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• Navigating polymorph generation and distilled-potential development via entropy-symmetry landscapes for metal plasticity mechanisms.
  Artificial intelligence has advanced crystal design, yet unifying crystal structure prediction with thermodynamics-driven structure-property modelling remains challenging owing to divergent methodological foundations. Here we show that an integrated framework …
  europepmc 2026 置信度 0.80
• Accelerating Discovery of Ternary Chiral Materials via Large-Scale Random Crystal Structure Prediction.
  Chiral inorganic crystals, particularly semiconductors with Weyl points near the band edges or semimetals hosting Weyl points at the Fermi level, have attracted considerable interest; yet, they remain scarce in existing materials databases. This study presents…
  europepmc 2026 置信度 0.80
• MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling.
  Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accuracy close to quantum calculations. However, their reliability and effectiveness…
  europepmc 2026 置信度 0.80
• A foundation machine learning potential with polarizable long-range interactions for materials modelling.
  Long-range interactions are essential determinants of chemical system behavior across diverse environments. We present a foundation framework that integrates explicit polarizable long-range physics with an equivariant graph neural network potential. It employs…
  europepmc 2025 置信度 0.80
• Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate.
  Understanding the structure and thermodynamics of solvated ions is essential for advancing applications in electrochemistry, water treatment, and energy storage. While ab initio molecular dynamics methods are highly accurate, they are limited by short accessib…
  europepmc 2025 置信度 0.80
• Unraveling disorder-to-order transitions and chemical ordering in PtCoM ternary alloys using machine learning potential.
  PtCo intermetallic alloy nanoparticles are highly active and stable catalysts for the oxygen reduction reaction (ORR), making them key materials for proton-exchange membrane fuel cells. However, the high-temperature annealing required for ordering into the int…
  europepmc 2025 置信度 0.80
• Incorporating Coulomb interactions with fixed charges in moment tensor potentials and equivariant tensor network potentials.
  In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning interatomic potentials (MLIPs), particularly in the moment tensor potential and equi…
  europepmc 2026 置信度 0.80
• Activation entropy of dislocation glide in body-centered cubic metals from atomistic simulations.
  The activation entropy of dislocation glide, a key process controlling the strength of many metals, is often assumed to be constant or linked to enthalpy through the empirical Meyer-Neldel law-both of which are simplified approximations. In this study, we take…
  europepmc 2025 置信度 0.80
• Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in Platinum-Based Fuel Cells.
  Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent systems remains a challenge. In this work, we train an MLIP to describe hydrated Na…
  europepmc 2026 置信度 0.80
• Fast evaluation of unbiased atomic forces in ab initio variational Monte Carlo via the Lagrangian technique.
  Ab initio quantum Monte Carlo (QMC) methods are state-of-the-art electronic structure calculations based on highly parallelizable stochastic frameworks for accurate solutions of the many-body Schrödinger equation, suitable for modern many-core supercomput…
  pubmed Nakano K, Battaglia S, Hutter J 2026 置信度 0.82
• Accelerating the global search of adsorbate molecule positions using machine-learning interatomic potentials with active learning.
  We accelerate the global search of adsorbate molecule positions using machine-learning interatomic potentials with active learning.
  europepmc Olga Klimanova, Nikita Rybin, Alexander Shapeev 2025 置信度 0.80
• High-Throughput Screening of Bulk Boride Superconductors with Honeycomb-Ruby Frameworks.
  Metal borides are promising phonon-mediated superconductors, yet discovering new high superconducting transition temperature ( T c ) bulk phases beyond MgB 2 -type motifs remains challenging. Here we propose a bulk layered boride prototype built from a honeyco…
  europepmc 2026 置信度 0.80
• ASH: A Multi-Scale, Multi-Theory Modeling Program.
  We introduce ASH, a multi-scale, multi-theory modeling program for quantum mechanics (QM), molecular mechanics (MM), and hybrid calculations, written in the Python programming language. ASH is written in response to the increasingly diverse computational chemi…
  europepmc 2026 置信度 0.80
• Machine Learning the Energetics of Electrified Solid-Liquid Interfaces.
  We present a response-augmented machine-learning (ML) approach to the energetics of electrified metal surfaces. We leverage local descriptors to learn the work function as the first-order energy change to introduced bias charges and stabilize this learning thr…
  europepmc 2025 置信度 0.80
• SphereDiff-TS: Sphere Space Diffusion Modeling for Accurate 3D Transition State Geometry Prediction.
  To overcome the high computational expense of conventional quantum chemistry techniques and the limited incorporation of physical constraints in machine learning models, we present SphereDiff-TS: a diffusion-based method for predicting 3D transition state (TS)…
  europepmc 2026 置信度 0.80
• A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes.
  Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large design space composed of different solvents, additives, and salts, which is difficult to explore experimentall…
  europepmc 2026 置信度 0.80
• Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium.
  We propose a novel approach for constructing training databases for Machine-Learned Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a wide range of conditions. The framework is uniquely appealing due to its ease of…
  europepmc 2026 置信度 0.80
• MOFSynth-ADV: An Open-Source Engine for Synthesizability Evaluation of Metal-Organic Frameworks.
  We present MOFSynth-ADV, an advanced iteration of the MOFSynth tool designed to evaluate the synthetic feasibility of Metal-Organic Frameworks (MOFs). By integrating the Atomic Simulation Environment (ASE) to leverage extended tight-binding (xTB) and machine l…
  europepmc 2026 置信度 0.80
• Structurally robust quaterphenyl-dicarbonitrile 2D MOF nanopores on Cu(111) for cobalt spin-coordination motifs.
  We demonstrate a strategy for fabricating robust ∼2 nm nanopores on copper (Cu) surfaces using a nonmagnetic two-dimensional (2D) metal-organic framework (MOF), without the need for additional 3d metal atom deposition, that serves as a platform for constructin…
  europepmc 2026 置信度 0.80
• Extended Rice-Thomson analysis and atomistic simulations revealing grain boundary effects on fracture in refractory high-entropy alloys.
  Understanding how grain boundaries mediate fracture remains a critical challenge in designing ductile, high-performance refractory alloys. Here, we extend the Rice-Thomson criterion to account for the angle between cracks and the impinging grain boundaries (GB…
  europepmc 2026 置信度 0.80
• Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.
  In the era of artificial intelligence (AI), machine learning interatomic potentials (MLIPs) have revolutionized materials science and engineering, enabling large-scale and accurate atomistic modeling in various chemical systems. The performance of these potent…
  europepmc 2026 置信度 0.80
• Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials.
  Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs i…
  europepmc 2026 置信度 0.80
• Machine Learning-Based Composition Design of Functionally Graded Alloys.
  Functionally graded materials (FGMs) effectively alleviate residual stress induced by physical property mismatch at dissimilar material interfaces through a graded transition in composition or structure. Among these, the matching of the coefficient of thermal …
  europepmc 2026 置信度 0.80
• Equivariant Neural Networks Reveal How Host-Guest Interactions Shape <sup>129</sup>Xe NMR in Porous Liquids.
  Understanding host-guest interactions in porous liquids (PLs) formed from porous organic cages (POCs) is pivotal in tailoring their physicochemical properties, therefore providing an avenue for engineering new PLs with enhanced functionalities. In this work, w…
  europepmc 2025 置信度 0.80
• A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials.
  Abstract Transition-state (TS) characterization underpins reaction modeling but conventional DFT is costly. Machine-learning interatomic potentials (MLIPs) promise quantum-level accuracy at lower cost, yet, lacking large-scale Hessian data, most are pretrained…
  europepmc Taoyong Cui, Yunhong Han, Haojun Jia, Chenru Duan 等 2025 置信度 0.80
• Large-Scale Cooperative Sulfur Vacancy Dynamics in Two-Dimensional MoS<sub>2</sub> From Machine Learning Interatomic Potentials.
  ABSTRACT The formation of extended sulfur vacancies in MoS 2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond‐scale molecular dynamics simulations using machine learning interatomic pote…
  europepmc Aaron Flötotto, Benjamin Spetzler, Rose von Stackelberg, Martin Ziegler 等 2026 置信度 0.80
• Collapse of the Helical Hydrogen Bond Structure in Imidazolium Hydrogen <i>o</i>-Phthalate Under High Pressure: A Vibrational Spectroscopy Study.
  Imidazolium hydrogen o -phthalate (OrtImi) is an organic ionic crystal with a helical hydrogen-bonded network. We report on the evolution of the Raman, terahertz, and infrared modes of OrtImi over a wide range of temperatures and hydrostatic pressures. We inte…
  europepmc 2026 置信度 0.80
• Mechanistic insights into vacancy-driven activation and dissociation of hydrogen peroxide on Ti<sub>3</sub>C<sub>2</sub>O<sub>2</sub> MXene in water.
  The rational design of sophisticated oxidation and electrochemical systems depends on an understanding of how hydrogen peroxide (H 2 O 2 ) activates and dissociates on two-dimensional catalysts. Here, using a combination of density functional theory (DFT), nud…
  europepmc 2026 置信度 0.80
• OrbGNN: A Wave function-based Machine Learning Interelectronic Representation
  Machine learning interatomic potentials (MLIPs) have become emerging tools in molecular modeling and computational chemistry. By learning high-dimensional potential energy surfaces from quantum chemical data, MLIPs enable accurate and efficient predictions of …
  datacite Quebedeaux, Brody, Akram, Shahzad, Reiher, Markus, Vogiatzis, Konstantinos D. 2026 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciences
• High-quality, high-information datasets for universal atomistic machine learning
  The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many widely used electronic-structure databases are assembled having materials screening …
  datacite Malosso, Cesare, Bigi, Filippo, Pegolo, Paolo, Abbott, Joseph W. 等 2026 置信度 0.66 machine learningdensity-functional theorydatasetatomistic simulationsinteratomic potentials
• High-quality, high-information datasets for universal atomistic machine learning
  The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many widely used electronic-structure databases are assembled having materials screening …
  datacite Malosso, Cesare, Bigi, Filippo, Pegolo, Paolo, Abbott, Joseph W. 等 2026 置信度 0.66 machine learningdensity-functional theorydatasetatomistic simulationsinteratomic potentials
• Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics.
  Amorphous silicon ( a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic pot…
  datacite Deringer, Volker L, Bernstein, Noam, Bartók, Albert P, Cliffe, Matthew J 等 2018 置信度 0.66 3403 Macromolecular and Materials Chemistry34 Chemical SciencesNetworking and Information Technology R&D (NITRD)Machine Learning and Artificial IntelligenceGeneric health relevance
• Machine learning interatomic potential study of grain boundaries thermal conductance and tensile strength in hexagonal boron nitride
  Fig. S1.jpg shows the effect of system length on the room-temperature interfacial thermal conductance of the representative 0h-N-s grain boundary. hBN.extxyz and hBN.cfg contain the AIMD datasets generated in this work, provided in extended XYZ (EXTXYZ) and CF…
  datacite Mortazavi, Bohayra 2026 置信度 0.66 Machine LearningDensity Functional Theory
• Machine learning interatomic potential study of grain boundaries thermal conductance and tensile strength in hexagonal boron nitride
  Fig. S1.jpg shows the effect of system length on the room-temperature interfacial thermal conductance of the representative 0h-N-s grain boundary. hBN.extxyz and hBN.cfg contain the AIMD datasets generated in this work, provided in extended XYZ (EXTXYZ) and CF…
  datacite Mortazavi, Bohayra 2026 置信度 0.66 Machine LearningDensity Functional Theory
• ase-calculator-kit: a unified ASE calculator factory for MLIP and DFT calculators
  A thin, unified ASE calculator factory for machine-learning interatomic potentials (SevenNet, CHGNet, MatterSim, NequIP OAM, UMA/fairchem, MACE) and external DFT calculators (VASP, Quantum ESPRESSO). Every call returns a standard ase.Calculator, so the rest of…
  datacite Wakamiya, Taishiro, Ishikawa, Atsushi 2026 置信度 0.66 ASEmachine-learning interatomic potentialdensity functional theorycomputational chemistrymaterials science
• ase-calculator-kit: a unified ASE calculator factory for MLIP and DFT calculators
  A thin, unified ASE calculator factory for machine-learning interatomic potentials (SevenNet, CHGNet, MatterSim, NequIP OAM, UMA/fairchem, MACE) and external DFT calculators (VASP, Quantum ESPRESSO). Every call returns a standard ase.Calculator, so the rest of…
  datacite Wakamiya, Taishiro, Ishikawa, Atsushi 2026 置信度 0.66 ASEmachine-learning interatomic potentialdensity functional theorycomputational chemistrymaterials science
• Generalizing Abell-Tersoff bond-order potential with explicit high-order many-body correlations for robust extrapolation of potential energy surfaces
  Machine-learning interatomic potentials enable accurate and efficient atomistic simulations, yet their reliability for out-of-distribution configurations far beyond the training domain remains a significant challenge. Here, we introduce a semiparametric intera…
  datacite Kohata, Ikuma 2026 置信度 0.66 Chemical Physics (physics.chem-ph)Disordered Systems and Neural Networks (cond-mat.dis-nn)Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Benchmarking of Fast and Interpretable UF Machine Learning Potentials
  Machine learning interatomic potentials (MLIPs) have emerged as a powerful alternative to density functional theory (DFT) for molecular dynamics simulations, offering near-DFT accuracy at a fraction of the computational cost. However, many state-of-the-art MLI…
  datacite Prakash, Pawan, Dong, Sam, Hennig, Richard G. 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• Grain-Boundary Premelting in High-Entropy Transition Metal Carbides
  Grain-boundary segregation and thermally induced interfacial disordering were investigated in four high-entropy transition metal carbides using Monte Carlo (MC) sampling and molecular dynamics (MD) with the universal MACE-OMAT-0 machine-learning interatomic po…
  datacite Mou, Marium M., Schenck, Caleb, Daigle, Samuel E., Fahrenholtz, William G. 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• training and test data for the machine learning interatomic potential construction of six solid electrolytes
  This repository contains the training and test datasets used to train the machine-learning interatomic potentials (deep-potential models) reported in "Disentangling Cation–Polyanion Coupling in Solid Electrolytes: Which Anion Motion Dominates Cation Transport?…
  datacite Li, Ke 2026 置信度 0.66
• training and test data for the machine learning interatomic potential construction of six solid electrolytes
  This repository contains the training and test datasets used to train the machine-learning interatomic potentials (deep-potential models) reported in "Disentangling Cation–Polyanion Coupling in Solid Electrolytes: Which Anion Motion Dominates Cation Transport?…
  datacite Li, Ke 2026 置信度 0.66
• Enhancing the Efficiency and Flexibility of AutoMeKin: Integrating ORCA and Machine-Learning Potentials
  This repository contains the reaction-network data and level-of-theory benchmark for five automated reaction discovery (AutoMeKin) studies: cBD-CCH, cBD-CN, cBD-OH, Tz2-HA, and MEA. For each system it includes:- LL (low-level) exploration data, obtained with t…
  datacite Rodríguez López, Omar, Martinez-Nunez, Emilio, Vazquez, Saulo, Fernández, Berta 2026 置信度 0.66
• Enhancing the Efficiency and Flexibility of AutoMeKin: Integrating ORCA and Machine-Learning Potentials
  This repository contains the reaction-network data and level-of-theory benchmark for five automated reaction discovery (AutoMeKin) studies: cBD-CCH, cBD-CN, cBD-OH, Tz2-HA, and MEA. For each system it includes:- LL (low-level) exploration data, obtained with t…
  datacite Rodríguez López, Omar, Martinez-Nunez, Emilio, Vazquez, Saulo, Fernández, Berta 2026 置信度 0.66
• Active Learning of a Neural Network Potential for Large-Scale Atomistic Simulations of Polymer Electrolyte Membranes
  Machine learning interatomic potentials (MLIPs) can achieve near density-functional-theory (DFT) accuracy at force-field computational cost; however, long-time, large-scale molecular dynamics (MD) simulations often fail when trajectories sample local atomic en…
  datacite Yoshimoto, Yuta, Matsumura, Naoki, Yamazaki, Meguru, Iwasaki, Yuto 等 2025 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• High-throughput Discovery of Magnetic Rare Earth Transition Metal Alloys
  We present an accelerated materials discovery framework that combines diffusion-based crystal structure generation with hierarchical screening to identify new rare-earth--transition-metal magnets simultaneously achieving high magnetization and thermodynamic st…
  datacite Tao, Shuo, Ridwan, Osman Goni, Ke, Liqin, Zhu, Qiang 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• BoroML
  This archive contains various files and scripts linked with the articles listed in the How to cite section below. It includes sample LAMMPS input files, potential files for different machine learning interatomic potentials (MLIPs) for borophene on silver, and …
  datacite Bousige, Colin 2026 置信度 0.66 borophenereactive machine learned interaction potentialsn2p2DeePMDNNMP
• BoroML
  This archive contains various files and scripts linked with the articles listed in the How to cite section below. It includes sample LAMMPS input files, potential files for different machine learning interatomic potentials (MLIPs) for borophene on silver, and …
  datacite Bousige, Colin 2026 置信度 0.66 borophenereactive machine learned interaction potentialsn2p2DeePMDNNMP
• Dataset for the publication- A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films
  This deposit contains the dataset of the manuscript: "A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films" by Ankit Yadav, [co-authors], and Jan Fikar. Contents: - README.md- Main LaTeX source file (.tex)- Bibliogra…
  datacite Yadav, Ankit, Bajtošová, Lucia, Cieslar, Miroslav, Fikar, Jan 2026 置信度 0.66 nanocrystalline aluminumMolecular Dynamics Simulationgrain-boundaryinverse Hall–Petch
• Dataset for the publication- A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films
  This deposit contains the dataset of the manuscript: "A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films" by Ankit Yadav, [co-authors], and Jan Fikar. Contents: - README.md- Main LaTeX source file (.tex)- Bibliogra…
  datacite Yadav, Ankit, Bajtošová, Lucia, Cieslar, Miroslav, Fikar, Jan 2026 置信度 0.66 nanocrystalline aluminumMolecular Dynamics Simulationgrain-boundaryinverse Hall–Petch
• Preprint of the publication- A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films.
  This deposit contains the preprint of the manuscript: "A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films" by Ankit Yadav, [co-authors], and Jan Fikar. Contents:- Complied preprint file (.pdf)The manuscript introdu…
  datacite Yadav, Ankit, Bajtošová, Lucia, Cieslar, Miroslav, Fikar, Jan 2026 置信度 0.66 nanocrystalline aluminumMolecular Dynamics Simulationgrain-boundaryinverse Hall–Petch
• Preprint of the publication- A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films.
  This deposit contains the preprint of the manuscript: "A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films" by Ankit Yadav, [co-authors], and Jan Fikar. Contents:- Complied preprint file (.pdf)The manuscript introdu…
  datacite Yadav, Ankit, Bajtošová, Lucia, Cieslar, Miroslav, Fikar, Jan 2026 置信度 0.66 nanocrystalline aluminumMolecular Dynamics Simulationgrain-boundaryinverse Hall–Petch
• mlmm-toolkit
  mlmm-toolkit v0.3.0 mlmm-toolkit is an open-source CLI for ML/MM ONIOM analyses of enzymatic reactions. It replaces the QM region of conventional QM/MM with a machine-learning interatomic potential (MLIP, default: UMA) while keeping the surrounding protein und…
  datacite Ohmura, Takuto 2026 置信度 0.66
• Structure-Agnostic Prediction of the Electronic Density of States with a Chemical Language Model
  The electronic density of states (DOS) is conventionally computed from a relaxed crystal structure, which is unavailable for compounds that have been neither synthesized nor cataloged. Here we introduce DOSSIER ($\textbf{D}$ensity $\textbf{o}$f $\textbf{S}$tat…
  datacite Rubtsov, Ivan D., Dudakov, Ivan V., Korolev, Vadim V. 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Equivariant Cellular Sheaves for Molecular Electronic Structure: Bridging Sheaf Cohomology and E(3)-Equivariant Hamiltonian Learning
  Equivariant message-passing networks are the standard model for molecular property and interatomic-potential prediction, and recent work predicts the electronic Hamiltonian itself in an E(3)-equivariant way. Separately, topological deep learning has extended g…
  datacite Harish, Krishna 2026 置信度 0.66 Machine Learning (cs.LG)Chemical Physics (physics.chem-ph)FOS: Computer and information sciencesFOS: Physical sciences
• Comparison of Elastic Constants and Surface Energies of β-Sn from Density Functional Theory, Universal Machine Learning Potential, and Empirical Potentials — data and code
  Companion data and code repository for "Comparison of Elastic Constants and Surface Energies of β-Sn from Density Functional Theory, Universal Machine Learning Potential, and Empirical Potentials" (Modelling and Simulation in Materials Science and Engineering …
  datacite Tatsumi, Hiroaki, Ito, Atsushi M., Takayama, Arimichi, Nishikawa, Hiroshi 2026 置信度 0.66 beta-tinelastic constantssurface energyWulff constructiondensity functional theory
• Comparison of Elastic Constants and Surface Energies of β-Sn from Density Functional Theory, Universal Machine Learning Potential, and Empirical Potentials — data and code
  Companion data and code repository for "Comparison of Elastic Constants and Surface Energies of β-Sn from Density Functional Theory, Universal Machine Learning Potential, and Empirical Potentials" (Modelling and Simulation in Materials Science and Engineering …
  datacite Tatsumi, Hiroaki, Ito, Atsushi M., Takayama, Arimichi, Nishikawa, Hiroshi 2026 置信度 0.66 beta-tinelastic constantssurface energyWulff constructiondensity functional theory
• Isurwars/Correlation: Correlation 3.7
  [3.7.0] - 2026-07-24 Added Multi-Vendor GPU Acceleration Framework: Implemented SYCL-based GPU acceleration for distance, $S(Q)$, and Steinhardt parameter calculators (GPUDistanceCalculator, GPUSQCalculator, GPUSteinhardtCalculator) with runtime fallback and d…
  datacite Isaías Rodríguez Aguirre, Mineralwater Xu 2026 置信度 0.66
• Isurwars/Correlation: Correlation 3.8.6
  Changelog All notable changes to this project will be documented in this file. [3.8.6] - 2026-08-04 Added & Refactored Application Settings Persistence: Implemented SettingsManager for JSON-backed user settings persistence across sessions, integrated into AppC…
  datacite Isaías Rodríguez Aguirre, Mineralwater Xu 2026 置信度 0.66
• Isurwars/Correlation: Correlation 3.8.6
  Changelog All notable changes to this project will be documented in this file. [3.8.6] - 2026-08-04 Added & Refactored Application Settings Persistence: Implemented SettingsManager for JSON-backed user settings persistence across sessions, integrated into AppC…
  datacite Isaías Rodríguez Aguirre, Mineralwater Xu 2026 置信度 0.66
• Isurwars/Correlation: Correlation 3.8.6
  Changelog All notable changes to this project will be documented in this file. [3.8.6] - 2026-08-04 Added & Refactored Application Settings Persistence: Implemented SettingsManager for JSON-backed user settings persistence across sessions, integrated into AppC…
  datacite Isaías Rodríguez Aguirre, Mineralwater Xu 2026 置信度 0.66
• Data and code for local hydrogen energetics and lattice-controlled mobility
  Curated DFT inputs and outputs, archived machine-learning-potential result arrays, analysis tables, figure-generation code and exact pseudopotentials supporting a manuscript on local hydrogen energetics and lattice-controlled mobility.
  datacite Lee, Ugwiyeon 2026 置信度 0.66 hydrogendensity functional theorymolecular dynamicsmachine-learning interatomic potentialhigh-entropy alloy
• Data and code for local hydrogen energetics and lattice-controlled mobility
  Curated DFT inputs and outputs, archived machine-learning-potential result arrays, analysis tables, figure-generation code and exact pseudopotentials supporting a manuscript on local hydrogen energetics and lattice-controlled mobility.
  datacite Lee, Ugwiyeon 2026 置信度 0.66 hydrogendensity functional theorymolecular dynamicsmachine-learning interatomic potentialhigh-entropy alloy
• Raw Trajectory Data for NPT Thermal Cycling of an Fe-Cr-C-Mn-Si-W Alloy Using a Deep Potential
  This dataset contains the raw LAMMPS atomic trajectory generated during the NPT thermal cycling simulation of an Fe-Cr-C-Mn-Si-W alloy using a trained deep potential. The system contains 16,148 atoms and was heated from 300 K to 1850 K, held at 1850 K, and sub…
  datacite Zhang, Yinglong, Dong, Chen, Zhang, Hongpeng, Fang, Xuan 等 2026 置信度 0.66 Deep Potential DeepMD-kit LAMMPS Molecular dynamics Thermal cycling SIMP steel Fe-Cr-C-Mn-Si-W Machine-learning interatomic potential
• Raw Trajectory Data for NPT Thermal Cycling of an Fe-Cr-C-Mn-Si-W Alloy Using a Deep Potential
  This dataset contains the raw LAMMPS atomic trajectory generated during the NPT thermal cycling simulation of an Fe-Cr-C-Mn-Si-W alloy using a trained deep potential. The system contains 16,148 atoms and was heated from 300 K to 1850 K, held at 1850 K, and sub…
  datacite Zhang, Yinglong, Dong, Chen, Zhang, Hongpeng, Fang, Xuan 等 2026 置信度 0.66 Deep Potential DeepMD-kit LAMMPS Molecular dynamics Thermal cycling SIMP steel Fe-Cr-C-Mn-Si-W Machine-learning interatomic potential
• First-Principles Atomistic Structure and Dynamics of Polyethylene During High-Pressure Radical Polymerization via Machine Learning Force Fields
  Polyethylene (PE) is one of the most commonly used synthetic polymers. While the synthesis and processing protocols for PE are well established, precise experimental assignment of microscopic structures at atomistic resolution (i.e., the position of each atom)…
  datacite Gunawardana, Bharatha K., Shah, Teresa, Azizova, Bicha, Ranabhat, Deepa 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Disordered Systems and Neural Networks (cond-mat.dis-nn)Machine Learning (cs.LG)Chemical Physics (physics.chem-ph)FOS: Physical sciences
• Dataset for: Out-of-Distribution Robust Active Learning of Message-Passing Potentials for Extreme Non-Equilibrium Iron Dynamics
  The simulation of extreme non-equilibrium materials phenomena, such as shock compression and extreme shear in body-centered cubic (BCC) iron, demands the quantum-mechanical fidelity of \textit{ab initio} methods at lengths and timescales accessible only to emp…
  datacite Fikri, Ahmad Atif, Suryanto, Heru, Ahmad, Ahmad Al Kafi, Permanasari, Avita Ayu Permanasari 等 2026 置信度 0.66 Machine Learning Interatomic PotentialsActive LearningOut-of-DistributionExtreme ConditionsBCC Iron
• Dataset for: Out-of-Distribution Robust Active Learning of Message-Passing Potentials for Extreme Non-Equilibrium Iron Dynamics
  The simulation of extreme non-equilibrium materials phenomena, such as shock compression and extreme shear in body-centered cubic (BCC) iron, demands the quantum-mechanical fidelity of \textit{ab initio} methods at lengths and timescales accessible only to emp…
  datacite Fikri, Ahmad Atif, Suryanto, Heru, Ahmad, Ahmad Al Kafi, Permanasari, Avita Ayu Permanasari 等 2026 置信度 0.66 Machine Learning Interatomic PotentialsActive LearningOut-of-DistributionExtreme ConditionsBCC Iron
• vasp-mace: a VASP simulator based on the MACE machine-learning interatomic potential
  VASP-like interface for structure relaxation, molecular dynamics, and energy calculations using MACE machine-learning interatomic potentials. Reads POSCAR/INCAR inputs and produces CONTCAR, OUTCAR, OSZICAR, XDATCAR, and vasprun.xml outputs compatible with stan…
  datacite Grau-Crespo, Ricardo 2026 置信度 0.66 MACEmachine learning potentialsmolecular dynamicsgeometry relaxationVASP
• vasp-mace: a VASP simulator based on the MACE machine-learning interatomic potential
  VASP-like interface for structure relaxation, molecular dynamics, and energy calculations using MACE machine-learning interatomic potentials. Reads POSCAR/INCAR inputs and produces CONTCAR, OUTCAR, OSZICAR, XDATCAR, and vasprun.xml outputs compatible with stan…
  datacite Grau-Crespo, Ricardo 2026 置信度 0.66 MACEmachine learning potentialsmolecular dynamicsgeometry relaxationVASP
• Y-Mn-B Magnetic Materials: A DFT and Machine Learning Dataset
  This dataset contains the crystallographic structures, thermodynamic-stability data, and selected phonon and electronic-structure outputs supporting the manuscript: “Discovery of novel magnetic Y-Mn-B compounds via advanced machine learning guided framework.” …
  datacite Xia, Weiyi, Tee, Wei-Shen, Moraru, Maxim, Li, Ying Wai 等 2026 置信度 0.66
• Y-Mn-B Magnetic Materials: A DFT and Machine Learning Dataset
  This dataset contains the crystallographic structures, thermodynamic-stability data, and selected phonon and electronic-structure outputs supporting the manuscript: “Discovery of novel magnetic Y-Mn-B compounds via advanced machine learning guided framework.” …
  datacite Xia, Weiyi, Tee, Wei-Shen, Moraru, Maxim, Li, Ying Wai 等 2026 置信度 0.66
• Machine-learned potential for fission gas diffusion in uranium oxide nuclear fuels
  We report the first machine-learned interatomic potential for uranium dioxide with xenon gas, as well as a foundational machine-learned potential for uranium dioxide. Training datasets were constructed by leveraging a combination of density functional theory c…
  datacite Miles, Audrey R, Monserrat, Bartomeu, Finkeldei, Sarah C 2026 置信度 0.66
• Quantum-corrected NMR crystallography at scale
  Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, computational cost limits the accuracy of shielding predictions, that usually rely o…
  datacite Kellner, Matthias, Rodriguez-Madrid, Ruben, Holmes, Jacob B., Principe, Victor Paul 等 2026 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciences
• HIP: Hessian Interatomic Potentials without derivatives
  Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate Hessians are computationally expensive to calculate and scale poorly with system size, whether computed using qu…
  datacite Burger, Andreas, Thiede, Luca, Rønne, Nikolaj, Bernales, Varinia 等 2025 置信度 0.66 Machine Learning (cs.LG)Chemical Physics (physics.chem-ph)Computational Physics (physics.comp-ph)FOS: Computer and information sciencesFOS: Physical sciences
• Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections
  Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictions have matured significantly and today are primarily limited by the electronic …
  datacite Kellner, Matthias, Rodriguez-Madrid, Ruben, Holmes, Jacob B., Unzueta, Pablo A. 等 2026 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciences
• Training and Test Datasets for a MACE Interatomic Potential for SiO2
  This dataset contains the training and test configurations used to develop a MACE machine-learning interatomic potential for SiO2. It includes several crystalline SiO2 polymorphs and combines equation-of-state, phonon-displacement, and randomly distorted confi…
  datacite Diaz Rodriguez, Hernan, Otero de la Roza, Alberto, Suárez Recio, Jorge 2026 置信度 0.66 SiO2training datasetDensity Functional Theoryatomistic simulationsmachine learning interatomic potential
• Training and Test Datasets for a MACE Interatomic Potential for SiO2
  This dataset contains the training and test configurations used to develop a MACE machine-learning interatomic potential for SiO2. It includes several crystalline SiO2 polymorphs and combines equation-of-state, phonon-displacement, and randomly distorted confi…
  datacite Diaz Rodriguez, Hernan, Otero de la Roza, Alberto, Suárez Recio, Jorge 2026 置信度 0.66 SiO2training datasetDensity Functional Theoryatomistic simulationsmachine learning interatomic potential
• AI_for_Crystalline_Materials_corse_data
  Course data package for “AI for Materials Science — MINI Course”, developed by Mikhail Lazarev. This record contains the curated input datasets, crystal structures and reference subsets used by the eight practical notebooks of the course. The data are distribu…
  datacite Lazarev, Mikhail 2026 置信度 0.66
• AI_for_Crystalline_Materials_corse_data
  Course data package for “AI for Materials Science — MINI Course”, developed by Mikhail Lazarev. This record contains the curated input datasets, crystal structures and reference subsets used by the eight practical notebooks of the course. The data are distribu…
  datacite Lazarev, Mikhail 2026 置信度 0.66
• The CHGNet uMLIP model fine-tuned for 2Hc-WS2
  The CHGNet uMLIP model fine-tuned for 2Hc-WS2 in P. Žguns, I. Pudza, A. Kuzmin, Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2, J. Chem. Theory Comput. 21 (2025) 8142–8150. Doi: 10.1021/…
  datacite Žguns, Pjotrs 2026 置信度 0.66
• The CHGNet uMLIP model fine-tuned for 2Hc-WS2
  The CHGNet uMLIP model fine-tuned for 2Hc-WS2 in P. Žguns, I. Pudza, A. Kuzmin, Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2, J. Chem. Theory Comput. 21 (2025) 8142–8150. Doi: 10.1021/…
  datacite Žguns, Pjotrs 2026 置信度 0.66
• Data-Driven Analysis of Nuclear Resonance Vibrational Spectra with Machine-Learning Potentials
  Data-Driven Analysis of Nuclear Resonance Vibrational Spectra with Machine-Learning Potentials Nuclear Resonance Vibrational Spectroscopy (NRVS) is a synchrotron-based inelastic X-ray scattering technique that probes the vibrational density of states projected…
  datacite Rulev, Alexey, Braun, Artur 2026 置信度 0.66 LiFePO4DFTMolecular DynamicsMachine LearningNRVS
• Data-Driven Analysis of Nuclear Resonance Vibrational Spectra with Machine-Learning Potentials
  Data-Driven Analysis of Nuclear Resonance Vibrational Spectra with Machine-Learning Potentials Nuclear Resonance Vibrational Spectroscopy (NRVS) is a synchrotron-based inelastic X-ray scattering technique that probes the vibrational density of states projected…
  datacite Rulev, Alexey, Braun, Artur 2026 置信度 0.66 LiFePO4DFTMolecular DynamicsMachine LearningNRVS
• Protocol-specific Quantum ESPRESSO energies and atomic forces for thirteen doped zirconia chemistries
  A balanced collection of 104 protocol-specific PBE single-point calculations for 13 doped-zirconia chemistries. Each chemistry contributes three migration-related structures and five MACE molecular-dynamics snapshots with nominal thermostat labels. The labels …
  datacite Zhang, Qikai, Jin, Zhihao, Chen, Xianfu, Xiong, Hao 等 2026 置信度 0.66 zirconiadensity functional theoryatomic forcesQuantum ESPRESSOmachine learning interatomic potential
• Protocol-specific Quantum ESPRESSO energies and atomic forces for thirteen doped zirconia chemistries
  A balanced collection of 104 protocol-specific PBE single-point calculations for 13 doped-zirconia chemistries. Each chemistry contributes three migration-related structures and five MACE molecular-dynamics snapshots with nominal thermostat labels. The labels …
  datacite Zhang, Qikai, Jin, Zhihao, Chen, Xianfu, Xiong, Hao 等 2026 置信度 0.66 zirconiadensity functional theoryatomic forcesQuantum ESPRESSOmachine learning interatomic potential
• Protocol-specific Quantum ESPRESSO energies and atomic forces for thirteen doped zirconia chemistries
  This V2.3 release contains 104 protocol-specific PBE single-point calculations for 13 doped-zirconia chemistries. Each chemistry contributes three migration-related structures and five snapshots from frozen MACE molecular dynamics trajectories. The temperature…
  datacite Zhang, Qikai, Jin, Zhihao, Chen, Xianfu, Xiong, Hao 等 2026 置信度 0.66 zirconiadensity functional theoryatomic forcesQuantum ESPRESSOmachine learning interatomic potential
• Resolving pressure–charge–reaction coupling at solid-state battery interfaces with machine-learning molecular dynamics
  This dataset contains the initial Li/Li6PS5Cl (Li/LPSC) interfacial structure and the DFT-labeled training configurations used to construct the pressure-aware Deep Potential models. The training data include Li metal, LPSC bulk,and chemically distinct Li/LPSC …
  datacite Jang, Kunik 2026 置信度 0.66 machine learning interatomic potentialLPSCMolecular dynimics
• Resolving pressure–charge–reaction coupling at solid-state battery interfaces with machine-learning molecular dynamics
  This dataset contains the initial Li/Li6PS5Cl (Li/LPSC) interfacial structure and the DFT-labeled training configurations used to construct the pressure-aware Deep Potential models. The training data include Li metal, LPSC bulk,and chemically distinct Li/LPSC …
  datacite Jang, Kunik 2026 置信度 0.66 machine learning interatomic potentialLPSCMolecular dynimics
• Exploring celecoxib polymorph landscape using AIMNet2 machine learning interatomic potential.
  The crystal form a drug adopts can change everything from how it dissolves to whether it works in the clinic, yet predicting which polymorphs a flexible molecule will produce remains one of the most stubborn problems in pharmaceutical science. Competing forms …
  datacite Sun, Changquan Calvin, Zheng, Peikun, Abramov, Yuriy A, Isayev, Olexandr 2026 置信度 0.66
• Crystal structures, energies and search databases for "Bayesian Active Crystal Hopping: Machine-Learned Potentials and Surrogate-Guided Sampling for Crystal Structure Prediction of π-Extended Organic Molecules"
  Primary data for three crystal-structure-prediction searches on tris(4-methoxyphenyl)amine-anthracene (TMPA-An, C42H36N2O4, Z = 4): the BACH active-learning search reported in the article (314 relaxed structures), an ablation of the same workflow with the Gaus…
  datacite Flores-Mena, Raul Rodolfo, Campos-Almazán, Mara Ibeth, Sánchez-Bojorge, Nora-Aydeé, Landeros-Martínez, Linda-Lucila 等 2026 置信度 0.66 Crystal Structure PredictionBayesian optimizationGaussian ProcessActive LearningMachine-Learned Interatomic Potential
• Crystal structures, energies and search databases for "Bayesian Active Crystal Hopping: Machine-Learned Potentials and Surrogate-Guided Sampling for Crystal Structure Prediction of π-Extended Organic Molecules"
  Primary data for three crystal-structure-prediction searches on tris(4-methoxyphenyl)amine-anthracene (TMPA-An, C42H36N2O4, Z = 4): the BACH active-learning search reported in the article (314 relaxed structures), an ablation of the same workflow with the Gaus…
  datacite Flores-Mena, Raul Rodolfo, Campos-Almazán, Mara Ibeth, Sánchez-Bojorge, Nora-Aydeé, Landeros-Martínez, Linda-Lucila 等 2026 置信度 0.66 Crystal Structure PredictionBayesian optimizationGaussian ProcessActive LearningMachine-Learned Interatomic Potential
• CrYAL: Crystal Structure Prediction via Active Learning
  Reference implementation of BACH (Bayesian Active Crystal Hopping), a blind crystal-structure-prediction workflow for molecular organic crystals. It couples symmetry-aware random generation, relaxation on a machine-learned interatomic potential (MACE-OFF23 thr…
  datacite Flores Mena, Raúl Rodolfo 2026 置信度 0.66 crystal structure predictionBayesian optimizationactive learningGaussian processmachine-learned interatomic potential
• CrYAL: Crystal Structure Prediction via Active Learning
  Reference implementation of BACH (Bayesian Active Crystal Hopping), a blind crystal-structure-prediction workflow for molecular organic crystals. It couples symmetry-aware random generation, relaxation on a machine-learned interatomic potential (MACE-OFF23 thr…
  datacite Flores Mena, Raúl Rodolfo 2026 置信度 0.66 crystal structure predictionBayesian optimizationactive learningGaussian processmachine-learned interatomic potential
• Discovery of novel magnetic Y-Mn-B compounds via advanced machine learning guided framework
  Rare-earth transition-metal borides offer critical structural motifs for permanent-magnet design; however, the manganese-rich regions within these compositional phase spaces remain largely unexplored. In this work, we develop an advanced machine-learning-assis…
  datacite Xia, Weiyi, Tee, Wei Shen, Moraru, Maxim, Li, Ying Wai 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• Temperature-Induced Reorganization of Supported Zn$_3$ Clusters on Cu(111): From Minimum-Energy Structures to Finite-Temperature Ensembles
  Understanding the nature of catalytic active sites under reaction conditions remains a central challenge in heterogeneous catalysis. In industrial copper/zinc oxide/alumina catalysts for methanol synthesis, small Zn-based species at the Cu interface have long …
  datacite Xu, Jiayan, Yu, Zheng, Patra, Abhirup, Pathak, Amar Deep 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• AIMNet2-rxn: Code and Pretrained Models for "AIMNet2-rxn: A Machine Learned Potential for Generalized Reaction Modeling on a Millions-of-Pathways Scale"
  Archived source code and pretrained model weights supporting the paper. Contains a snapshot of the isayevlab/aimnetcentral repository (AIMNet2 architecture, AIMNet2-rxn model configuration, model registry, pysisyphus calculator integration, training scripts) a…
  datacite Anstine, Dylan M., Zhao, Qiyuan, Zubatyuk, Roman, Zhang, Shuhao 等 2026 置信度 0.66 machine learning interatomic potentialneural network potentialreaction modelingtransition statenudged elastic band
• AIMNet2-rxn: Code and Pretrained Models for "AIMNet2-rxn: A Machine Learned Potential for Generalized Reaction Modeling on a Millions-of-Pathways Scale"
  Archived source code and pretrained model weights supporting the paper. Contains a snapshot of the isayevlab/aimnetcentral repository (AIMNet2 architecture, AIMNet2-rxn model configuration, model registry, pysisyphus calculator integration, training scripts) a…
  datacite Anstine, Dylan M., Zhao, Qiyuan, Zubatyuk, Roman, Zhang, Shuhao 等 2026 置信度 0.66 machine learning interatomic potentialneural network potentialreaction modelingtransition statenudged elastic band
• High-throughput inverse design of a stable topological chalcogenide: The mixed-mass decoupling principle and the priority candidate Ta3PbS6
  Computational Materials Data Package: High-throughput inverse design of a stable topological chalcogenide: The mixed-mass decoupling principle and the priority candidate Ta3PbS6 Associated Manuscript "High-throughput inverse design of a stable topological chal…
  datacite Peinador Sala, José Ignacio 2026 置信度 0.66 SuperconductivityTa3PbS6Phonon density of states (DOS)CHGNetSupervised Machine Learning
• High-throughput inverse design of a stable topological chalcogenide: The mixed-mass decoupling principle and the priority candidate Ta3PbS6
  Computational Materials Data Package: High-throughput inverse design of a stable topological chalcogenide: The mixed-mass decoupling principle and the priority candidate Ta3PbS6 Associated Manuscript "High-throughput inverse design of a stable topological chal…
  datacite Peinador Sala, José Ignacio 2026 置信度 0.66 SuperconductivityTa3PbS6Phonon density of states (DOS)CHGNetSupervised Machine Learning
• Training sets for A unified microscopic picture of cation and anion migration in MAPbI3
  The training sets of the MAPbI3 machine learning potentials used in arXiv.2605.02685 are provided. These training sets, in combination with the training parameters provided in the publication, can be used for generating the force fields. The training sets are …
  datacite Tyagi, Viren 2026 置信度 0.66