jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: César Gabriel Vera de la Garza | Wilmer E. Vallejo Narváez | Serguei Fomine
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Accelerating Hydrogen Storage Material Discovery: Machine Learning Approach for Predicting Multi-Step Hydrogenation Energetics in Transition Metal-Doped Phosphorene Phosphorene ; Density functional theory ; Hydrogen storage ; Gibbs free energy ; Feature (linguistics) ; Hydrogen ; Ensemble learning ; Training set Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion
Authors: Maksim Posysoev | Alexander P. Lyubartsev
Source: The Journal of Chemical Physics (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Systematic selection of symmetry functions for transferable neural network potentials in coarse-grained molecular modeling Developing transferable coarse-grained (CG) models is a major challenge in molecular simulations, as conventional potentials are often state-point and system composition dependent. While neural network (NN) potentials offer a promising path to transferability, their development is hampered by training instabilities and the complex, often heuristic, selection of model parameters. This work presents a systematic methodology to address these issues, focusing on the rational selection of descriptors for NN-based CG potentials. We propose a systematic workflow for parameterizing Behler-Parrinello symmetry functions (G2) by analyzing their resolving power and response to changes in system conditions, such as concentration. This allows for the selection of an optimized set of descriptors that captures essential structural features, handles short-range repulsions efficiently, and includes descriptors sensitive to the thermodynamic state to ensure transferability. Furthermore, we adopt a network extension technique that enables iterative improvement of model accuracy by expanding the network architecture and descriptor set without discarding prior training. The methodology is demonstrated on a CG model of methanol-water mixtures, with the NN trained to reproduce radial distribution functions derived from atomistic simulations across a wide range of methanol concentrations (10%-100%). The resulting NN potential, built using the systematic approach, demonstrates significantly improved accuracy and transferability, particularly at high concentrations, outperforming models developed previously with empirically chosen parameters. Our findings provide practical guidelines and a robust workflow for developing accurate and transferable NN potentials for CG simulations, paving the way for more reliable mesoscale modeling of complex systems. Workflow ; Set (abstract data type) ; Selection (genetic algorithm) ; Model selection ; Artificial neural network ; Network model ; Range (aeronautics) ; Path (computing) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Phase Equilibria and Thermodynamics
Authors: Shuo Cao | Benrui Tang | Zezhu Zeng | Zheyong Fan | Ye Su | Yu Yan
Source: Journal of Applied Physics (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Intrinsic and tunable lattice thermal conductivity of single- and multi-layer goldene: A machine-learning molecular dynamics study The recent successful fabrication of single- and multi-layer gold, referred to as goldene, represents a significant breakthrough in low-dimensional materials research. A fundamental understanding of its phonon transport properties, both intrinsic and extrinsic, is crucial for assessing its potential in applications such as thermal management, sensing, solar cells, and photothermal therapy. Here, we perform extensive machine-learning molecular dynamics simulations using an accurate and efficient unified neuroevolution potential to investigate the intrinsic lattice thermal conductivity of goldene and its tunability through external strain, defects, and layer thickness, drawing close comparisons with bulk gold. We find that single-layer goldene exhibits a high intrinsic lattice thermal conductivity of 34.4±0.4Wm−1K−1, which is approximately 17 times as large as that of bulk gold. While both systems exhibit a κ∼T−p dependence (p=1.69 for goldene, p=1.30 for bulk), they respond oppositely to tensile strain: κ decreases in bulk gold but increases in goldene. We attribute this contrasting behavior to the fundamentally different effects of strain on their phonon modes, hardening the dominant flexural phonons in goldene while softening all the phonon modes in bulk gold. Furthermore, both vacancy and adatom defects significantly reduce κ, with adatoms having a more pronounced effect. Finally, κ decreases monotonically with increasing layer number, rapidly converging toward the bulk value. Our results elucidate the intrinsic lattice thermal transport in goldene and demonstrate versatile strategies for tuning it, thereby enriching the understanding of heat transport in two-dimensional metallic systems. Phonon ; Thermal conductivity ; Molecular dynamics ; Lattice (music) ; Thermal ; Vacancy defect Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Topological Materials and Phenomena
Authors: Mojahed Abushawish | Guillaume Baulieu | J. Dudouet | O. Stézowski
Source: The European Physical Journal A (article, 2026-02-04)
Field: Materials Science — Biomaterials

Neural networks for 3D characterisation of AGATA crystals Artificial neural network ; Feature (linguistics) ; Signal processing ; Noise (video) ; Pattern recognition (psychology) Calcium Carbonate Crystallization and Inhibition ; Enzyme Structure and Function ; Machine Learning in Materials Science
Authors: Cheng Zeng | Harry W. Sullivan | Thomas Egg | Maya M. Martirossyan | Philipp Höllmer | Jirui Jin | Richard G. Hennig | Adrian Roitberg | Stefano Martiniani | Ellad B. Tadmor | Mingjie Liu
Source: arXiv (Cornell University) (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids, and metal-organic frameworks, extending such approaches to fully periodic molecular crystals is still elusive. Here, we present MolCrystalFlow, a flow-based generative model for molecular crystal structure prediction. The framework disentangles intramolecular complexity from intermolecular packing by embedding molecules as rigid bodies and jointly learning the lattice matrix, molecular orientations, and centroid positions. Centroids and orientations are represented on their native Riemannian manifolds, allowing geodesic flow construction and graph neural network operations that respects geometric symmetries. We benchmark our model against a state-of-the-art generative model (MOFFlow) for large-size periodic crystals and a rule-based structure generation method (Genarris) on two open-source molecular crystal datasets. MolCrystalFlow outperforms MOFFlow while achieving competitive performance against Genarris. We also demonstrate an integration of MolCrystalFlow model with universal machine learning potential to accelerate molecular crystal structure prediction, paving the way for data-driven generative discovery of molecular crystals. Intermolecular force ; Intramolecular force ; Crystal (programming language) ; Crystal structure ; Molecular graph ; Crystal structure prediction ; Molecule ; Generative grammar ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Supramolecular Chemistry and Complexes
Authors: Peng Li | Chenglong Qiu | Jinlong Duan | Yao Qin | Mingxing Li | Dandan Zhai | Xile Cheng | Yuqi Yang
Source: Analytica Chimica Acta (article, 2026-03-27)
Field: Chemistry — Analytical Chemistry

GAN-FixMatch: A generative–semi-supervised deep learning framework for small-sample quantitative analysis in near-infrared spectroscopy of agricultural and pharmaceutical materials Residual ; Gaussian ; Noise (video) ; Deep learning ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Consistency (knowledge bases) ; Quantitative analysis (chemistry) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Machine Learning in Materials Science
Authors: Jia-Hua Liu | Shuo Cui | Feng Guo | Yu-Shi Wen | Chun-Liang Ji | Xiao-Chun Wang
Source: Chinese Physics B (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Thermal conductivity of carbon nanotubes using nonequilibrium molecular dynamics combined with a machine learning potential Abstract Large-scale and long-time-span nonequilibrium molecular dynamics simulations have been performed to determine the thermal conductivity of single-walled and double-walled carbon nanotubes (CNTs) using a machine learning potential trained on atomic energies and forces from density functional theory calculations for sp 2 -hybridized carbon. The size dependence of graphene and CNTs up to 1 μm has been studied with 200000 atoms and simulation times up to 5 ns. The simulations reveal that thermal transport, whether ballistic, quasi-ballistic, or diffusive, is determined by the relationship between the sample length and the effective mean-free path (MFP). The system size has less effect on thermal conductivity when the sample length significantly exceeds the MFP. Radial tensile strain in CNTs causes the C–C bond length to increase in smaller-diameter CNTs, resulting in a phonon softening effect that subsequently reduces thermal conductivity. An analytical function is proposed to describe the relationship between phonon relaxation time and nanotube diameter. The thermal conductivity of the double-walled CNT is lower than that of an equivalent-size single-walled CNT. Phonon–phonon scattering, interlayer van der Waals interactions, and degenerate coupling of transverse acoustic modes are considered to contribute to the reduction in thermal transport. Thermal conductivity ; Carbon nanotube ; Phonon ; Molecular dynamics ; Graphene ; van der Waals force ; Relaxation (psychology) ; Mean free path Thermal properties of materials ; Carbon Nanotubes in Composites ; Advanced Physical and Chemical Molecular Interactions
Authors: Fan Fan | Bin Xi | Xianghu Meng | Han Wang | Bowen Zhang | Qingbo Xu | Wei Feng | Wenfeng Gao | Xiaoman Wang | Li Li | Hongbo Zhang | Feng Zhou | Zhenming Liu | Wenbiao Zhou | Bo Huang
Source: Nature Communications (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Assessing conformation validity and rationality of deep learning-generated 3D molecules Recent advancements in artificial intelligence (AI) have revolutionized the field of 3D molecule generation. However, the lack of effective evaluation methods for 3D conformations limits further improvements. Current techniques, in order to achieve the necessary speed for evaluating large number of AI-generated molecules, often rely on empirical geometric metrics that do not adequately capture various conformational anomalies, or on molecular mechanics energy metrics that exhibit low accuracy and lack atomic or torsional details. To address this gap, we propose a two-stage approach that achieves both high speed and quantum mechanical level accuracy. The first stage, termed the validity test, employs an AI-derived force field to identify atoms with elevated energy resulting from implausible neighboring environments. The second stage, known as the rationality test, utilizes a deep learning network trained on data with density functional theory accuracy to detect rotatable bonds with high torsional energies. To demonstrate the functionality of our evaluation system, we applied our approach to five recently reported 3D molecule generation AI models across 102 targets in Directory of Useful Decoys-Enhanced dataset. To facilitate accessibility for the academic community, our method is available as an open-source package. Rationality ; Force field (fiction) ; Field (mathematics) ; Density functional theory ; Energy (signal processing) ; Quantum ; Molecule ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Nicolaï Gouraud | Côme Cattin | Thomas Plé | Olivier Adjoua | Louis Lagardère | Jean-Philip Piquemal
Source: Open MIND (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces Following our previous work (J. Phys. Chem. Lett., 2026, 17, 5, 1288-1295), we propose the DMTS-NC approach, a distilled multi-time-step (DMTS) strategy using non-conservative (NC) forces to further accelerate atomistic molecular dynamics simulations using foundation neural network models such as FeNNix-Bio1. There, a dual-level reversible reference system propagator algorithm (RESPA) formalism couples a target accurate conservative potential to a simplified distilled representation optimized for the production of non-conservative forces. Despite being non-conservative, the distilled architecture is designed to enforce key physical priors, such as equivariance under rotation and cancellation of atomic force components. These choices facilitate the distillation process and therefore improve drastically the robustness of simulation, significantly limiting abnormal discrepancies between the two models, thus achieving excellent agreement with the forces data. Overall, the DMTS-NC scheme is found to be more stable and efficient than its conservative counterpart with additional speedups reaching 15-30\% over DMTS. Requiring no fine-tuning steps, it is easier to implement and can be pushed to the limit of the systems physical resonances to maintain accuracy while providing maximum efficiency. We obtain additional speedup by combining hydrogen mass repartitioning (HMR), High Hydrogen Friction (HHF) to further extended the largest timestep up to 10fs of our schemes while conserving stability and accuracy. As for DMTS, DMTS-NC is applicable to any neural network potential and can be applied to approaches that are computationally heavier than FeNNix-Bio1. We show a proof of principle applying the approach to the distillation of MACE-OFF23 with consequent speedups ranging from 3.66 to 5.64 compared to single timestep. Robustness (evolution) ; Formalism (music) ; Artificial neural network ; Limiting ; Molecular dynamics ; Fractionating column ; Distillation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Zhepeng Wang | Bing Wang | Penghua Ying | Jin Zhang
Source: ACS Applied Nano Materials (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Machine Learning Molecular Dynamics Simulations on Piezopotentials of Hexagonal Boron Nitride/Graphene Lateral Heterostructures: Implications for Flexible Piezoelectric Devices Lateral heterostructures of graphene and hexagonal boron nitride (h-BN) exhibit promising properties that are highly sensitive to the patterning of alternating domains. However, the vast design space of domain arrangements has hindered systematic mapping of their properties. Herein, we develop a neuroevolution machine learning interatomic potential (MLIP) to efficiently and accurately predict the piezopotential properties of h-BN/graphene heterostructures. Using the trained MLIP, high-throughput molecular dynamics simulations show that the mechanical, piezoelectric, and dielectric responses of h-BN/graphene heterostructures can be widely tuned by varying the concentration and arrangement of graphene domains, rendering their piezopotentials highly designable. This tunability of piezopotential is primarily ascribed to the polarization-gradient and flexoelectric effects, majorly governed by two topological descriptors: the coefficient of variation and a symmetry index. To target the specified piezopotential, we further introduce a machine learning-based inverse-design method. This study not only expands the understanding of piezoelectric properties of h-BN/graphene lateral heterostructures but also provides a strategy to design two-dimensional lateral heterostructures with tailored electromechanical properties for high-performance flexible piezoelectric devices. Heterojunction ; Piezoelectricity ; Graphene ; Dielectric ; Molecular dynamics ; Hexagonal crystal system ; Rendering (computer graphics) ; Boron nitride Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; 2D Materials and Applications
Authors: Y. Gong | Menglin Wu | Xiang Zhang | Yang Zhao | Xunxiao Zhao | Jiang Li | Weitao Yang | Xi Zhang | Dingwei Fu | Bingbo Zhang | Xue Li | Shuang Xia
Source: ACS Nano (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Predicting Plaque Vulnerability Using Machine Learning-Enabled Nanoagents Sensitized Molecular High-Resolution Magnetic Resonance Imaging Data Molecular imaging based on paramagnetic nanoagents has emerged as an intriguing strategy to sensitize the local magnetic properties of pivotal pathological processes related to atherosclerotic plaque destabilization, opening up a potential possibility for noninvasively predicting plaque vulnerability. Unfortunately, current magnetic resonance (MR) imaging interpretation fails to provide objectively and precisely quantitative imaging descriptors, thus showing limited values in stratifying the plaque risk from MR images. To address this need, we originated a synergistic nanoagents (tFM-Nanoagents)-assisted machine learning (nano-AML) technology for directly reading out plaque vulnerability from molecular high-resolution vessel wall MR imaging (HR-VWI). The proposed diagnostic paradigm provided a holistic visualization of the distribution of foamy macrophage-defined plaques; by using a machine learning (ML) approach to decode data of tFM-Nanoagents sensitized HR-VWI, an imaging-derived risk score (nano-AML score) correlating with the pathology vulnerability index of plaques was generated and validated in a preclinical atherosclerotic model. Our data showed that the nano-AML score could effectively phenotype plaques into "vulnerable" and "stable" classes, with an area under the curve (AUC) of 0.871 in the training cohort and 0.870 in the validation cohort. We also demonstrated that the predictive performance of nano-AML score outperformed that of commercial contrast agent Gadovist (AUC of 0.560 in the training cohort and 0.538 in the validation cohort), suggesting its robust potency for serving as a reliable predictor for vulnerable plaques. Magnetic resonance imaging ; Vulnerability (computing) ; Molecular imaging ; Visualization ; Training set ; Contrast (vision) ; Quantitative assessment ; Imaging technique Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: F. J. Dominguez-Gutierrez | T. Stasiak | G. Markovic | A. Kosinska | K. Mulewska
Source: arXiv (Cornell University) (preprint, 2026-01-11)
Field: Materials Science — Materials Chemistry

Nanoindentation induced plasticity in equiatomic MoTaW alloys by experimentally guided machine learning molecular dynamics simulations Refractory complex concentrated alloys (RCCA) exhibit exceptional strength and thermal stability, yet their plastic deformation mechanisms under complex contact loading remain insufficiently understood. Here, the nanoindentation response of an equiatomic MoTaW alloy is investigated through a combined experimental and atomistically resolved modeling approach. Spherical nanoindentation experiments are coupled with large scale molecular dynamics simulations employing a tabulated low dimensional Gaussian Approximation Potential (tabGAP), enabling near DFT accuracy. A physics based similarity criterion, implemented via PCA of load-displacement curves, is used to identify mechanically representative experimental responses for quantitative comparison with simulations. Indentation stress-strain curves are constructed yielding excellent agreement in the elastic regime between experiment and simulation, with reduced Young's moduli of approximately 270 GPa. Generalized stacking fault energy calculations reveal elevated unstable stacking- and twinning-fault energies in MoTaW relative to pure refractory elements, indicating suppressed localized shear and a preference for dislocation-mediated plasticity. Atomistic analyses demonstrate a strong crystallographic dependence of plastic deformation, with symmetric {110}&lt;111&gt; slip activation and four-fold rosette pile-ups for the [001] orientation, and anisotropic slip, strain localization, and enhanced junction formation for [011]. Local entropy and polyhedral template matching analyses further elucidate dislocation network evolution and deformation-induced local structural transformations. Overall, this study establishes a direct mechanistic link between fault energetics, orientation-dependent dislocation activity, and experimentally observed nanoindentation behavior in RCCA. Nanoindentation ; Stacking-fault energy ; Molecular dynamics ; Plasticity ; Indentation ; Dislocation ; Deformation mechanism ; Anisotropy ; Deformation (meteorology) ; Alloy 2D Materials and Applications ; High Entropy Alloys Studies ; Metal and Thin Film Mechanics
Authors: Shoeb Athar | Philippe Jund
Source: Artificial Intelligence Chemistry (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Beyond predicted zT: Machine learning strategies for the experimental discovery of thermoelectric materials International audience Thermoelectric effect ; Work (physics) ; Field (mathematics) ; Artificial neural network Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices