jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fengjun Zhuo | Zhenyu Dai | Kai Chang | Hongxin Yang | Zhenxiang Cheng
Source: Applied Physics Reviews (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Moiré spintronics: Emergent phenomena, material realization and machine learning accelerating discovery Twisted van der Waals (vdW) materials have emerged as a promising platform for exploring exotic quantum phenomena and engineering novel material properties in two dimensions, potentially revolutionizing developments in spintronics. This review provides an overview of recent progress on emerging moiré spintronics in twisted vdW materials, with a particular focus on two-dimensional magnetic materials. Following a brief introduction to the general features of twisted vdW materials, we discuss recent theoretical and experimental studies on stacking-dependent interlayer magnetism, non-collinear spin textures, moiré magnetic exchange interactions, moiré skyrmions, and moiré magnons. We further highlight the potential of machine learning to accelerate the discovery and design of multifunctional materials for moiré spintronics. Finally, we conclude by addressing the most pressing challenges and potential opportunities in this rapidly expanding field. Spintronics ; Realization (probability) ; Focus (optics) ; van der Waals force ; Spin (aerodynamics) 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Chemical and Physical Properties of Materials
Authors: Mauricio Lima Souza | Gisele G. Santos | Daniel R. Cassar | Edgar Dutra Zanotto
Source: Ceramics International (article, 2026-02-01)
Field: Materials Science — Ceramics and Composites

Machine-learning assisted design of lead-free niobium crystal glass with improved optical properties Niobium ; Crystal (programming language) ; Indentation hardness ; Refractive index ; Glass transition ; Lead glass ; High-refractive-index polymer ; Niobium oxide Glass properties and applications ; Structural Analysis of Composite Materials ; Metallic Glasses and Amorphous Alloys
Authors: Marco Fronzi | Michael J. Ford | Kamal Singh Nayal | Olexandr Isayev | Catherine Stampfl
Source: Scientific Reports (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Interpretable machine learning for thermoelectric materials design with Kolmogorov–Arnold networks The discovery of high-performance thermoelectric materials requires models that are both accurate and interpretable. Traditional machine learning approaches, while effective at property prediction, often act as black boxes and provide limited physical insight. In this work, we introduce Kolmogorov–Arnold Networks (KANs) for the prediction of thermoelectric properties, focusing on the Seebeck coefficient and band gap. Compared to multilayer perceptrons (MLPs), KANs achieve comparable predictive accuracy while offering explicit symbolic representations of structure-property relationships. This dual capability enables both reliable predictions and physically interpretable functional forms, providing insight into the governing mechanisms of thermoelectric behaviour. Benchmarking against literature baselines highlights their robustness and generalisability, demonstrating that KANs constitute a practical framework for reverse engineering materials with targeted thermoelectric performance and bridging the gap between predictive power and scientific interpretability. Bridging (networking) ; Perceptron ; Benchmarking ; Thermoelectric effect ; Robustness (evolution) ; Thermoelectric materials ; Seebeck coefficient ; Materials informatics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Quantum many-body systems
Authors: Tomáš Bučko | Katarína Skladanová | Monika Gešvandtnerová | Céline Chizallet | Emeline Sola | Thomas Pigeon | Michaël Badawi
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Isobutanol Dehydration Catalyzed by (100) Surface of Ferrierite: From Static DFT to Machine Learning Accelerated Molecular Dynamics Catalysis ; Selectivity ; Brønsted–Lowry acid–base theory ; Dehydration ; Density functional theory ; Dispersion (optics) ; Reaction mechanism ; Ferrierite ; Reaction rate Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis for Biomass Conversion
Authors: Jianlu Qu | Chaoliang Jia | Yaobin Chen | Haibin Qu | W. Li
Source: AAPS PharmSciTech (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Design and Optimization of Indomethacin Nanocrystals using Machine Learning and Molecular Dynamics Simulations Molecular dynamics ; Nanocrystal ; Dynamics (music) ; Nanoparticle ; Statistical learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yehui Zhang | Jiangxin He | Zhuangzhaung Lai | Chongyi Ling | Qiang Li | Jinlan Wang
Source: The Journal of Physical Chemistry Letters (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated Kinetic Simulations of Surface Reactions with Complex Coverage Effects The presence of multiple adsorbates and their lateral interactions significantly influence catalytic performance, yet accurately simulating these coverage effects remains computationally challenging. We present a machine learning-accelerated kinetic Monte Carlo (ML-kMC) framework to efficiently model coverage-dependent surface reactions. Our approach integrates three key functions: an automated site-encoding scheme for generating representative surface configurations, structure descriptors for structural characterization, and specialized machine learning models for stability and energy predictions. The Gaussian Process Classifier achieves outstanding stability prediction performance (AUC = 98.93%), and Bayesian Ridge Regression delivers accurate energy predictions with a mean absolute error of 0.04 eV. Taking CO oxidation on Pd(111) as a model system, our ML-kMC simulations successfully reproduce experimental observations including oxygen redistribution under CO coadsorption and the characteristic volcano-shaped activity trend. Further analysis reveals that the reaction rate is strongly influenced by the spatial distribution and local coverage of surface O species, highlighting the essential role of adsorbate-adsorbate interactions. This work provides an efficient and generalizable computational framework for incorporating coverage effects into catalytic simulations, offering valuable insights into the microscopic mechanisms of surface reactions. Monte Carlo method ; Kinetic energy ; Gaussian ; Stability (learning theory) ; Bayesian probability ; Kriging ; Gaussian process ; Work (physics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Gonçalo V. S. M. Carrera | Carlos E. S. Bernardes | Ana V. M. Nunes | Teresa Casimiro | João Sotomayor | Ana Aguiar‐Ricardo
Source: Journal of Molecular Modeling (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

The use of an interpretable machine-learning approach to disclose the molecular assemblage on melting transition Assemblage (archaeology) ; Set (abstract data type) ; Random forest ; Causality (physics) ; Transition (genetics) ; Point (geometry) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Protein Structure and Dynamics
Authors: Côme Cattin | Thomas Plé | Olivier Adjoua | Nicolaï Gouraud | Louis Lagardère | Jean‐Philip Piquemal
Source: The Journal of Physical Chemistry Letters (preprint, 2026-01-21)
Field: Materials Science — Materials Chemistry

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models Using Multiple Time Steps and Distillation We present a distilled multi-time-step (DMTS) strategy to accelerate molecular dynamics simulations using foundation neural network models. DMTS uses a dual-level neural network, where the target accurate potential is coupled to a simpler but faster model obtained via a distillation process. The 3.5 Å cutoff distilled model is sufficient to capture the fast-varying forces, i.e., mainly bonded interactions, from the accurate potential, allowing its use in a reversible reference system propagator algorithm (RESPA)-like formalism. The approach conserves accuracy, preserving both static and dynamic properties, while enabling us to evaluate the costly model only every 3 to 6 fs depending on the system. Consequently, large simulation speedups over standard 1 fs integration are observed: nearly 4-fold in homogeneous systems and 3-fold in large solvated proteins through leveraging active learning for enhanced stability. Such a strategy is applicable to any neural network potential and reduces the performance gap with classical force fields. Artificial neural network ; Distillation ; Molecular dynamics ; System dynamics ; Homogeneous ; Foundation (evidence) ; Cutoff ; Scheme (mathematics) Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Pengtao Li | Chenke Ding | Yutong Song | Yihui Jiang | Fei Cao | Yanfang Wang | Zhongyi Ding | Yuanxi Liu | Ruihan Zhang | Yong Gao
Source: Materials Characterization (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Machine learning-guided revelation of Detwinning-dominated cryogenic plasticity in Nano-twinned Cu-Ag alloys Plasticity ; Cryogenic temperature ; Revelation ; Cryogenics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Nanoporous metals and alloys
Authors: Guy W. Dayhoff | Sameer Varma
Source: AIP Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Velocity updates from machine learning for stable molecular dynamics integration We present a machine-augmented molecular dynamics (MAMD) framework in which particle velocities are updated using predictions from stacked long short-term memory networks trained on historical velocity and coordinate data. MAMD propagates coordinate trajectories in time without access to forces or energies during training or inference. Applied to isolated harmonic diatomics, MAMD conserves total energy, preserves molecular structure, and reproduces velocity autocorrelation functions. Small integration errors can accumulate over long trajectories, but we show that molecular dynamics stability can be recovered through periodic, but infrequent injections of velocity updates computed from Hamiltonian forces (frequency ≤0.01). We also find that the optimal history length for each diatom closely matches the first inflection point of its velocity autocorrelation function, suggesting a link between model architecture and statistical mechanics. These results establish MAMD as a proof-of-concept integration strategy for the isolated harmonic diatomic systems studied here, in which finite velocity memory supports short-horizon predictions and sparse Hamiltonian check-ins provide long-horizon stability. In particular, we show that force-free, velocity-based machine learning updates can be embedded directly into conventional molecular dynamics algorithms while retaining essential physical invariants, providing a physically interpretable basis for hybrid MD/ML integration. Systematic extensions to more complex systems and quantitative performance comparisons in regimes with expensive force evaluations (e.g., ab initio molecular dynamics) are left for future work. Autocorrelation ; Molecular dynamics ; Diatomic molecule ; Inflection point ; Hamiltonian (control theory) ; Hamiltonian mechanics ; Dynamical systems theory ; Verlet integration ; Point (geometry) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Qianxiang Xu | Yudong Zhao | Yu Liu | Yiming Li
Source: Journal of Chemical Education (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Construction of Potential Energy Surface for Ne Atom and H <sub>2</sub> <sup>+</sup> Ion Collision Reaction Based on Artificial Intelligence In undergraduate chemistry education, the applications of machine learning methods remain limited, particularly in potential energy surface (PES) fitting for molecular dynamics simulations. To address this gap, this experiment focuses on the one-dimensional triatomic collision reaction Ne + H2+ → NeH+ + H, a classical reaction in physical chemistry. Starting with quantum chemistry calculations, we developed a user-friendly open-source package in Python, leveraging the machine learning framework PyTorch to fit the PES of this reaction. The trained model was then used to visualize molecular dynamics simulations. Through this experiment, students gain comprehensive experience in applying quantum chemistry calculations and machine learning techniques to solve a specific chemical problem while developing insights into PES fitting using neural networks. Quantum chemistry ; Triatomic molecule ; Potential energy surface ; Reaction dynamics ; Molecular dynamics ; Collision ; Quantum ; Atom (system on chip) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Various Chemistry Research Topics
Authors: Vishu Gupta | Kamal Choudhary | Youjia Li | Muhammed Nur Talha Kilic | Daniel Wines | Wei-keng Liao | Alok Choudhary | Ankit Agrawal
Source: Materials Research Express (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Dynamic embedding representation for graph neural networks to enhance materials property prediction with limited datasets Abstract Graph neural networks (GNNs) have proven effective in understanding and predicting diverse material properties, even when working with limited datasets. An important step in training GNN is to use an appropriate and informative graph embedding that can adequately represent the structural and compositional information in the chemical space. Current graph embeddings consist of composition and structure-agnostic element-level encodings, which are static in nature. This makes it challenging to differentiate between different compounds on the element level, especially for datasets with limited data size, thereby relying more on the complex input and architecture for model training. Here, we present a novel framework for GNN-based prediction tasks that use dynamic embedding to significantly improve the models’ predictive ability on materials properties with limited data size. We evaluated the proposed framework on multiple materials datasets across various domains to find that the model trained using dynamic embedding outperforms the models trained using conventional static embedding and features obtained using a pre-trained model. The proposed framework holds significant potential for expediting artificial intelligence (AI)-driven materials discovery. Embedding ; Graph ; Expediting ; Representation (politics) ; Property (philosophy) ; Graph embedding ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Nikita  Yu Nikitin | Andrey  A. Stepashkin
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Statistical analysis and prediction of mechanical properties of composite materials based on thermoplastic polymers reinforced with continuous carbon fibers based on Convolutional Neural Network Ultimate tensile strength ; Composite number ; Correlation coefficient ; Convolutional neural network ; Fiber ; Tensile testing ; Artificial neural network ; Material properties ; Mean squared error Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Mechanical Behavior of Composites
Authors: Yangfan Zhang | Yiming Xia | Ali Shakiba | Hongrui Zhang | Xiaojing Hao | Priyank V. Kumar | Mahesh P. Suryawanshi
Source: Advanced Science (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges The development of perovskites and perovskite-inspired materials (PIMs) is driven by the need for efficient, non-toxic and stable solar energy conversion technologies. While halide perovskites exhibit outstanding optoelectronic properties, their practical deployment remains hindered by toxicity concerns and long-term instability. Conventional experimental and computational approaches, though effective, are often limited by high costs and low throughput, prompting the need for data-driven strategies. In this review, we provide a comprehensive analysis of machine learning (ML)-driven approaches for predicting key properties such as bandgap, stability, and lattice constants in perovskite and PIMs systems. We outline a complete ML workflow, from target identification and data collection to feature engineering and model selection across supervised, unsupervised, and reinforcement learning frameworks. Special attention is given to the transferability of ML strategies developed for halide perovskites to the more chemical diverse PIMs landscape. By highlighting recent progress and current limitations, we provide a critical roadmap for integrating ML into the rational design and discovery of next-generation non-toxic, stable solar materials. These insights are expected to accelerate the discovery-to-deployment cycle for low-toxicity, high-efficiency solar absorbers and catalyze innovation across the broader field of data-driven energy materials. Software deployment ; Transferability ; Perovskite (structure) ; Solar energy ; Identification (biology) ; Halide ; Field (mathematics) ; Key (lock) ; Scheduling (production processes) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Qingzhou Tao | Guanhua Sun | Chao Yang | Hao Tian | Wenjie Du | Bo Peng | Sijia Liu | Da Shu | Ye Wei
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multi-modal mechanical property prediction of recycled metallic materials with stacking machine learning Mean absolute percentage error ; Ultimate tensile strength ; Stacking ; Process (computing) ; Remanufacturing ; Property (philosophy) ; Mean squared error ; Artificial neural network Machine Learning in Materials Science ; Material Selection and Properties ; Microstructure and Mechanical Properties of Steels
Authors: Ankita .R. Nevase.
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Role of Artificial intelligence (AI) in Organic chemistry Abstract Artificial Intelligence (Al) have rapidly emerged as indispensable tools in organic chemistry, accelerating molecular discovery, synthesis planning, and reaction optimization. This paper reviews the integration of Al-driven strategies, including deep learning and graph neural networks, which are reshaping how chemists approach complex molecular design and retrosynthetic analysis. We highlight key applications, such as predictive modeling of reaction outcomes, stereo chemical control, and the automation of laboratory workflows. While Al offers unprecedented speed and efficiency-surpassing traditional, experience-driven methods-this work also addresses current bottlenecks, including data availability, model interpretability, and the gap between computational predictions and experimental reality. Finally, we discuss the future of hybrid human-Al intelligence in driving innovation in materials science and drug discovery Automation ; Artificial neural network ; Key (lock) ; Applications of artificial intelligence ; Deep learning ; Artificial life Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Peijie FENG | Lele Aditya Dilip | Minhyeok Lee | Yiguang Ju | Yuji Suzuki
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulation of nitrogen diffusion in iron and iron nitrides using <i>ab initio</i> data trained machine learning potentials Ammonia is receiving increasing attention as a hydrogen energy carrier and a green fuel for achieving carbon neutrality. Combustors utilizing ammonia as a fuel are subject to "unwanted" surface nitriding, which hardens and embrittles the metal walls of combustors. Predicting the nitridation rate and nitride layer thicknesses with Fick's diffusion law requires temperature- and concentration-dependent nitrogen atoms' diffusion coefficients in the relevant iron and iron nitrides. Experimentally determining the diffusion coefficients is challenging due to steep N-gradient, and concurrent phase transformations. In this study, we calculated the nitrogen atom diffusion coefficients using molecular dynamics (MD) driven by a machine learning interatomic potential (MLP). The MLP was trained on snapshots extracted from <i>ab initio</i> molecular dynamics (AIMD) trajectories covering different temperatures and nitrogen atom concentrations, and the contribution of each subset to the model's accuracy was quantified. The results show that the MLP reproduces independently the calculated diffusion energy barriers obtained using density functional theory (DFT) in α-, γ-iron and γ'-, ε-iron nitrides. For ε-iron nitride, the MLP-driven MD-computed self-diffusion coefficient was converted into chemical (Fickian) diffusion coefficients, enabling direct comparison with experiments. Our MLP-driven MD simulation delivers diffusion coefficients approaching <i>ab initio</i> accuracy, enabling precise concentration- and temperature-resolved modeling of nitrogen diffusion in iron and its nitrides. Arrhenius fitting to the 830-1500 K chemical diffusion coefficients reproduce the experimental activation energy and pre-exponential factor. Extrapolation to 823 K, the temperature at which most of the experiments are performed, yields a coefficient that falls within the experimental uncertainty. The present MLP-MD model will facilitate the mechanistic understanding of iron surface nitridation and quantitative predictions of "unwanted" nitriding of iron-based combustor wall materials induced by ammonia at high temperatures. Molecular dynamics ; Ab initio ; Nitride ; Hydrogen ; Nitrogen ; Diffusion ; Carbon fibers ; Ammonia Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Hydrogen Storage and Materials