jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. N. Zaloga | V. V. Stanovov | I. S. Yakimov | P. S. Dubinin | O. E. Bezrukova
Source: Journal of Structural Chemistry (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Neural Network Determination of the Crystal Symmetry of Substances Using Powder XRD Patterns Normalized to the Crystal′s Unit Cell Volume Crystal systems, Bravais lattices, extinction groups, space groups, and intervals of unit cell volume are classified using a convolutional neural network based on the deep learning of model full-profile powder XRD patterns computed from ICSD data. A new method is proposed to significantly increase the classification accuracy by normalizing unit cell volumes to the total fixed value when computing the model XRD patterns. The classification accuracy determined from an independent set of normalized model XRD patterns is 97.4% for crystal systems and 88.0% for space groups. Due to its high accuracy, this neural network can be utilized to perform a crystal symmetry analysis using experimental unit cell volumes and to determine space groups without using reflection extinction rules after finding the unit cell volume by commonly utilized indexing programs. Extinction (optical mineralogy) ; Artificial neural network ; Crystal (programming language) ; Symmetry (geometry) ; Volume (thermodynamics) ; Convolutional neural network ; Crystal structure ; Crystal system X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Powder Metallurgy Techniques and Materials
Authors: Kai Li | Lei Xu | Huiwen Guan | Jie Li | Linlin Wu | You‐Nian Liu
Source: ChemRxiv (article, 2026-02-24)
Field: Chemistry — Organic Chemistry

Unveiling the Adsorption of N-PHA on Graphene-Supported Single-Atom Catalysts: A DFT and Machine Learning Study Highly selective catalytic synthesis of N-phenylhydroxylamine (N-PHA) is of great significance. However, undesired side reactions readily occur under reaction conditions. Understanding its adsorption on catalyst surfaces is essential for controlling reactivity and selectivity. Herein, density functional theory (DFT) is employed to investigate the adsorption of N-PHA on graphene-supported M1-N3X/C single-atom catalysts (M: transition metal; X: a non-metal dopant). Two primary coordination modes, N-and O-coordination, are compared by analyzing adsorption Gibbs free energies, Bader charges, d-band centers, and ICOHP. In addition, high-throughput screening of 108 configurations via the SISSO machine learning algorithm identifies key physical descriptors governing adsorption energy, enabling the construction of predictive regression and classification models. This work reveals the synergistic roles of metal center and non-metal dopant in regulating adsorption strength and selectivity, offering both fundamental insights and a data-driven framework for designing highperformance single-atom catalysts in green synthesis. Adsorption ; Catalysis ; Density functional theory ; Gibbs free energy ; Reactivity (psychology) ; Transition metal Nanomaterials for catalytic reactions ; Catalysis for Biomass Conversion ; Asymmetric Hydrogenation and Catalysis
Authors: Danila Shiryaev | Emil I Jaffal | Sangjoon Lee | Balaranjan Selvaratnam
Source: ChemRxiv (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Achieving a Scalable Machine Learning Workflow for Crystal Structure Discovery with Experimental Validations Machine learning (ML) has become a central component of data-driven materials discovery, yet its practical impact remains heavily dependent on how these predictions are translated into experimentally-realizable outcomes. In this review, we examine ML-guided crystal structure discovery through the lens of recommendation as well as unconstrained generation, to emphasize interpretable workflows embedding chemical intuition, physical constraints, and experimental validation. Surveying standalone ML, hybrid ML-DFT, and machine-learned interatomic potential (MLIP) approaches, we highlight how constrained design spaces, data preprocessing, and validation strategies shape novel discovery success. Drawing on our own experimentally-validated case studies, ranging from supervised and unsupervised learning, as well as recommendation-type explorations, we outline the shift towards interpretable and explainable ML models guiding synthetic decisions and thus revealing trends otherwise previously confirming established trends while uncovering new ones. Collectively, we highlight the results of interpretable ML being more effective when deployed within experimental workflows to bridge learning and chemistry, to enable a reliable discovery pathway of solid state materials. Workflow ; Scalability ; Embedding ; Component (thermodynamics) ; Bridge (graph theory) ; Supervised learning ; Synthetic data Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Selim Demirci
Source: Engineering Science and Technology an International Journal (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Machine-learning-guided titanium alloy design for intrinsic grain growth resistance Controlling grain growth during high temperature processing remains a critical challenge in the development of titanium alloys, as excessive β-grain coarsening severely degrades mechanical properties. Although machine learning has increasingly been applied in materials science, its use for the systematic and mechanism-informed suppression of β-grain growth in titanium alloys has not been previously reported. In this study, a machine learning–guided framework is presented for the first time to design titanium alloys with intrinsic resistance to grain growth by integrating data-driven modelling, robust optimization, and kinetic analysis. A comprehensive database was constructed by correlating alloy composition, processing temperature, and holding time with β-grain size evolution. Pearson correlation analysis was employed to identify the most influential parameters governing grain growth behaviour. Multiple regression algorithms were evaluated to establish accurate surrogate models, among which the Gradient Boosting Regressor demonstrated superior predictive accuracy and robustness. Building on the trained surrogate models, a robust Bayesian optimization strategy was implemented to identify alloy compositions that minimize grain growth across a wide temperature–time window. The optimized Ti–Nb–Mo–Zr–Ta–Sn alloy exhibited consistently suppressed β-grain coarsening compared to conventional titanium alloys. Grain growth kinetics analysis revealed diffusion-controlled behaviour characterized by a high grain growth exponent and a large apparent activation energy of 961.10 kJ·mol −1 , indicating sluggish grain boundary migration dominated by solute drag effects. Furthermore, thermodynamic calculations confirmed the stability of a single β phase within the investigated processing range, supporting the metallurgical feasibility of the optimized alloy. Overall, this work demonstrates that integrating machine learning, optimization, and kinetic analysis provides a powerful and generalizable strategy for microstructure tailoring and the design of thermally stable titanium alloys. Grain growth ; Titanium alloy ; Solvent drag ; Grain boundary ; Alloy ; Grain size ; Bayesian optimization ; Titanium Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes
Authors: Caroline J. Crowder | Brandon J. Yik | Stephanie J. H. Frost | Daniel Cruz-Ramírez de Arellano | Kimberly Bliss-Roche | Jeffrey R. Raker
Source: Journal of Science Education and Technology (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Development of Machine Learning Models to Predict the Level of Explanation Sophistication for Organic Chemistry Mechanisms Generalizability theory ; Sophistication ; Formative assessment ; Scalability ; Categorization ; Science education ; Concept learning ; Computational model ; Outcome (game theory) Machine Learning in Materials Science ; Science Education and Pedagogy ; Various Chemistry Research Topics
Authors: Shinya Shiomi | Takuma Morimoto | Masahiro Yoshida
Source: ChemRxiv (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Rapid and General Machine Learning for Coupling Reactions via a Structure-Based Regression (SBR) Strategy Practical adoption of machine learning (ML) in synthetic chemistry is often limited by the need for large datasets and complex feature engineering. A rapid and practical ML strategy termed Structure-Based Regression (SBR) enables predictive modeling of coupling reactions using solely structural information of the substrates. The SBR framework employs uniform bit-length molecular fingerprints to represent diverse reaction archetypes without reaction-specific descriptors, allowing an accurate model construction even from small datasets (<100 data points). The generality of SBR was demonstrated across multiple coupling reactions, including reductive amination, Ni-catalyzed electrochemical etherification, metal-free deaminative coupling, and oxidative amidation. Despite this simplicity, the resulting regression models achieved mean absolute errors (MAE) of 4-12% over ten random train-test divisions. External validation using independent literature datasets revealed that systematic prediction deviations can indicate condition-dependent reactivity trends and highlight substrate classes where conventional conditions remain advantageous. Coupling (piping) ; Generality ; Feature (linguistics) ; Regression ; Support vector machine ; Random forest ; Regression analysis Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Haewon Kim | Taekgi Lee | Seongeun Hong | Kyeong-Ho Kim | Yongchul G. Chung
Source: Open MIND (preprint, 2026-01-16)
Field: Materials Science — Materials Chemistry

Data-driven Prediction of Ionic Conductivity in Solid-State Electrolytes with Machine Learning and Large Language Models Solid-state electrolytes (SSEs) are attractive for next-generation lithium-ion batteries due to improved safety and stability but their low room-temperature ionic conductivity hinders practical application. Experimental synthesis and testing of new SSEs remain time-consuming and resource intensive. Machine learning (ML) offers an accelerated route for SSE discovery; however, composition-only models neglect structural factors important for ion transport while graph neural networks (GNNs) are challenged by the scarcity of structure-labeled conductivity data and the prevalence of crystallographic disorder in CIFs. Here, we train two complementary predictors on the same room-temperature, structure-labeled dataset (n = 499). A gradient-boosted tree regressor (GBR) combining stoichiometric and geometric descriptors achieves best performance (MAE = 0.543 in log(S cm-1)), and Shapley Additive exPlanations (SHAP) identifies probe-occupiable volume (POAV) and lattice parameters as key correlations for conductivity. In parallel, we fine-tune large language models (LLMs) using compact text prompts derived from CIF metadata (formula with optional symmetry and disorder tags), avoiding direct use of raw atomic coordinates. Notably, Llama-3.1-8B-Instruct achieves high accuracy (MAE = 0.657 in log(S cm-1)) using formula and symmetry information, eliminating the need for numerical feature extraction from CIF files. Together, these results show that global geometric descriptors improve tree-based predictions and enable interpretable structure-property analysis, while LLMs provide a competitive low-preprocessing alternative for rapid SSE screening. Artificial neural network ; Symmetry (geometry) ; Stability (learning theory) ; Feature (linguistics) ; Conductivity ; Lattice (music) ; Tree (set theory) ; Key (lock) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Enran Hou | Xiaoyang WANG | Han WANG
Source: SHILAP Revista de lepidopterología (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Machine Learning Potential Model for Simulating Dynamic Mechanical Response of Pb-Sn Alloy Lead is a low-melting-point metal with a complex temperature-pressure phase diagram. Alloying with tin further reduces its melting temperature, making lead-tin alloys an important model material for studying dynamic mechanical responses and failure behavior. However, experimental characterization of atomic-scale dynamic failure mechanisms in PbSn alloys remains challenging due to current technical limitations. Non-equilibrium molecular dynamics (NEMD) simulations can track atom trajectories and reveal key dynamic processes under dynamic loading-unloading. It thus serves as a critical alternative tool. Yet, the reliability of molecular dynamics relies on the accuracy of interatomic potentials, and currently, no high-accuracy potential exists for PbSn alloys under dynamic conditions. In this work, we develop a machine-learning interatomic potential (DP-PbSn) for PbSn alloys using a concurrent learning scheme. This potential achieves first-principles accuracy across a wide thermodynamic range (0–100 GPa, 0–5000 K), reliably predicting fundamental properties (e.g., lattice constants, elastic constants), defect energetics (e.g., surface energy, stacking fault energy, vacancy formation energy), as well as melting curves and shock Hugoniot curves, demonstrating its suitability for dynamic simulations. Leveraging this potential, we conduct preliminary NEMD simulations to investigate the dynamic mechanical responses of pure Pb and PbSn alloys, elucidating the influence of Sn on phase transitions and plastic deformation under dynamic loading. The DP-PbSn serves as a robust theoretical tool for high-accuracy non-equilibrium molecular dynamics, providing essential insights for experimental studies on the dynamic damage behavior of PbSn alloys. Molecular dynamics ; Shock (circulatory) ; Tin ; Interatomic potential ; Deformation (meteorology) ; Stacking ; Phase (matter) ; Vacancy defect Machine Learning in Materials Science ; Electronic Packaging and Soldering Technologies ; Thermal properties of materials
Authors: Vidit Agrawal
Source: MRS Communications (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Polymer property prediction using ensemble of directed message-passing neural networks with uncertainty estimation and applicability domain analysis Property (philosophy) ; Artificial neural network ; Domain (mathematical analysis) ; Polymer ; Uncertainty analysis ; Estimation ; Uncertainty quantification Machine Learning in Materials Science ; Neural Networks and Applications ; Fault Detection and Control Systems
Authors: K. Barmak | J.M. Rickman
Source: Acta Materialia (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Machine learning for materials microstructures: A survey of applications and methodologies Image processing ; Segmentation ; Interpretation (philosophy) ; Feature extraction ; Image segmentation ; Artificial neural network ; Feature (linguistics) ; Parsing ; Correlative Machine Learning in Materials Science ; Composite Material Mechanics ; Material Selection and Properties
Authors: Chong Ma | Chen Yao | Jing Xu | Yibing Xie
Source: Sustainable materials and technologies (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Machine learning toward electrochemical energy storage materials and devices: A review Energy storage ; Electrochemical energy storage ; Energy (signal processing) ; Energy consumption Machine Learning in Materials Science ; Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research
Authors: Y. Xie | Menghang Wang | Senja Ramakers | F. Spaepen | Boris Kozinsky
Source: npj Computational Materials (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Incongruent melting and phase diagram of SiC from machine learning molecular dynamics Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale machine learning molecular dynamics (MLMD) simulations to gain insights into SiC decomposition and phase transitions. Our approach relies on a Bayesian active learning workflow to efficiently train an accurate machine learning force field on density functional theory data. Our large-scale simulations provide direct indication that melting of SiC proceeds incongruently via decomposition into silicon-rich and carbon phases at high temperature and pressure. During cooling at high pressures, carbon nanoclusters nucleate and grow within the homogeneous molten liquid. During heating, the decomposed mixture reversibly transitions back into a homogeneous SiC liquid. The full pressure-temperature phase diagram of SiC is systematically constructed using MLMD simulations, providing new understanding of the nature of phases, resolving long-standing inconsistencies from previous experiments and yielding technologically relevant implications for processing and deposition of this material. Phase diagram ; Nanoclusters ; Molecular dynamics ; Nucleation ; Silicon carbide ; Homogeneous ; Decomposition ; Phase (matter) ; Deposition (geology) Machine Learning in Materials Science ; Silicon Carbide Semiconductor Technologies ; Boron and Carbon Nanomaterials Research
Authors: Gunjan R. Ramteke | K. V. Jovan Jose
Source: Journal of Chemical Information and Modeling (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Local-to-Global Optimization Strategy for Accelerated Molecular Cluster Structure Prediction We present a machine learning-assisted local-to-global optimization strategy (LOGOS) for predicting stable molecular cluster structures on potential energy surfaces (PESs). This model facilitates the construction of molecular clusters by identifying localized patterns using deep learning, thereby enhancing the structure-building process. LOGOS predicts new plausible binding sites for a parent structure, and the geometry-optimized daughter structure is subsequently generated in the molecular electrostatic potential (MESP)-topography feature space. This approach is benchmarked by building various ground-state clusters of a prototype (CO<sub>2</sub>)<sub><i>n</i></sub> with <i>n</i> < 30, which are well corroborated by the minimum-energy structures reported in the literature. Therefore, the LOGOS approach holds great promise for accelerating the structure search and prediction of large molecular clusters, guided by the learned topology of smaller clusters. Overall, LOGOS provides a practical solution for efficiently identifying energetically favorable cluster structures in a hierarchical manner. The LOGOS approach, leveraging deep learning algorithms, helps to understand complex patterns within molecular clusters and efficiently predict daughter clusters with minimal computational cost and minimal loss of chemical accuracy. Cluster (spacecraft) ; Feature (linguistics) ; Topology (electrical circuits) ; Logos Bible Software ; Molecular dynamics ; Energy (signal processing) ; Process (computing) ; Molecular cluster Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Leonardo Medrano Sandonas | David Balcells | Anton Bochkarev | Jacqueline M. Cole | Volker L. Deringer | Werner Dobrautz | Adrian Ehrenhofer | Thorben Frank | Pascal Friederich | Rico Friedrich | Janine George | Luca M. Ghiringhelli | Alejandra Hinostroza Caldas | Veronika Jurásková | Hannes Kneiding | Yury Lysogorskiy | Johannes T. Margraf | Hanna Türk | Anatole von Lilienfeld | Milica Todorović | Alexandre Tkatchenko | Mariana Rossi | Gianaurelio Cuniberti
Source: arXiv (Cornell University) (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Perspective: Towards sustainable exploration of chemical spaces with machine learning Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics. Bridging (networking) ; Benchmarking ; Big data ; Sustainability ; Resource (disambiguation) ; Resource efficiency Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Yanxiao Hu | Ye Sheng | Caichao Ye | Wenxing Qian | Xiaoxin Xu | Yabei Wu | Jiong Yang | William A. Goddard III | Wenqing Zhang
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Material-Property-Field-based Deep Neural Network in Hopfield Framework The current Deep Neural Networks (DNNs) lack the necessary physical priors and a clear formulation specifically designed for material systems. making them non-analytical and non-interpretable 'black boxes'. In this work, we integrate Material Property Fields (MPF) with the Hopfield network architecture and propose an analytical DNN framework named mPFDNN. MPF provides a unified framework that represents physical properties of materials as an analytical field built upon pairwise interactions, rigorously respecting fundamental symmetries, while also enabling a physically legitimate decomposition of property distributions at the atomic level. Although the Hopfield model was initially developed for Ising-like systems, we prove that its dynamical evolution strategy for DNN design is equally well suited to MPF. By mathematically reformatting interatomic nonlinear interactions as 'hidden neurons', the MPF can be naturally evolved into a deep yet analytically tractable DNN architecture that approximates a fully connected interaction landscape. This framework also unifies nonlinear DNNs and linear approaches within a single cohesive model. Extensive validation across diverse systems (inorganic crystals, organic molecules, and aqueous solutions) and multiple properties (diffusion coefficients, adsorption energy, etc.) confirm that mPFDNN not only achieves accurate predictions but also provides a principled and universal framework for structure-property mapping for physical, chemical and materials science. Artificial neural network ; Nonlinear system ; Property (philosophy) ; Pairwise comparison ; Field (mathematics) ; Hopfield network ; Deep neural networks Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Inorganic Chemistry and Materials