jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhipeng Sun | Yinan Wang | Wenjie Li | Xi Qiu | Ben Xu | Xiaoyang Wang
Source: Computational Materials Science (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

A high accuracy machine-learning potential model for Mo-Re binary alloy Molybdenum is a promising candidate material for advanced nuclear reactors . However, its application in nuclear energy facilities is limited by its intrinsic brittleness , a common characteristic of body-centered cubic transition metals, which often exhibit poor plasticity and workability. The addition of Re to Mo can exploit the “Re softening effect” to enhance plasticity. To better understand the physical origin of this effect and explore the nanoscale atomistic mechanisms in Mo-Re alloys under service conditions, atomic-scale simulation methods, such as molecular dynamics (MD), are widely used as a complementary theoretical tool to experimental studies. However, the reliability of MD simulations is constrained by the limitations of existing empirical interatomic potentials . To address this challenge, this study employs state-of-the-art deep-potential methods to develop a machine learning-based interatomic potential for Mo-Re alloys. This advanced potential model achieves first-principles accuracy across a wide range of material properties , including elastic constants , surface energies, point defects , dislocations, and melting points, within a single potential. It enables high-accuracy atomic-scale simulations and investigations into the microstructural evolution of Mo-Re alloys under complex multi-field coupling conditions (irradiation, heat, and stress), which will establish the theoretical foundation for understanding the Re softening effect. • A accurate machine-learning interatomic potential for Mo-Re alloy. • Accurate in a wide range of basic, surface, and defect properties. • Highly generalizable to predict properties that are not training targets. • Suitable for investigation of the Re softening effect in Mo-Re alloy during service. Alloy ; Binary number ; Materials science ; Binary alloy ; Metallurgy ; Thermodynamics ; Computer science ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Seokhyun Choung | Miyeon Kim | Jinuk Moon | Jeong Woo Han
Source: ACS Energy Letters (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

From Atomic Motif to Realistic Single Atom Catalysts through Machine Learning Interatomic Potentials Metal–nitrogen–carbon (M-N-C) catalysts demonstrate exceptional electrochemical performance, with density functional theory (DFT) simulations successfully guiding atomic-scale optimization of coordination environments. However, recent experiments reveal that catalyst performance depends on phenomena beyond DFT’s spatiotemporal limits. This Perspective examines how machine learning interatomic potentials (MLIPs) bridge this critical gap, achieving orders-of-magnitude acceleration while maintaining near-DFT accuracy. MLIPs capture previously inaccessible phenomena spanning atomic to mesoscopic scales, including structural complexity and electrolyte dynamics. These capabilities reveal how support architecture, collective site interactions, solvation, and reaction kinetics at the mesoscale determine rate-limiting steps in electrochemical reactions. By connecting atomic-level understanding to experimentally relevant scales, MLIPs transform catalyst design from isolated site optimization to comprehensive multiscale engineering. Mesoscopic physics ; Catalysis ; Molecular dynamics ; Density functional theory ; Atom (system on chip) ; Interatomic potential ; Atomic units Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Jinuk Moon | Uchan Jeon | Seokhyun Choung | Jeong Woo Han
Source: Cell Reports Physical Science (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

CatBench framework for benchmarking machine learning interatomic potentials in adsorption energy predictions for heterogeneous catalysis <h2>Summary</h2> The rapid advancement of machine learning interatomic potentials (MLIPs) has opened new avenues for accelerating computational catalysis research, particularly in adsorption energy predictions—a key descriptor that efficiently correlates with catalytic activity and selectivity. While density functional theory remains the gold standard for first-principles calculations, its computational cost limits large-scale applications. MLIPs accelerate adsorption energy prediction but require rigorous validation to ensure accuracy. Here we report CatBench, a benchmarking framework designed to systematically evaluate the adsorption energy prediction performance of MLIPs. By applying CatBench to extensive adsorption reaction datasets including both small- and large-molecule adsorption, we analyze the predictive capabilities of widely used universal MLIPs (uMLIPs). The benchmarking results provide a comprehensive comparison of uMLIPs, offering critical insights into the practical use of these models for catalysis research. This work highlights the importance of systematic benchmarking in the adoption of machine learning-driven approaches to catalytic system modeling. Benchmarking ; Adsorption ; Work (physics) ; Heterogeneous catalysis ; Energy (signal processing) ; Catalysis Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Kohei Yoshida | Yusuke Kanematsu | David S. Rivera Rocabado | Takayoshi Ishimoto
Source: Computational Materials Science (article, 2025-01-22)
Field: Materials Science — Polymers and Plastics

Integrated approach of coarse-grained molecular dynamics calculations and machine learning for understanding mechanical properties of filler-filled polymer models Filler (materials) ; Molecular dynamics ; Polymer ; Materials science ; Dynamics (music) ; Composite material ; Chemical physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Composite Material Mechanics
Authors: Ethan Berger | Mohammad Bagheri | Hannu‐Pekka Komsa
Source: Small (article, 2025-08-03)
Field: Materials Science — Materials Chemistry

Screening of Material Defects using Universal Machine‐Learning Interatomic Potentials Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling. Recently, such screening has been dramatically accelerated by the invent of universal machine-learning interatomic potentials that offer first principles accuracy at orders of magnitude lower computational cost. Their application to the screening of defects with desired properties or to finding new stable compounds with high density of defects, however, has not been explored. Here, it is shown that the universal machine-learning interatomic potentials have reached sufficient accuracy to enable large-scale screening of defective materials. Vacancy calculations are carried out for 86,259 materials in the Materials Project database and the formation energies analyzed in terms of oxidation numbers. The application of these models is further demonstrated for finding new materials at or below the convex hull of known materials and for simulated etching of low-dimensional materials. Interatomic potential ; Convex hull ; Vacancy defect ; Set (abstract data type) ; Computer science ; Materials science ; Regular polygon ; Statistical physics ; Molecular dynamics ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Condensed matter physics ; Geometry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Chenbo Zhang | Xian Chen
Source: npj Computational Materials (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

FerroAI: a deep learning model for predicting phase diagrams of ferroelectric materials Composition-temperature phase diagrams are crucial for designing ferroelectric materials, however predicting them accurately remains challenging due to limited phase transformation data and the constraints of conventional methods. Here, we utilize natural language processing (NLP) to text-mine 41,597 research articles, compiling a dataset of 2838 phase transformations across 846 ferroelectric materials. Leveraging this dataset, we develop FerroAI, a deep learning model for phase diagram prediction. FerroAI successfully predicts phase boundaries and transformations among different crystal symmetries in Ce/Zr co-doped BaTiO3 (BT)-xBa0.7Ca0.3TiO3(BCT). It also identifies a morphotropic phase boundary in Zr/Hf co-doped BT-xBCT at x = 0.3, guiding the discovery of a new ferroelectric material with an experimentally measured dielectric constant of 11,051. These results establish FerroAI as a powerful tool for phase diagram construction, guiding the design of high-performance ferroelectric materials. Ferroelectricity ; Phase diagram ; Phase (matter) ; Materials science ; Computer science ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Titanium Alloys Microstructure and Properties
Authors: Abrar U. Hassan | Cihat Güleryüz | Sajjad Hussain Sumrra | Sadaf Noreen | M.H.H. Mahmoud
Source: Organic Electronics (article, 2025-02-22)
Field: Materials Science — Polymers and Plastics

A rapid UV/Vis assisted designing of benzodithiophene based polymers by machine learning to predict their light absorption for photovoltaics Photovoltaics ; Absorption (acoustics) ; Polymer ; Materials science ; Organic solar cell ; Photochemistry ; Optoelectronics ; Nanotechnology ; Chemistry ; Photovoltaic system ; Engineering ; Composite material ; Electrical engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Rongzhi Gao | ChiYung Yam | Jianjun Mao | Shuguang Chen | GuanHua Chen | Ziyang Hu
Source: Nature Communications (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

A foundation machine learning potential with polarizable long-range interactions for materials modelling Long-range interactions are essential determinants of chemical system behavior across diverse environments. We present a foundation framework that integrates explicit polarizable long-range physics with an equivariant graph neural network potential. It employs a physically motivated polarizable charge equilibration scheme that directly optimizes electrostatic interaction energies rather than partial charges. The foundation model, trained across the periodic table up to Pu, demonstrates strong performance across key materials modeling challenges. It effectively captures long-range interactions that are challenging for traditional message-passing mechanisms and accurately reproduces polarization effects under external electric fields. We have applied the model to mechanical properties, ionic diffusivity in solid-state electrolytes, ferroelectric phase transitions, and reactive dynamics at electrode-electrolyte interfaces, highlighting the model's capacity to balance accuracy and computational efficiency. Furthermore, we show that as a foundation model, it can be efficiently finetuned to achieve high-level accuracy for specific challenging systems. Polarizability ; Foundation (evidence) ; Polarization (electrochemistry) ; Artificial neural network ; Scalability ; Graph ; Charge (physics) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Block Copolymer Self-Assembly
Authors: Yanbo Han | Mengyang Li | Masahiro Ehara | Xiang Zhao
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Unveiling fullerene formation and interconversion through molecular dynamics simulations with deep neural network potentials Utilizing deep neural network potentials within molecular dynamics simulations, this research uncovers insights into fullerene formation and interconversion, particularly during the cooling stage of the annealing process. Our deep learning-enhanced approach effectively models the generation of fullerenes from C<sub>2</sub> units in carbon vapor, highlighting the critical role of carbon density in structuring outcomes in a primary iron-carbon system. This study provides differences in molecular dynamics simulations for fullerene generation and also demonstrates the potential of deep learning in investigating complex carbon structures, paving the way for further investigations into the fullerene family. Fullerene ; Molecular dynamics ; Chemical physics ; Chemistry ; Dynamics (music) ; Computational chemistry ; Nanotechnology ; Materials science ; Physics ; Organic chemistry Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Nuclear Physics and Applications
Authors: Xiaoyu Hou | Huijia Lu | Xiaorong Wang | Zongbao Yu | Changdong Chen | Kang Xu | Hao Wu | Yuanchao He | Bin Pan
Source: Journal of Water Process Engineering (article, 2025-03-22)
Field: Materials Science — Materials Chemistry

Predicting photocatalytic degradation efficiency of rhodamine: A machine learning-based model for composite catalyst screening Degradation (telecommunications) ; Photocatalysis ; Rhodamine B ; Composite number ; Catalysis ; Rhodamine ; Chemical engineering ; Photochemistry ; Chemistry ; Materials science ; Computer science ; Composite material ; Organic chemistry ; Engineering ; Physics ; Optics ; Telecommunications ; Fluorescence Machine Learning in Materials Science ; Water Quality Monitoring and Analysis
Authors: Shuhao Zhang | Michael Chigaev | Olexandr Isayev | Richard A. Messerly | Nicholas Lubbers
Source: Journal of Chemical Information and Modeling (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and a relatively low computational cost. However, a common and unaddressed challenge with many current neural network (NN) MLIP models is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which appear in various reactive processes. To address this limitation, we present a mathematical technique for modifying any existing atom-centered NN architecture to account for the energies of isolated atoms. The result produces a consistent prediction of the atomization energy (AE) of a system using minimal constraints on the model. Using this technique, we build a model architecture that we call hierarchically interacting particle neural network (HIP-NN)-AE, an AE-constrained version of the HIP-NN, as well as ANI-AE, the AE-constrained version of the accurate NN engine for molecular energies (ANI). Our results demonstrate AE consistency of AE-constrained models, which drastically improves the AE predictions for the models. We compare the AE-constrained approach to unconstrained models as well as models from the literature in other scenarios, such as bond dissociation energies, bond dissociation pathways, and extensibility tests. These results show that the constraints improve the model performance in some of these tasks and do not negatively affect the performance on any tasks. The AE constraint approach thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks. Artificial neural network ; Reactive intermediate ; Statistical physics ; Chemistry ; Nanotechnology ; Theoretical physics ; Physics ; Computer science ; Artificial intelligence ; Materials science ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Chunmei Tang | Baoyin Yuan | Xinyi Xie | Yoshitaka Aoki | Ning Wang | Siyu Ye
Source: Energy Materials (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Machine learning-assisted advances and perspectives for electrolytes of protonic solid oxide fuel cells Protonic solid oxide fuel cells (P-SOFCs), as a promising power generation technology, have garnered increasing attention due to their advantages of cleanliness, high efficiency, and high reliability. As a critical component of P-SOFCs, proton-conducting electrolytes exhibit high ionic conductivity, enabling high chemical-to-electrical energy conversion efficiency at intermediate temperatures. However, there are still many challenges in further enhancing the proton conductivity and stability of the currently widely used Ba(Zr, Ce)O3 electrolytes through traditional experimental methods. Herein, this review firstly summarized the current research status of proton-conducting oxides, including ABO3 perovskite-type oxides and other structural oxides, and highlighted the challenges faced by electrolyte development in terms of proton conductivity, compatibility with other components, and long-term durability. Then, the relevant progress of machine learning (ML) in the research of P-SOFC electrolytes was meticulously discussed and the promising applications of ML in proton-conducting electrolyte performance screening, stability prediction, and morphology analysis were pointed out. More importantly, the challenges and solutions of proton-conducting electrolytes designed by ML were uncovered by considering the reliable database, feature engineering, accurate model, and experimental validation. Overall, this review concluded the advances of ML-assisted P-SOFC electrolytes and addressed the future research directions in the synergy of ML and electrolytes. Oxide ; Fuel cells ; Electrolyte ; Chemical engineering ; Materials science ; Fast ion conductor ; Nanotechnology ; Chemistry ; Engineering ; Metallurgy ; Physical chemistry ; Electrode Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Joe G. Greener
Source: Proceedings of the National Academy of Sciences (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Reversible molecular simulation for training classical and machine-learning force fields The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine-learning potentials. Differentiable molecular simulation calculates gradients of observables with respect to parameters through molecular dynamics trajectories. Here, we improve this approach by explicitly calculating gradients using a reverse-time simulation with effectively constant memory cost and a computation count similar to the forward simulation. The method is applied to learn all-atom water and gas diffusion models with different functional forms and to train a machine-learning potential for diamond from scratch. Comparison to ensemble reweighting indicates that reversible simulation can provide more accurate gradients and train to match time-dependent observables. Molecular dynamics ; Computer science ; Observable ; Computation ; Differentiable function ; Force field (fiction) ; Scratch ; Constant (computer programming) ; Statistical physics ; Artificial intelligence ; Simulation ; Algorithm ; Physics ; Mathematics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties
Authors: Wei Feng | Songqi Zhang | Yanfei Xu | Xiangfei Ye | Xiaoyong Xu | Liquan Wang | Jiaping Lin | Chunhua Cai | Lei Du
Source: Chinese Journal of Polymer Science (article, 2025-09-02)
Field: Materials Science — Polymers and Plastics

Machine-learning-assisted Materials Genome Approach for Designing High-performance Thermosetting Polyimides Thermosetting polymer ; Materials science ; Composite material ; Computer science Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Injection Molding Process and Properties
Authors: Simon Gramatte | O. Politano | N. Jakse | Claudia Cancellieri | Ivo Utke | Lars P. H. Jeurgens | Vladyslav Turlo
Source: npj Computational Materials (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling Advancing hydrogen-based technologies requires detailed characterization of hydrogen chemical states in amorphous materials. As experimental probing of hydrogen is challenging, interpretation in amorphous systems demands accurate structural models. Guided by experiments on atomic layer deposited alumina, a fast atomistic simulation technique is introduced using an ab initio-based machine learning interatomic potential to generate amorphous structures with realistic hydrogen contents. As such, the annealing of highly defective crystalline hydroxide structures at atomic layer deposition temperatures reproduces experimental density and structure, enabling accurate prediction of Al Auger parameter chemical shifts. Our analysis shows that higher hydrogen content favors OH ligands, whereas lower hydrogen content leads to diverse chemical states and hydrogen bonding, consistent with charge density and partial Bader charge calculations. Our approach offers a robust route to link hydrogen content with experimentally accessible chemical shifts, aiding the design of next-generation hydrogen-related materials. Amorphous solid ; Hydrogen ; Materials science ; Annealing (glass) ; Chemical physics ; Ab initio ; Nanotechnology ; Chemistry ; Crystallography ; Metallurgy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Zheng Li | Yan Xu | Mian Cai | Ruxiang Liu | Chang Long | Ze Yang | Yaqun Wang | F. Qiu | Yanru Yang | Huiping Du
Source: Journal of Power Sources (article, 2025-03-27)
Field: Materials Science — Materials Chemistry

A front-end fusion feature-based machine learning engine for rechargeable battery manufacturing accelerates screening of organic electrodes Battery (electricity) ; Electrode ; Front and back ends ; Feature (linguistics) ; Automotive engineering ; Computer science ; Fusion ; Materials science ; Engineering ; Mechanical engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Hamid Zentou | Ali H. Tayeb | Islam M. Tayeb | Mahmoud M. Abdelnaby
Source: Journal of environmental chemical engineering (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Machine learning for predicting and optimizing the CO2 uptake in porous organic polymers Hyperparameter ; Hyperparameter optimization ; Gradient boosting ; Artificial neural network ; Support vector machine ; Boosting (machine learning) ; Adsorption ; Polymer ; Porosity Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Jing Zhang | Tianshu Zhai | Yunrui Yan | Qiyi Fang | Yifeng Liu | Yifan Zhu | Weichao Tu | Chen-yang Lin | Yuguo Wang | Jun Lou
Source: Materials Today Nano (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Scalable mechanical exfoliation of two-dimensional nanosheets by polymer-assisted dry ball-mill of layered materials and insights from machine learning Exfoliation joint ; Ball (mathematics) ; Ball mill ; Materials science ; Mill ; Polymer ; Scalability ; Composite material ; Mechanical engineering ; Computer science ; Engineering ; Nanotechnology ; Mathematics ; Geometry ; Graphene 2D Materials and Applications ; Graphene research and applications ; Conducting polymers and applications