jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haoyu Zhou | Ping Xue | Zhang, Hao | Fu, Tianfan
Source: ArXiv.org (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Quantized SO(3)-Equivariant Graph Neural Networks for Efficient Molecular Property Prediction Deploying 3D graph neural networks (GNNs) that are equivariant to 3D rotations (the group SO(3)) on edge devices is challenging due to their high computational cost. This paper addresses the problem by compressing and accelerating an SO(3)-equivariant GNN using low-bit quantization techniques. Specifically, we introduce three innovations for quantized equivariant transformers: (1) a magnitude-direction decoupled quantization scheme that separately quantizes the norm and orientation of equivariant (vector) features, (2) a branch-separated quantization-aware training strategy that treats invariant and equivariant feature channels differently in an attention-based $SO(3)$-GNN, and (3) a robustness-enhancing attention normalization mechanism that stabilizes low-precision attention computations. Experiments on the QM9 and rMD17 molecular benchmarks demonstrate that our 8-bit models achieve accuracy on energy and force predictions comparable to full-precision baselines with markedly improved efficiency. We also conduct ablation studies to quantify the contribution of each component to maintain accuracy and equivariance under quantization, using the Local error of equivariance (LEE) metric. The proposed techniques enable the deployment of symmetry-aware GNNs in practical chemistry applications with 2.37--2.73x faster inference and 4x smaller model size, without sacrificing accuracy or physical symmetry. Equivariant map ; Quantization (signal processing) ; Normalization (sociology) ; Graph ; Invariant (physics) ; Inference ; Artificial neural network ; Norm (philosophy) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Yuqi An | Zhenbin Wang
Source: arXiv (Cornell University) (preprint, 2026-02-03)
Field: Materials Science — Materials Chemistry

Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles calculations in crystal structure prediction (CSP). However, their effectiveness in identifying new, complex materials remains uncertain. Here, we systematically assess the capability of a uMLIP (i.e.,M3GNet) to accelerate CSP in quaternary oxides. Through extensive exploration of the Sr-Li-Al-O and Ba-Y-Al-O systems, we show that uMLIP can rediscover experimentally known materials absent from its training set and identify seven new thermodynamically and dynamically stable compounds. These include a new polymorph of Sr2LiAlO4 (P3221) and a new disordered phase, Sr2Li4Al2O7 (P1_bar). Furthermore, our results show stability predictions based on the semilocal PBE functional require cross-validation with higher-level methods, such as SCAN and RPA, to ensure reliability. While uMLIPs substantially reduce the computational cost of CSP, the primary bottleneck has shifted to the efficiency of search algorithms in navigating complex structural spaces. This work highlights both the promise and current limitations of uMLIP-driven CSP in the discovery of new materials. Bottleneck ; Stability (learning theory) ; Set (abstract data type) ; Crystal structure prediction ; Complex system ; Work (physics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Condensed Matter Physics
Authors: Hussein A. K. Kyhoiesh | Rihab A. H. Dubaish | Zaman A. I. Alaridhee | Hassan E. Abd Elsalam | Islam H. El Azab
Source: ChemistrySelect (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Machine Learning‐Guided Design of Organic Compounds With Tailored Surface Tension Using Molecular Fingerprints ABSTRACT Organic compounds play a crucial role in various industrial applications, and their properties, such as surface tension, significantly impact their performance. Designing new organic compounds with desired properties is a challenging task, and machine learning (ML) has emerged as a promising approach to accelerate this process. In this study, we developed an ML model to predict the surface tension of organic compounds using 2048‐bit fingerprints. Among various algorithms, XGBoost demonstrated the best performance with an R 2 score of 0.87. Leveraging this model, we designed 910 new organic compounds with surface tension values as high as 35. The data was visualized using SALI scatter plots, providing insights into the chemical space of the designed compounds. Furthermore, we calculated the synthetic accessibility scores for these compounds and identified 20 promising candidates for experimental synthesis. This work showcases the potential of ML in designing new organic compounds with desired properties, paving the way for accelerated materials discovery and development. Surface tension ; Work (physics) ; Surface (topology) ; Chemical space ; Experimental data ; Organic chemicals Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Crystallography and molecular interactions
Authors: Yehui Zhang | Jiangxin He | Zhuangzhaung Lai | Chongyi Ling | Qiang Li (8118) | Jinlan Wang (1235505)
Source: Figshare (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Machine Learning-AcceleratedKinetic Simulations ofSurface 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 ; CO2 Reduction Techniques and Catalysts
Authors: Zoë J. Gray | Joseph B Choi | Y. Choi | H. Keo Springer | H. S. Udaykumar | Stephen Baek
Source: Propellants Explosives Pyrotechnics (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC) ABSTRACT Physics‐aware deep learning (PADL) has gained popularity for use in spatiotemporal dynamics simulations, such as those in computational modeling of energetic materials (EM). We show that the challenge PADL methods face while learning complex field evolution problems can be simplified and accelerated by decoupling it into two tasks: learning complex geometric features in evolving fields and modeling dynamics over these features in a lower‐dimensional feature space. We build upon our previous work on physics‐aware recurrent convolutional neural networks (PARC). PARC embeds knowledge of underlying physics into its neural network architecture for more robust and accurate prediction of evolving physical fields. PARC was shown to effectively learn complex nonlinear features such as the formation of hotspots and coupled shock fronts in various initiation scenarios of EMs, as a function of microstructures, serving effectively as a microstructure‐aware burn model. Here, we further accelerate PARC and reduce its computational cost by projecting the original dynamics onto a lower‐dimensional invariant manifold, or “latent space.” The projected latent representation encodes the complex geometry of evolving fields (e.g., temperature and pressure) in a set of data‐driven features. The reduced dimension of this latent space allows us to learn the dynamics during the initiation of EM with a lighter and more efficient model. We observe a significant decrease in training and inference time while maintaining results comparable to PARC at inference. This work takes steps towards enabling rapid prediction of EM thermomechanics at larger scales and characterization of EM structure–property–performance linkages at a full application scale. Inference ; Deep learning ; Convolutional neural network ; Feature learning ; Representation (politics) ; Decoupling (probability) ; Invariant (physics) ; Artificial neural network ; Field (mathematics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Valerii Andreichev | Ján Dušek | Markus Meuwly | Jeremy O. Richardson
Source: arXiv (Cornell University) (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of this workflow without any loss of accuracy. We discuss various methods for constructing potential energy surfaces including transfer learning, which requires a minimal number of expensive training points. In this way, we can study chemical reactions at a high level but a low cost. In particular, as the potentials are smooth and differentiable, they enable the use of more advanced semiclassical approximations to quantum dynamics, such as perturbatively corrected instanton theory, which can capture both tunnelling and anharmonicity. Semiclassical physics ; Quantum tunnelling ; Instanton ; Quantum ; Quantum dynamics ; Molecular dynamics ; Potential energy ; Energy (signal processing) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Gaussian Processes and Bayesian Inference
Authors: Shakib Al Sharif
Source: Next Materials (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Surrogate-assisted optimization of orientation distribution functions for alloy 6061 using physics-based data and explainable machine learning The crystallographic orientation distribution function (ODF) plays a significant role in controlling the anisotropic mechanical response of aluminum alloy 6061. However, optimization of high-dimensional ODFs still remains a challenge due to the nonlinear nature of microstructure-property relationships and the computational cost of physics-based simulations. This study presents a physics-informed machine-learning framework that identifies optimal ODF configurations to maximize the stiffness–strength synergy metric F Y/E. A synthetic dataset of 150,000 microstructures was generated using Dirichlet-sampled ODFs, rotated stiffness tensors, Voigt-Reuss-Hill homogenization, and a Taylor-type crystal plasticity model. Multi-method feature ranking (Mutual Information, ANOVA, Chi-square, L1-SVC) consistently identified 20 dominant orientation components. A shallow decision tree extracted physically meaningful orientation thresholds that provides transparent processing-relevant rules. A Random Forest surrogate which was trained on scaled ODFs achieved good predictive fidelity (R 2 =0.88). It enabled rapid exploration of the ODF landscape. A multi-start surrogate-assisted optimization strategy was implemented and benchmarked against genetic algorithms and random search. It shows a 2–3% improvement in the best achievable performance. Nearest-neighbor back-projection confirmed that optimized candidates closely matched true physics-based evaluations (<3% deviation). DBSCAN clustering unveiled a unimodal distribution of optimized solutions, which means that only one strong microstructure family existed. In summary, this study opens up a clear and physically based as well as a computationally efficient route for the optimization of microstructures in high-dimensional orientation spaces. Apart from alloy 6061, the suggested framework gives a generalizable basis for the design of anisotropic materials where the mechanical performance is greatly influenced by the crystallographic texture. • Physics-based models generate high-fidelity orientation–property data for alloy 6061. • Multi-method feature ranking identifies the most influential orientation components. • Interpretable machine learning extracts clear orientation rules linked to performance. • Surrogate optimization outperforms genetic and random search in finding optimal texture. • Clustering confirms a single robust high-performance texture family in alloy 6061. Metric (unit) ; Orientation (vector space) ; Anisotropy ; Ranking (information retrieval) ; Smoothness ; Cluster analysis ; Random forest ; Optimization problem ; Multi-objective optimization Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Model Reduction and Neural Networks
Authors: Susumu Fujii | Tatsuya Yokoi | Katsuyuki Matsunaga
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Nanoscale thermal transport at PbTe grain boundaries: a neural-network-potential molecular dynamics study Neural-network-potential molecular dynamics simulations on 24 PbTe grain boundaries (GBs) show that lattice thermal conductivity depends strongly on GB structure, with distinct correlations for heat flow normal versus parallel to the GB plane. Molecular dynamics ; Grain boundary ; Thermal conductivity ; Nanoscopic scale ; Thermal ; Thermal conduction ; Dynamics (music) Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Yuge Chen | Jinjie You | Li Qin | Rui Chen | Liuquan Zhang | Haifeng Jiao | Jiafeng Yang | Hua Liu | Xizhi Shi
Source: Analytica Chimica Acta (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Smartphone-integrated molecularly imprinted sensor with convolutional neural networks for on-site detection of Norfloxacin Detection limit ; Convolutional neural network ; Norfloxacin ; Fluorescence ; Linear range ; Molecularly imprinted polymer ; Partial least squares regression Carbon and Quantum Dots Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Florence Szczepaniak | Donghyuk Suh | Wonpil Im
Source: Journal of Chemical Information and Modeling (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

CHARMM-GUI Hybrid ML/MM Builder for Hybrid Machine Learning and Molecular Mechanical Modeling and Simulations Recent advances in machine learning (ML) have enabled new developments in molecular dynamics simulation. Neural network potentials (NNPs) trained on quantum mechanical (QM) data provide highly accurate descriptions of drug-like molecules. Analogous to a QM and molecular mechanical (QM/MM) approach, hybrid ML/MM simulations employ NNPs to describe a localized region of the system, such as a ligand, while the rest of the system is treated using classical MM force fields. This hybrid framework enables simulations of protein-ligand complexes with near-QM accuracy for the ligand at a substantially reduced computational cost. CHARMM-GUI <i>Hybrid ML/MM Builder</i> automates the preparation of system and input files required for hybrid ML/MM modeling and simulation. This new module generates all necessary files to simulate protein-ligand complexes in solution or membrane using TorchANI-AMBER and OpenMM-ML. Currently supported NNPs include MACE and ANI. In this paper, we present <i>Hybrid ML/MM Builder</i> and representative application systems that demonstrate its usage and capabilities. Hybrid system ; Artificial neural network ; Molecular dynamics ; Deep learning ; Mechanical system ; Complex system Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: Jaehoon Kim | Minseok Moon | Hyunsung Cho | Hyeon-Deuk Kim | Rokyeon Kim | Gyehyun Park | Seungwu Han | Youngho Kang
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomic-Scale Mechanisms of SiO$_2$ Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential Plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide (SiO$_2$) is widely used for low-temperature fabrication of dielectric thin films, yet its atomic-scale growth mechanisms remain incompletely understood. In this work, we investigate SiO$_2$ PECVD using silane and N$_2$O as source gases via molecular dynamics simulations driven by a machine-learning interatomic potential. By systematically varying the oxidant-to-silane-derived species ratio $r$, we elucidate the evolution of film stoichiometry, density, and hydrogen content. Formation of the Si-O-Si network primarily proceeds via oxidation of surface Si-H groups to form Si-OH species, followed by condensation of neighboring Si-OH groups that produces H$_2$O as the dominant byproduct. At low $r$, H$_2$ formation via reactions between Si-H and Si-OH groups also contributes to the network formation. Increasing oxidant supply promotes the network formation through oxidation of residual Si-H species, suppressing hydrogen incorporation and leading to saturation of the Si/O ratio. Rapid chemisorption of silane-derived species, together with steric hindrance from pre-deposited species, results in localized growth and surface roughness. We further show that high-kinetic-energy plasma species can etch SiO$_2$ films, which potentially limits growth rates and enhances surface roughness under high RF-power conditions. These results provide atomic-scale insight into PECVD growth and guidance for optimizing film composition and quality. Plasma-enhanced chemical vapor deposition ; Chemical vapor deposition ; Molecular dynamics ; Silane ; Hydrogen ; Sticking coefficient ; Thin film ; Chemisorption ; Condensation Copper Interconnects and Reliability ; Semiconductor materials and devices ; Thin-Film Transistor Technologies
Authors: Zhenhua Wang | Ningjie Xie | Xiyang Zhao | Yuanming Liu
Source: Journal of Materials Research and Technology (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Deep learning modeling of 4J36 invar alloy process-microstructure-property relationships based on multimodal data 4J36 alloy is a key material in aerospace, electronic devices and other fields. It has been an important challenge for the design, development and optimization of related materials to construct the complex correspondence between process, microstructure and properties. In this paper, different specifications of 4J36 Invar alloy plates were prepared by designing various heat treatment and rolling processes, and the microstructure and mechanical properties curves of the rolled plates were tested. Combining rolling process data (including heat treatment temperature, holding time, cooling method) and microstructure image data (four EBSD images), six different types of deep learning models were constructed, namely MLP and CNN models based on single-modal process data and MLP-ResNet18, MLP-ResNet50, MLP-VITb16, MLP-VITb32 models based on multi-modal data. The stress-strain curves from the yield point to the tensile point of the rolled strip were predicted by using the constructed models, and an effective correspondence between the process-microstructure-properties of 4J36 Invar alloy was achieved. The research results show that under the multimodal and small sample data set in this work, the ResNet network model has more advantages than the VIT Transformer model. The predicted stress-strain curve of the MLP-ResNet18 multimodal mechanical performance model has the highest fit to the actual stress-strain curves, and the R 2 value can reach 0.96. This research provides a new method for the intelligent design and process optimization of 4J36 alloy, promoting its reliable application under different process conditions. Microstructure ; Deep learning ; Process optimization ; Process (computing) ; Alloy ; Ultimate tensile strength ; Artificial neural network ; Invar Machine Learning in Materials Science ; Metallurgy and Material Forming ; Additive Manufacturing Materials and Processes
Authors: Hassan Nosrati | Basem Abu Zneid | Raghavendra Rao | Abinash Mahapatro | Karthikeyan A | Harjot Singh Gill | Yashwant Singh Bisht
Source: Journal of The Electrochemical Society (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Integration of COMSOL Multiphysics Simulations and Machine Learning Algorithms for Predictive Optimization of Lithium-Ion Battery Efficiency High-energy lithium-ion batteries demand cathode architectures capable of sustaining high areal capacities without compromising electrochemical or thermal stability. Among layered oxide cathodes, LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) offers high specific capacity (∼200 mAhg −1 ) and reduced cobalt content, but thick, high-mass-loading electrodes typically exhibit severe Li + transport limitations, polarization, and thermal heterogeneity. To address these issues, an integrated framework is developed, combining COMSOL-based pseudo-two-dimensional (P2D) simulations with machine learning algorithms to enable rapid prediction and optimization of structure-property-performance relationships. High-fidelity simulations are performed across electrode thicknesses of 60–240 μm and porosities of 20%–45%, generating datasets that capture coupled ion diffusion, charge transport, and heat generation. Trained ensemble learning and deep neural network models achieve R 2 &gt; 0.95 for key electrochemical and thermal outputs. Bayesian multi-objective optimization identifies Pareto-optimal electrode architectures, achieving 178 mAhg −1 with minimized overpotential (&lt;45 mV) and temperature rise (&lt;8 °C). This hybrid physics-ML strategy establishes a scalable platform for accelerating thick-electrode design and provides quantitative insights into the critical role of transport and thermal dynamics in next-generation high-energy lithium-ion batteries. Multiphysics ; Bayesian optimization ; Scalability ; Artificial neural network ; Battery (electricity) ; Cathode Machine Learning in Materials Science ; Advancements in Battery Materials ; Big Data and Digital Economy