in the canonical NVT and isobaric NPT sets are 632 K and 606 K, while the Tersoff.SiC potential have the high melting points, respectively. In addition, the radial distribution function exhibits characteristic peaks at interatomic distances of 2.275 Å\text{} and 2.375 Å, while the Tersoff.SiC potential only describe distance of 2.375 Å. Furthermore, penta silicene was also simulated using on-the-fly machine learning for 10 ps to evaluate the structural stability of the system. This study investigates the thermodynamic properties of two-dimensional penta silicene sheets with pentagonal structures using a high-precision, cost-effective method, contributing further evidence to support experimental synthesis and opening up potential future applications of this material. Silicene ; Molecular dynamics ; Interatomic potential ; Stability (learning theory) ; Radial distribution function ; Function (biology) ; Canonical ensemble Graphene research and applications ; Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Mohammad Khajavian | Suzylawati Ismail | Vahid Vatanpour
Source: Advanced Engineering Materials (article, 2026-03-19)
Field: Materials Science — Surfaces, Coatings and Films

Zwitterionic Materials for Water Treatment and Food Packaging: Synthesis, Performance, and Machine Learning‐Driven Design The growing demand for high‐performance materials in water treatment and food packaging has intensified research into zwitterionic‐based structures due to their exceptional hydrophilicity, fouling resistance, and functional versatility. This review presents a comprehensive analysis of recent advances in the synthesis, characterization, and performance evaluation of zwitterionic materials for these critical applications, with particular emphasis on emerging machine learning approaches that enable their rational design and optimization. By summarizing key findings and identifying current challenges, this work aims to bridge the gap between laboratory‐scale innovations and real‐world implementation. Among various synthesis strategies, polymerization has emerged as the most effective method for incorporating zwitterionic functionalities. Morphological studies further demonstrate that zwitterionic modification significantly enhances both adsorptive and barrier properties. Machine learning models have shown strong potential in accelerating the development of zwitterionic materials by establishing structure–property relationships that predict and optimize antifouling performance. By integrating insights from multiple disciplines, this review outlines a strategic framework for transforming fundamental research into practical solutions that promote environmental sustainability and food security. Fouling ; Biofouling ; Sustainability ; Water treatment ; Rational design ; Work (physics) Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Hydrogels: synthesis, properties, applications
Authors: Sobin Alosious | Yuhan Liu | Jiaxin Xu | Gang Liu | Renzheng Zhang | Meng Jiang | Tengfei Luo
Source: arXiv (Cornell University) (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

ADEPT-PolyGraphMT: Automated Molecular Simulation and Multi-Task Multi-Fidelity Machine Learning for Polymer Property Generation and Prediction The discovery of polymers with targeted properties is challenged by the vast chemical design space and the limited availability of consistent, high-quality data across multiple properties. In this work, an integrated polymer informatics framework is presented that combines the Automated molecular Dynamics Engine for Polymer simulaTions (ADEPT) workflow with multi-task and multi-fidelity machine learning (PolyGraphMT). Polymer repeat units are represented as molecular graphs and processed using a graph neural network to learn structure-property relationships. Starting from SMILES representations for monomers, ADEPT automates the construction of atomistic models and the evaluation of their properties using molecular dynamics simulations and density functional theory calculations. The simulation data are combined with curated experimental data and group contribution theory estimates to construct a unified dataset of approximately 62,000 polymer property values spanning 28 properties. Using this dataset, inter-property correlations are analyzed, and multi-task learning strategies are evaluated for joint property prediction. The results show that multi-task models achieve performance comparable to single-task models in data-rich regimes and exhibit superior accuracy as training data become limited. In addition, fidelity-aware training improves predictive accuracy when combining experimental and computational data sources. The trained models are further applied to large-scale property prediction for polymers in the PolyInfo database and the PI1M virtual polymer library, producing physically consistent property distributions across a broad chemical space. Overall, the proposed framework provides a structured approach for scalable prediction and screening of polymer properties across multiple property types and data fidelity levels. Property (philosophy) ; Workflow ; Scalability ; Artificial neural network ; Fidelity ; Chemical space ; Predictive modelling ; Experimental data ; Data modeling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ling Chen | Bo Li | hujun shen
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated Prediction of the Electrochemical Window for Electrolyte Solvents Using Multiple Quantum-Chemical Descriptors Density functional theory ; Robustness (evolution) ; Hyperparameter ; Generalization ; Electrolyte ; Bayesian inference ; Dipole ; Bayesian probability ; Moment (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Yuanchuan Ren | Yuhang Lin | Xuejun Zhu | Shiyong Zhao | Renjie Huang | Tingfeng Su | Cheng Wang | Nanqi Ren
Source: Polymer Degradation and Stability (article, 2026-01-31)
Field: Materials Science — Polymers and Plastics

The paradigm shift of intelligent material design and development for fire safety: progress, challenges, and machine learning Paradigm shift ; Field (mathematics) ; Deep learning ; Active learning (machine learning) ; Mechanism (biology) ; Artificial neural network ; Material Design Flame retardant materials and properties ; Fire dynamics and safety research ; Thermal and Kinetic Analysis
Authors: Sobin Alosious | Yuhan Liu | Jiaxin Xu | Gang Liu | Renzheng Zhang | Meng Jiang | Tengfei Luo
Source: arXiv (Cornell University) (preprint, 2026-03-28)
Field: Materials Science — Materials Chemistry

ADEPT-PolyGraphMT: Automated Molecular Simulation and Multi-Task Multi-Fidelity Machine Learning for Polymer Property Generation and Prediction The discovery of polymers with targeted properties is challenged by the vast chemical design space and the limited availability of consistent, high-quality data across multiple properties. In this work, an integrated polymer informatics framework is presented that combines the Automated molecular Dynamics Engine for Polymer simulaTions (ADEPT) workflow with multi-task and multi-fidelity machine learning (PolyGraphMT). Polymer repeat units are represented as molecular graphs and processed using a graph neural network to learn structure-property relationships. Starting from SMILES representations for monomers, ADEPT automates the construction of atomistic models and the evaluation of their properties using molecular dynamics simulations and density functional theory calculations. The simulation data are combined with curated experimental data and group contribution theory estimates to construct a unified dataset of approximately 62,000 polymer property values spanning 28 properties. Using this dataset, inter-property correlations are analyzed, and multi-task learning strategies are evaluated for joint property prediction. The results show that multi-task models achieve performance comparable to single-task models in data-rich regimes and exhibit superior accuracy as training data become limited. In addition, fidelity-aware training improves predictive accuracy when combining experimental and computational data sources. The trained models are further applied to large-scale property prediction for polymers in the PolyInfo database and the PI1M virtual polymer library, producing physically consistent property distributions across a broad chemical space. Overall, the proposed framework provides a structured approach for scalable prediction and screening of polymer properties across multiple property types and data fidelity levels. Property (philosophy) ; Workflow ; Scalability ; Artificial neural network ; Fidelity ; Chemical space ; Predictive modelling ; Experimental data ; Data modeling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks