jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tadashi Ogitsu
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Elemental Processes in Nanoscale Electrocatalytic Interfaces for Photocatalytic and Photoelectrochemical Water Splitting Advancing next-generation electrochemical systems for energy conversion requires a comprehensive understanding of the coupled processes of charge carrier generation, electron and ion transport, and interfacial electrochemical reactions. In this work, we integrate ab initio and machine learning-enhanced molecular dynamics simulations to investigate electrocatalytic water oxidation and selective species transport in photoelectrochemical and photocatalytic systems. For halide perovskite photoabsorbers with long carrier lifetimes, we demonstrate that spontaneous polarization domains and lattice dynamics govern charge separation and carrier dynamics. In colloidal suspension-type photocatalytic systems, we examine how the chemistry and geometry of protective oxide coatings influence the transport of reactants and redox shuttles through nanopores, revealing mechanisms of selective permeation that are potentially critical to Z-scheme photocatalysis. Furthermore, grand-canonical DFT simulations on IrO₂ surfaces under varying potentials reveal that surface hydrogen coverage directly modulates oxygen evolution reaction (OER) barriers and Tafel behavior. A continuum microkinetic model incorporating voltage-dependent surface coverage and active site accessibility successfully reproduces experimental current-voltage characteristics. Together, these findings highlight the synergistic effects of electrostatics, solvation, and surface chemistry on electrocatalytic performance, providing a rational framework for catalyst–overlayer interface design in water splitting and related reactions. This work was partially performed under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 and supported by the U.S. DOE, Office of Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office as well as Office of Science, Basic Energy Science Program. Water splitting ; Charge carrier ; Photocatalysis ; Photoelectrochemical cell ; Oxide ; Electrochemical energy conversion ; Hydrogen fuel ; Electrochemistry ; Oxygen evolution Machine Learning in Materials Science ; Iron oxide chemistry and applications ; Electrocatalysts for Energy Conversion
Authors: Yixi Zhang | Junfan Xia | Yaolong Zhang | Bin Jiang
Source: Chinese Journal of Chemical Physics (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

REANN 2.0: An efficient package of neural network potentials for multi-element systems Recursively embedded atom neural network (REANN) is a general-purpose atomistic machine learning software package for representing potential energy and other physical properties. The original REANN 1.0 architecture is a physically inspired invariant message passing neural network, which was designed for systems with a limited number of elements. It is efficient but hardly transferable to more complex multielement systems. In this work, we release REANN 2.0 aimed at multi-element systems and universal potentials, which integrates element embedding and equivariant representation. Compared to the first version, REANN 2.0 demonstrates enhanced element transferability and higher accuracy across various periodic systems with higher efficiency. Built upon this framework, a pre-trained REANN-MPtrj model without fine-tuning accurately predicts the lithium-ion diffusion dynamics in a benchmark solid-state electrolyte Li3YCl6. We hope this open-source software package will facilitate the development of computationally efficient universal potentials in the future. Benchmark (surveying) ; Artificial neural network ; Software ; Invariant (physics) ; Embedding ; Transferability ; Software package ; Energy (signal processing) ; Physical system Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Electrocatalysts for Energy Conversion
Authors: Ming Xuan
Source:  (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

SchNet++: A Novel Framework Based on Dynamic Cutoff Radius &amp; Multi-head Prediction for Molecule Energy Prediction Deep learning has revolutionized quantum materials research by enabling highly accurate predictions of atomic and molecular properties. However, the predictive performance of these models has been fundamentally limited by architectural constraints, particularly in capturing complex quantum interactions and maintaining physical consistency. These limitations motivate the development of more sophisticated frameworks that better integrate physical principles with data-driven approaches. In SchNet++, this work addresses a critical bottleneck in molecular modeling—the static cutoff radius—by reintroducing a discretized convolutional neural network (DCNN) architecture to capture finer spatial interactions at atomic scales. This approach enables a more granular representation of local atomic environments, overcoming the limitations of fixed-distance cutoffs. Additionally, the robustness of SchNet++ is significantly enhanced through a novel multi-head prediction mechanism. By integrating these advancements, SchNet++ achieves superior performance in capturing both short- and long-range interactions. SchNet++ achieves 61.3% reduction in MAE, compared with SchNet, and state-of-the-art performance in SchNet series, demonstrating the effectiveness of the improvements. Bottleneck ; Robustness (evolution) ; Cutoff ; Quantum ; Convolutional neural network ; Representation (politics) ; Discretization ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Yangjun Qin | Zhicheng Zong | Junwei Che | Tianhao Li | Haisheng Fang | Nuo Yang
Source: ArXiv.org (preprint, 2025-01-21)
Field: Materials Science — Materials Chemistry

Unveiling the thermal transport mechanism in compressed plastic crystals assisted by deep potential The unique properties of plastic crystals highlight their potential for use in solid-state refrigeration. However, their practical applications are limited by thermal hysteresis due to low thermal conductivity. In this study, the effect of compressive strain on the thermal transport properties of plastic crystal [(CH3)4N][FeCl4] was investigated using molecular dynamic simulation with a deep neural network potential. It is found that a 9% strain along [001] direction enhances thermal conductivity sixfold. The underlying mechanisms are analyzed through vibrational density of states, spectral energy densities, and mean square displacements. The enhancement in thermal conductivity is primarily due to increased group velocity and reduced phonon scattering, driven by volume compression within the 0-1 THz. These findings offer theoretical insights for the practical application of plastic crystals in thermal management systems. Mechanism (biology) ; Thermal ; Materials science ; Thermodynamics ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; Adhesion, Friction, and Surface Interactions
Authors: Musa Husaini
Source: Journal of Applied Sciences and Environmental Management (article, 2025-12-26)
Field: Chemistry — Inorganic Chemistry

Recent Computational Innovations for Understanding Adsorption Dynamics in Nanoporous Materials Understanding adsorption dynamics within nanoporous materials plays a pivotal role in optimizing applications such as gas separation, energy storage, and catalysis. Recent advances in computational methodologies have significantly enhanced our ability to simulate adsorption behavior at multiple length and time scales with improved precision. This review comprehensively examines the latest developments in theoretical frameworks, classical molecular simulations, machine learning algorithms, and multiscale hybrid approaches that together offer deeper insights into both equilibrium and dynamic adsorption processes. Special attention is given to the effects of structural heterogeneity, framework flexibility, and solvent interactions, as well as the increasing integration of computational results with experimental validations. Emerging technologies, including artificial intelligence and quantum computing, promise to address existing challenges related to accuracy and computational cost, accelerating the discovery of novel nanoporous materials. This review aims to provide a foundation for future research efforts focused on designing materials with tailored adsorption properties and enhanced performance Nanoporous ; Adsorption ; Molecular dynamics ; Foundation (evidence) ; Computational model ; Process (computing) Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis
Authors: Sungyong Park | Sungwoo Park | Jinhee Lee
Source: Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Predicting global warming potential: A self-training and ensemble machine learning approach Amid growing concerns about global warming, a critical challenge for the semiconductor industry is the development of materials with low global warming potential (GWP). However, determining GWP is complicated for newly developed compounds, making the assessment of their environmental impact difficult. Instead of relying on experimental determination, Chung’s group pioneered the use of machine learning to predict GWP values [G. Zhao, H. Kim, C. Yang, and Y. G. Chung, J. Phys. Chem. A 128, 2399 (2024)]. In this study, we propose a machine learning approach named ST-GWP modeling, which combines self-training with an ensemble learning technique to improve prediction accuracy. The model achieved an R2 of 0.97 on the training set and 0.92 on the test set in the regression task. Specifically, in the classification task, our model achieved an accuracy of 0.99 on the training set and 0.96 on the test set, demonstrating robust generalization capabilities. Furthermore, the analysis suggests that GWP is influenced by various molecular properties, including the number of halogen atoms and the distribution of electrons within the molecule, such as polarity, polarizability, and partial charge. Generalization ; Test set ; Ensemble learning ; Training set ; Set (abstract data type) ; Global warming ; Regression ; Ensemble forecasting Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Chun-I Wang | Aidan C. Lindsay | Nicholas E. Jackson
Source: APL Computational Physics (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Machine learning intermolecular electronic couplings: Opportunities and challenges Intermolecular electronic couplings (IECs) play a critical role in theories of charge and excitation transfer. However, the considerable computational cost of evaluating IECs limits the exploration of large molecular systems and long timescales key to numerous molecular and materials applications. Recent advances in machine learning (ML) offer a means of accelerating IEC predictions, enabling previously intractable calculations for large disordered systems and their optoelectronic properties. In this pedagogical review, we summarize recent progress in applying ML to predict IECs and leverage a case study of IEC prediction applied to a canonical molecular dopant and organic semiconductor system to illustrate conformational data sampling, feature engineering, and ML algorithms. For each step, we discuss the existing challenges and outline future research directions to improve the accuracy, interpretability, and transferability of ML models for IEC predictions. Leverage (statistics) ; Transferability ; Intermolecular force ; Key (lock) ; Charge (physics) ; Semiconductor Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds ; Perovskite Materials and Applications
Authors: Ying Zhou | James A. Dawson
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Elucidating Oxide-Ion and Proton Transport in Next-Generation Ionic Conductors Using Machine Learning Potentials It is imperative to have highly conductive oxide ion and proton conductors that operate at intermediate temperatures to improve the performance of ceramic fuel cells. The crystal structures of these materials significantly influence their ionic conduction properties. Identifying novel materials with enhanced conductive abilities is a crucial research focus, promising substantial advancements in future fuel cell technologies. In pursuing this goal, we focus on advanced perovskite solid electrolytes and their interfaces. Employing atomistic simulations, we aim to unravel the complexities of these materials, gaining a detailed understanding of ion transport mechanisms crucial for optimising electrolyte performance and efficiency. Our approach integrates the predictive capabilities of density functional theory with the dynamic nature of molecular dynamics, supplemented by machine-learning-based potentials for a thorough analysis. To address larger system sizes and extended time scales in our modelling, we utilise machine learning potentials, specifically moment tensor potentials (MTPs) [1]. The crucial potential fitting process relies on energies, forces, and stresses obtained from diverse configurations through ab initio calculations [2]. Our study focuses on the systems Sr 3 V 2 O 8 and Ba 7 Nb 4 MoO 20 , and our employed MTPs, fitted using active learning, exhibit excellent agreement with data from ab initio molecular dynamics (AIMD). Notably, oxygen and protons' diffusion coefficients, along with the conductivity of oxygen ions and protons, show commendable consistency with both AIMD simulations and experimental data. This alignment underscores the accuracy and reliability of our MTPs in capturing the intricate dynamics of these materials, reinforcing their suitability for modelling and predicting the behaviour of Sr 3 V 2 O 8 and Ba 7 Nb 4 MoO 20 . Reference Shapeev, A.V., 2016. Multiscale Modeling &amp; Simulation, 14(3), pp.1153-1173. B. Grabowski, Y. Ikeda, P. Srinivasan, F. Körmann,C. Freysoldt, A. I. Duff, A. Shapeev, J. Neugebauer,npj Computational Materials5 (1) (2019) 1–6. Ionic bonding ; Proton ; Ion ; Electrical conductor ; Oxide ; Materials science ; Computer science ; Chemistry ; Inorganic chemistry ; Chemical physics ; Nanotechnology ; Physics ; Nuclear physics ; Composite material ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Stef A. H. Jansen | Ghislaine Vantomme | E. W. Meijer
Source: Angewandte Chemie (article, 2025-08-22)
Field: Materials Science — Biomaterials

Evolving Data‐Driven Strategies for the Characterization of Supramolecular Polymers and Systems Abstract Inspired by the dynamic assembly of fibrillar proteins in biology, research on supramolecular polymers has progressed rapidly toward the development of synthetic multicomponent systems. In this review, we highlight recent advances in the study of supramolecular polymers in solution, with an emphasis on how combined computational and experimental approaches deepen our understanding of these systems. In particular, these studies have elucidated the mechanisms of protein aggregation and provided insights into the characteristics of synthetic systems. We discuss the classification of these polymers and systems, highlighting how their different interaction modes and microstructures give rise to diverse structural and functional properties. In addition, we outline the emerging role of machine learning as a powerful tool to navigate the inherent complexity of these systems, thereby enhancing strategies for rational design and characterization. This review highlights how computational approaches, from traditional modeling to emerging machine learning techniques, enable the experimental characterization and understanding of supramolecular polymer chemistry. Supramolecular chemistry ; Characterization (materials science) ; Polymer ; Supramolecular polymers ; Nanotechnology ; Chemistry ; Materials science ; Combinatorial chemistry ; Organic chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Polydiacetylene-based materials and applications
Authors: Yeyong Yu | Xuecheng Bian | Jie Xiong | Xing-Gang Wu | Quan Qian
Source: ArXiv.org (preprint, 2025-06-17)
Field: Materials Science — Materials Chemistry

AIMatDesign: Knowledge-Augmented Reinforcement Learning for Inverse Materials Design under Data Scarcity With the growing demand for novel materials, machine learning-driven inverse design methods face significant challenges in reconciling the high-dimensional materials composition space with limited experimental data. Existing approaches suffer from two major limitations: (I) machine learning models often lack reliability in high-dimensional spaces, leading to prediction biases during the design process; (II) these models fail to effectively incorporate domain expert knowledge, limiting their capacity to support knowledge-guided inverse design. To address these challenges, we introduce AIMatDesign, a reinforcement learning framework that addresses these limitations by augmenting experimental data using difference-based algorithms to build a trusted experience pool, accelerating model convergence. To enhance model reliability, an automated refinement strategy guided by large language models (LLMs) dynamically corrects prediction inconsistencies, reinforcing alignment between reward signals and state value functions. Additionally, a knowledge-based reward function leverages expert domain rules to improve stability and efficiency during training. Our experiments demonstrate that AIMatDesign significantly surpasses traditional machine learning and reinforcement learning methods in discovery efficiency, convergence speed, and success rates. Among the numerous candidates proposed by AIMatDesign, experimental synthesis of representative Zr-based alloys yielded a top-performing BMG with 1.7GPa yield strength and 10.2\% elongation, closely matching predictions. Moreover, the framework accurately captured the trend of yield strength variation with composition, demonstrating its reliability and potential for closed-loop materials discovery. Reinforcement learning ; Reliability (semiconductor) ; Stability (learning theory) ; Matching (statistics) ; Function (biology) ; Domain (mathematical analysis) ; Convergence (economics) ; Structured prediction Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; MXene and MAX Phase Materials
Authors: Shaoda Zhao | Junfeng Wang | Zhixiong Wang
Source:  (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

Exploration on optimization of synthetic routes for thermal stability of functional materials This paper constructs a thermal stability synthesis path optimization scheme that integrates multi-objective optimization and deep learning modeling, and theoretically analyzes the influence mechanism of grain structure, interface state, and defect evolution on thermal stability at multiple scales; then, with the help of orthogonal design and response surface method, a synthesis parameter space model is built, and NSGA-II and DNN algorithms are used to achieve comprehensive optimization screening of the "process-structure-performance" path; at the experimental level, in-situ high-temperature XRD, TEM and Raman techniques are used to verify the microstructure regulation effect, and TG-MS, DMA and thermal shock experiments are used to evaluate the actual effect of thermal stability improvement. The materials under the optimized path are better than the control group in terms of pyrolysis temperature, modulus retention rate and thermal cycle life, and show obvious performance advantages in various engineering applications. The research results show that this method can provide theoretical and technical support for the intelligent synthesis of high-performance thermally stable materials. Stability (learning theory) ; Thermal stability ; Thermal ; Path (computing) ; Surface (topology) ; Microstructure ; Shock (circulatory) ; Thermal shock Machine Learning in Materials Science ; Thermal properties of materials ; Topology Optimization in Engineering
Authors: Hyeonbin Moon | Songho Lee | Wabi Demeke | Byungki Ryu | Seunghwa Ryu
Source: Research Square (preprint, 2025-06-23)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties Inference ; Thermoelectric effect ; Artificial neural network ; Physics ; Statistical physics ; Artificial intelligence ; Computer science ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Neural Networks and Applications
Authors: Khalil Benabderazag | Moussa Guebailia | Zouheyr Belouadah | Lotfi Toubal | Salah Eddine Tachi
Source: Preprints.org (preprint, 2025-01-28)
Field: Materials Science — Polymers and Plastics

Machine Learning for Identifying Damage and Predicting Properties in 3D-Printed PLA/Lygeum spartum Biocomposites This paper offers an experimental approach to use machine learning (ML) models and Acoustic Emission (AE) data for the identification of damage mechanisms and predict the mechanical properties of 3D printed bio-composite. The specimens were produced using a bio-filament made of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers. AE signals were gathered during tensile and flexural tests in order to monitor the progression of damage under mechanical loading. subsequently, using Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Network (ANN), and Decision Tree (DT) models, the stress levels of the specimens under both test conditions were predicted. These algorithms were trained with 80% of the data, in a Python environment. 20% remained, to be used for testing. The models' accuracy was assessed using R-squared (R&amp;sup2;) and Mean Squared Error (MSE) metrics. While the other models also demonstrated outstanding prediction capabilities for both tensile and flexural stresses, the RFR model outperformed the others. In addition, 5-fold cross-validation yielded results consistent with the hold-out test, further validating the models' accuracy. This research demonstrates how well these machine learning algorithms analyze AE data for material property evaluation, paving the way for data-driven approaches in material testing and health monitoring. Random forest ; Artificial neural network ; Python (programming language) ; Machine learning ; Ultimate tensile strength ; Support vector machine ; Artificial intelligence ; Acoustic emission ; Mean squared error ; Test data ; Decision tree ; Flexural strength ; Computer science ; Materials science ; Structural engineering ; Composite material ; Mathematics ; Engineering ; Statistics Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials