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• Effect of Surface Interatomic Potential on Thermal Accommodation Coefficients Derived from Molecular Dynamics
  This\nwork investigates how the interatomic surface potential influences\nmolecular dynamics (MD)-derived thermal accommodation coefficients\n(TACs). Iron, copper, and silicon surfaces are considered over a range\nof temperatures that include their melting poi…
  crossref 2020-04-09T08:03:00Z 置信度 0.70
• Decision letter for "Modelling Silica using MACE-MP Machine Learnt Interatomic Potentials"
  crossref 2025-07-21T21:05:55Z 置信度 0.70
• Chirality-Dependent Mechanical Response of Graphyne-Based Multi-Ring Carbon Nanotubes (PolyPyGy Nanotubes) via Machine-Learning Interatomic Potentials
  The rapid progress in synthesis control and theoretical predictions for 2D and quasi-2D materials is enabling an unprecedented opportunity to explore the infinite possibilities in materials science through rational, reliable exploration. Graphyne-based nanotub…
  crossref Gesiel Gomes Silva, Rodrigo Alkimim Faria Alves, Alysson Martins Silva, Tiago de Sousa Araujo Cassiano 等 2026-06-26T06:37:11Z 置信度 0.70
• Experimental Validation of Universal Machine Learning Interatomic Potentials for Lithium-Ion Dynamics in Solid Electrolytes via 7Li NMR
  A significant step toward electrification is the adoption of more sustainable energy storage technologies such as batteries. Safer alternatives to conventional solution-state lithium-ion batteries (LIBs) are solid-state batteries, which employ solid-state elec…
  crossref Cameron A. Gurwell, Taiana L. E. Pereira, Mengyang Cui, Carlos A. Martins Junior 等 2026-04-28T08:55:26Z 置信度 0.70
• Effect of Surface Interatomic Potential on Thermal Accommodation Coefficients Derived from Molecular Dynamics
  This\nwork investigates how the interatomic surface potential influences\nmolecular dynamics (MD)-derived thermal accommodation coefficients\n(TACs). Iron, copper, and silicon surfaces are considered over a range\nof temperatures that include their melting poi…
  crossref 2020-04-09T08:03:00Z 置信度 0.70
• Leveraging the Potential of Machine-Learning Interatomic Potentials for QM/MM Simulations
  Machine-learning interatomic potentials (MLIPs) are increasingly used to replace computationally expensive quantum-mechanical (QM) calculations to obtain the energies and forces in ab initio or multiscale molecular dynamics (MD) simulations. While the computat…
  datacite Kuhn, Antonia S., Gordiy, Igor, Pultar, Felix, Riniker, Sereina 2026 置信度 0.66 Machine learningMolecular dynamicsMultiscale simulationsNeural network potentialsQM/MM
• 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
• FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
  Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensive due to the need to explore a large search space with sub-kJ/mol accuracy to di…
  datacite Gharakhanyan, Vahe, Yang, Yi, Barroso-Luque, Luis, Levine, Daniel S. 等 2025 置信度 0.66 Chemical Physics (physics.chem-ph)Machine Learning (cs.LG)FOS: Physical sciencesFOS: Computer and information sciences
• dpti: An Automated Thermodynamic Integration Workflow for Phase Diagram Calculations with Machine Learning Interatomic Potentials
  Thermodynamic integration (TI) is a widely used approach for computing free energies and phase diagrams. However, TI calculations driven by machine learning interatomic potentials (MLIPs) remain technically challenging because they require careful design of re…
  datacite Yuan, Fengbo, Zhong, Xin, Zheng, Donghao, Zeng, Jinzhe 等 2026 置信度 0.66 Computational Physics (physics.comp-ph)Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Data and scripts for "Displacive quantum critical point in superconducting hydrides: The case of H3S"
  The datasets support the paper "Displacive quantum critical point in superconducting hydrides: The case of H3S" The files contain Final MACE machine learning interatomic potential used for all simulations (MACE.model) The training set (total_train.xyz, total_t…
  datacite Cherubini, Marco, Raghav, Abhishek, Casula, Michele 2026 置信度 0.66
• Data and scripts for "Displacive quantum critical point in superconducting hydrides: The case of H3S"
  The datasets support the paper "Displacive quantum critical point in superconducting hydrides: The case of H3S" The files contain Final MACE machine learning interatomic potential used for all simulations (MACE.model) The training set (total_train.xyz, total_t…
  datacite Cherubini, Marco, Raghav, Abhishek, Casula, Michele 2026 置信度 0.66
• Complex crystal structure prediction using ML-enhanced multi-minima iterative genetic algorithm
  Current machine learning (ML) approaches for materials discovery rely heavily on known structural databases, limiting their ability to identify entirely novel structure types. In this work, we develop a multi-minima iterative genetic algorithm (MMIGA) that int…
  datacite Tang, Ling, Xia, Weiyi, Slade, Tyler J., Canfield, Paul C. 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• A general-purpose atomic cluster expansion interatomic potential for niobium
  Niobium, a body-centered cubic transition metal, poses a challenge for interatomic potentials, which struggle to capture its properties, such as phonons, high-pressure behavior, energy barriers to dislocation glide, and others. To tackle this challenge, we con…
  datacite Egorov, Aleksei, Drautz, Ralf, Hammerschmidt, Thomas 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Contrastive Regularization of Machine Learning Potentials
  Machine learning interatomic potentials are trained to predict energies and forces but built to be sampled: their purpose is to drive molecular simulations whose observables average over the equilibrium distribution the potential defines. They exemplify a broa…
  datacite Tzivrailis, Dimitrios, Sotiropoulos, Georgios, Rosso, Alberto, Kawasaki, Eiji 2026 置信度 0.66 Chemical Physics (physics.chem-ph)Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Adaptive fine-tuning of foundation models for crystal structure prediction: Discovery of high-pressure phases in the CaFeNi system
  The prediction of crystal structures is a key challenge in chemistry and materials science, but evolutionary crystal structure prediction (CSP) remains computationally expensive because it relies on repeated \textit{ab initio} relaxations and energy ranking. M…
  datacite Chtchelkatchev, N. M., Magnitskaya, M. V., Ryltsev, R. E. 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
  The characterization of nanostructured materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformations. Global optimization approaches allow predicting stable structures for target…
  datacite Dominguez, Jon Eunan Quinlivan, Christiansen, Mads-Peter Verner, Neyman, Konstantin M., Hammer, Bøjrk 等 2025 置信度 0.66 Materials Science (cond-mat.mtrl-sci)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
• Development and application of machine-learned interatomic potentials for the study of high-temperature TiAlNb alloys
  The lack of accurate and efficient interatomic potentials for technologically vital TiAlNb alloys has hindered their computational design and optimization for aerospace and energy applications. The thesis addresses the gap through a hierarchical investigation …
  datacite Chandran, Anju 2026 置信度 0.66 Technology::660: Chemistry; Chemical Engineering
• Local lattice dynamics of hcp zinc from EXAFS and machine-learning interatomic potentials
  The lattice dynamics of hexagonal close-packed (hcp) zinc, a prototypical anisotropic metal, is studied using temperature-dependent Zn K-edge extended X-ray absorption fine structure (EXAFS) spectroscopy combined with atomistic simulations. The reverse Monte C…
  datacite Dimitrijevs, Vitalijs, Zguns, Pjotrs, Pudza, Inga, Kalinko, Aleksandr 等 2026 置信度 0.66 530
• Molecular mechanism of ice nucleation on feldspar
  Understanding how water transforms into ice at complex surfaces is central to a wide range of natural and technological processes, yet molecular simulations of this transformation have largely been restricted to idealized surfaces or used models with limited p…
  datacite Zhou, Wanqi, Piaggi, Pablo M. 2025 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciences
• Thermodynamic database for the Mo–Si–Y–Hf quaternary system
  CALPHAD thermodynamic database for the Mo–Si–Y–Hf quaternary system, in TDB format compatible with pycalphad, Thermo-Calc, and OpenCalphad. The database contains 20 phases (LIQUID, BCC_A2, HCP_A3, DIAMOND_A4, three Mo-silicides, six Hf-silicides, seven Y-silic…
  datacite selagam setty, Pranav Preetam 2026 置信度 0.66 CALPHADthermodynamic databasepycalphad
• Thermodynamic database for the Mo–Si–Y–Hf quaternary system
  CALPHAD thermodynamic database for the Mo–Si–Y–Hf quaternary system, in TDB format compatible with pycalphad, Thermo-Calc, and OpenCalphad. The database contains 20 phases (LIQUID, BCC_A2, HCP_A3, DIAMOND_A4, three Mo-silicides, six Hf-silicides, seven Y-silic…
  datacite selagam setty, Pranav Preetam 2026 置信度 0.66 CALPHADthermodynamic databasepycalphad
• Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
  This repository contains the training data, model files, input/configuration files, and analysis scripts supporting the above study. Universal machine-learning interatomic potentials (MLIPs) are becoming general-purpose tools for atomistic simulation, but thei…
  datacite Hänseroth, Jonas, Flötotto, Aaron, Dreßler, Christian 2026 置信度 0.66
• Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
  This repository contains the training data, model files, input/configuration files, and analysis scripts supporting the above study. Universal machine-learning interatomic potentials (MLIPs) are becoming general-purpose tools for atomistic simulation, but thei…
  datacite Hänseroth, Jonas, Flötotto, Aaron, Dreßler, Christian 2026 置信度 0.66
• Data and supporting information to paper entitled "Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products"
  This directory contains data accompanying the manuscript " Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products. " The files support development of the MLIP4SiC-Mg machine-learning interatomic potential for 3C-SiC with intrinsic defect…
  datacite Morgan, Dane, Shen, Chen, P. Polak, Maciej, Szlufarska, Izabela 等 2026 置信度 0.66 Nuclear physics
• Data and supporting information to paper entitled "Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products"
  This directory contains data accompanying the manuscript " Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products. " The files support development of the MLIP4SiC-Mg machine-learning interatomic potential for 3C-SiC with intrinsic defect…
  datacite Morgan, Dane, Shen, Chen, P. Polak, Maciej, Szlufarska, Izabela 等 2026 置信度 0.66 Nuclear physics
• Deciphering borophene growth pathways with data-driven simulations
  Deterministic synthesis of borophene remains challenging because many polymorphs compete during nucleation and growth. Here we combine a reactive machine-learned interatomic potential with grand-canonical Monte Carlo simulations and data-driven structural clas…
  datacite Bousige, Colin, Furstoss, Jean, Lam, Julien, Mignon, Pierre 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)FOS: Physical sciencesFOS: Physical sciences
• From Atoms to Devices: Multiphysics Modeling and Machine Learning for Nanoscale Thermal Management
  Thermal management is a critical bottleneck for all avenues of modern human society, like infrastructure, power generation and storage, large datacenters, electronics, vehicles, space technology, and many others. For example, data centers in the United States …
  datacite Khot, Krutarth Hemant 2026 置信度 0.66 NanoelectronicsCondensed matter modelling and density functional theoryMachine learning not elsewhere classified
• From Atoms to Devices: Multiphysics Modeling and Machine Learning for Nanoscale Thermal Management
  Thermal management is a critical bottleneck for all avenues of modern human society, like infrastructure, power generation and storage, large datacenters, electronics, vehicles, space technology, and many others. For example, data centers in the United States …
  datacite Khot, Krutarth Hemant 2026 置信度 0.66 NanoelectronicsCondensed matter modelling and density functional theoryMachine learning not elsewhere classified
• Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
  Universal machine-learning interatomic potentials (MLIPs) are rapidly becoming general-purpose tools for atomistic simulation, but their role in quantitative materials modeling when reactive events are involved remains unsettled. We compare five universal MLIP…
  datacite Hänseroth, Jonas, Flötotto, Aaron, Dreßler, Christian 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Chemical Physics (physics.chem-ph)FOS: Physical sciencesFOS: Physical sciences
• Interfacial-melt stability as a thermodynamic prerequisite for solid-state synthesis
  Computational materials discovery commonly ranks candidate materials by their thermodynamic stability on the formation energy convex hull, yet many predicted-stable phases resist synthesis. We propose that solid-state synthesizability through interfacial-melt-…
  datacite Zhang, Zihan, Chen, Mengyi, Li, Qianxiao, Zhong, Peichen 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products
  Silicon carbide is a leading candidate material for advanced nuclear energy systems, but irradiation-induced defects and transmutation products can severely degrade its thermal conductivity. In fusion environments, Mg is predicted to be a major solid transmuta…
  datacite Shen, Chen, Su, Yang, Polak, Maciej P., Ullah, Rafi 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery
  Machine-learned interatomic potentials now enable efficient atomistic evaluation for interactive materials discovery, yet closed-loop crystal search methods remain fragmented across bespoke pipelines for editing, relaxation, scoring, constraints, and bookkeepi…
  datacite Cao, Bin 2026 置信度 0.66 Artificial Intelligence (cs.AI)Materials Science (cond-mat.mtrl-sci)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Physical sciences
• The FAST Framework: Developing a Data-Efficient Machine Learning Potential to Decode Superionic Transition-Induced Thermophysical and Kinetic Anomalies in UO2 under Extreme Conditions
  Uranium dioxide ($UO_2$) serves as the predominant nuclear fuel globally. Despite its widespread application, evaluating its mechanical, thermophysical, and species transport behaviors under extreme accident scenarios remains a formidable challenge for convent…
  datacite Zhuang, Fengnian, Yan, Gaosheng, Chen, Hong, Zhang, Yi 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• NequIP-OAM-M-ft-periclase-brucite-water-njord-2026-v1: an r²SCAN machine-learning interatomic potential and active-learning dataset for the Mg–O–H system (periclase, brucite, water)
  A machine-learning interatomic potential (MLIP) and its training dataset for the Mg-O-H system: periclase (MgO), brucite (Mg(OH)2), liquid water, and their reactive interfaces (dissolution, surface hydroxylation, water dissociation, and proton transport). The …
  datacite Sveinsson, Henrik Amdersen 2026 置信度 0.66
• NequIP-OAM-M-ft-periclase-brucite-water-njord-2026-v1: an r²SCAN machine-learning interatomic potential and active-learning dataset for the Mg–O–H system (periclase, brucite, water)
  A machine-learning interatomic potential (MLIP) and its training dataset for the Mg-O-H system: periclase (MgO), brucite (Mg(OH)2), liquid water, and their reactive interfaces (dissolution, surface hydroxylation, water dissociation, and proton transport). The …
  datacite Sveinsson, Henrik Amdersen 2026 置信度 0.66
• AI-Driven Design of Small Molecules for Enhanced and Precise Innate Immune Modulation
  The innate immune system orchestrates the earliest responses to infection and plays a central role in inflammation and antiviral defense. Small molecules capable of modulating innate immune pathways hold considerable therapeutic potential, yet their discovery …
  datacite Tang, Yifeng 2026 置信度 0.66 Innate immunitySmall-molecule discoveryMachine learningHigh-throughput screeningActive learning
• AI-Driven Design of Small Molecules for Enhanced and Precise Innate Immune Modulation
  The innate immune system orchestrates the earliest responses to infection and plays a central role in inflammation and antiviral defense. Small molecules capable of modulating innate immune pathways hold considerable therapeutic potential, yet their discovery …
  datacite Tang, Yifeng 2026 置信度 0.66 Innate immunitySmall-molecule discoveryMachine learningHigh-throughput screeningActive learning
• Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning
  Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces. However, they often require domain-specific calibration due to physicochemical…
  datacite Cho, Youngwoo, Yi, Seunghoon, Yang, Wooil, Kang, Sungmo 等 2026 置信度 0.66 Machine Learning (cs.LG)Materials Science (cond-mat.mtrl-sci)FOS: Computer and information sciencesFOS: Physical sciences
• UMA-NEB Structures and Energies for Ion Migration in Solid-State Ionic Conductors
  This dataset contains structures and energies from climbing-image nudged elastic band (CI-NEB) calculations performed using the Universal Models for Atoms (UMA) machine learning interatomic potential (MLIP). It covers ion migration pathways for Li⁺, Na⁺, K⁺, a…
  datacite Saravanan, Ramanuja Srinivasan, Mo, Yifei 2026 置信度 0.66 Theory and design of materialsComputational chemistrySolid state chemistryElectrochemistry
• UMA-NEB Structures and Energies for Ion Migration in Solid-State Ionic Conductors
  This dataset contains structures and energies from climbing-image nudged elastic band (CI-NEB) calculations performed using the Universal Models for Atoms (UMA) machine learning interatomic potential (MLIP). It covers ion migration pathways for Li⁺, Na⁺, K⁺, a…
  datacite Saravanan, Ramanuja Srinivasan, Mo, Yifei 2026 置信度 0.66 Theory and design of materialsComputational chemistrySolid state chemistryElectrochemistry
• Data for: Machine learning interatomic potentials for ordered mesoporous yttrium silicates
  Data for the master's thesis "Machine learning interatomic potentials for ordered mesoporous yttrium silicates" by Daniel Kevin Frank. This dataset contains the files mentioned in the thesis, the three domain-specific Moment Tensor Potentials (MTPs) with their…
  datacite Frank, Daniel Kevin 2026 置信度 0.66 ChemistryEngineeringPhysicsDensity Functional TheoryInteratomic Potentials
• Dynamical properties of ab initio water from machine-learning potentials
  We assess the dynamical properties of liquid water predicted by several density functionals using machine-learning interatomic potentials. MACE models were trained for SCAN, RPBE-D3/zd, revPBE-D3/zd, revPBE0-D3/BJ, PBE0-D3/zd, and PBE0-D3/BJ using previously r…
  datacite de Hijes, P. Montero, Neubeck, L., Kresse, G., Dellago, C. 2026 置信度 0.66 Soft Condensed Matter (cond-mat.soft)FOS: Physical sciencesFOS: Physical sciences
• Halide_Perovskite_Ion_Migration_MLFF_negative_iodide_vacancy
  VASP machine-learning-force-field (ML_AB) training set for the CsPbI3 halide perovskite, covering migration of a negatively charged iodide vacancy. This is one of seven sister datasets from the same publication, each providing a VASP ML_AB training set for a s…
  datacite Viren Tyagi, Pols, Mike, Brocks, Geert, Shuxia Tao 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Halide_Perovskite_Ion_Migration_MLFF_negative_iodide_interstitial
  VASP machine-learning-force-field (ML_AB) training set for the CsPbI3 halide perovskite, covering migration of a negatively charged iodide interstitial. This is one of seven sister datasets from the same publication, each providing a VASP ML_AB training set fo…
  datacite Viren Tyagi, Pols, Mike, Brocks, Geert, Shuxia Tao 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Halide_Perovskite_Ion_Migration_MLFF_positive_iodide_vacancy
  VASP machine-learning-force-field (ML_AB) training set for the CsPbI3 halide perovskite, covering migration of a positively charged iodide vacancy. This is one of seven sister datasets from the same publication, each providing a VASP ML_AB training set for a s…
  datacite Viren Tyagi, Pols, Mike, Brocks, Geert, Shuxia Tao 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Halide_Perovskite_Ion_Migration_MLFF_positive_iodide_interstitial
  VASP machine-learning-force-field (ML_AB) training set for the CsPbI3 halide perovskite, covering migration of a positively charged iodide interstitial. This is one of seven sister datasets from the same publication, each providing a VASP ML_AB training set fo…
  datacite Viren Tyagi, Pols, Mike, Brocks, Geert, Shuxia Tao 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Halide_Perovskite_Ion_Migration_MLFF_neutral_iodide_vacancy
  VASP machine-learning-force-field (ML_AB) training set for the CsPbI3 halide perovskite, covering migration of a neutral iodide vacancy. This is one of seven sister datasets from the same publication, each providing a VASP ML_AB training set for a slightly dif…
  datacite Viren Tyagi, Pols, Mike, Brocks, Geert, Shuxia Tao 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Halide_Perovskite_Ion_Migration_MLFF_neutral_iodide_interstitial
  VASP machine-learning-force-field (ML_AB) training set for the CsPbI3 halide perovskite, covering migration of a neutral iodide interstitial. This is one of seven sister datasets from the same publication, each providing a VASP ML_AB training set for a slightl…
  datacite Viren Tyagi, Pols, Mike, Brocks, Geert, Shuxia Tao 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Halide_Perovskite_Ion_Migration_MLFF_phase_transition
  VASP machine-learning-force-field (ML_AB) training set for the CsPbI3 halide perovskite, covering the cubic-tetragonal phase transition. This is one of seven sister datasets from the same publication, each providing a VASP ML_AB training set for a slightly dif…
  datacite Viren Tyagi, Pols, Mike, Brocks, Geert, Shuxia Tao 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Graphene_Field_Effect_Transistor_Urea_Sensor
  DFT-optimized structures and total energies of graphene interacting with urea and water molecules, supporting a combined experimental and first-principles study of a graphene field-effect-transistor (FET) sensor for the detection of urea in water. The configur…
  datacite Ondřej Špaček, Supalová, Linda, Jindřich Mach, Nezval, David 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• AIMNet2
  AIMNet2(2025) is the extended training dataset for the AIMNet2 (second generation atoms-in-molecules network) neural network interatomic potential, curated to improve the model's description of noncovalent interactions (NCIs) including hydrogen bonding, pi-pi …
  datacite Nayal, Kamal Singh, Ilkwon Cho, Runtian Nick Gao, Peikun Zheng 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Massive_Atomic_Diversity_MAD-1.5_PBE
  A subset of the MAD-1.5 (Massive Atomic Diversity version 1.5) structures recomputed with the PBE GGA functional, covering the MAD-1 subsets (MC3D, MC3D-rattled, MC3D-random, MC3D-surface, MC3D-cluster, MC2D, SHIFTML-molcrys, SHIFTML-molfrags) plus monomers an…
  datacite Malosso, Cesare, Bigi, Filippo, Pegolo, Paolo, Abbott, Joseph W. 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Open_Catalyst_2025_OC25_Val
  The validation split of the Open Catalyst 2025 (OC25) dataset for solid-liquid interfaces. OC25 consists of single-point DFT calculations of catalyst/solvent/ion/adsorbate structures, covering 88 elements, 8 solvents (water, methanol, CCl4, DMSO, benzene, hexa…
  datacite Sushree Jagriti Sahoo, Maroschin, Mikael, Levine, Daniel S., Ulissi, Zachary 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Open_Direct_Air_Capture_ODAC2025_Train_Filtered
  This is the filtered training split of ODAC25. ODAC25 is a large-scale DFT dataset intended to advance the computational screening of Metal-Organic Framework (MOF) sorbents for direct air capture (DAC) of atmospheric CO2 from humid air. Spanning ~15,000 MOFs, …
  datacite Anuroop Sriram, Brabson, Logan M., Xiaohan Yu, Sihoon Choi 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• CrN-mMTP-Paramagnetic-ActiveLearning
  Training set for a magnetic Moment Tensor Potential (mMTP) for paramagnetic B1-CrN, created via active learning. Contains 2423 configurations of 64-atom CrN supercells with collinear atomic magnetic moments and magnetic forces (negative derivatives of energy w…
  datacite Kotykhov, Alexey S., Hodapp, Max, Tantardini, Christian, Kravtsov, Konstantin 等 2024 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• OPoly26-val
  Validation set of the Open Polymers 2026 (OPoly26) dataset. OPoly26 contains over 6.57 million density functional theory (DFT) calculations on cluster fragments of up to 360 atoms derived from polymeric systems. The dataset encompasses variations in monomer co…
  datacite Levine, Daniel S., Liesen, Nicholas, Chua, Lauren, Diffenderfer, James 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Silica_Allegro_15000K
  DFT reference dataset for an Allegro machine-learned interatomic potential for silica (SiO2) valid up to 15000 K, spanning the high-temperature melt, melt-quench amorphization, and mechanical-deformation regimes. The configurations were selected by HYAL active…
  datacite Sveinsson, Henrik Andersen 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Halide_Double_Perovskite_Octahedral_Tilting
  DFT reference structures used to train neuroevolution potentials (NEP) for a study disentangling lone-pair chemistry and geometric effects in the octahedral tilting of halide double perovskites. The dataset contains the training configurations (with energies, …
  datacite Baskurt, Mehmet, Fransson, Erik, Lindvik, Madeleine, Erhart, Paul 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• CeO2_Surface_Oxygen_Vacancy_MLFF
  Density functional theory reference data for constructing a machine-learning force field (MLFF) of cerium oxide (CeO2) surfaces containing an oxygen vacancy, generated with VASP on-the-fly machine-learning and stored in ML_AB training files. The dataset follow…
  datacite Oshiro, Kai, Gao, Min, Jun-ya Hasegawa 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Open_Direct_Air_Capture_ODAC2025_Train_Full
  This is the full (unfiltered) training split of ODAC25. ODAC25 is a large-scale DFT dataset intended to advance the computational screening of Metal-Organic Framework (MOF) sorbents for direct air capture (DAC) of atmospheric CO2 from humid air. Spanning ~15,0…
  datacite Anuroop Sriram, Brabson, Logan M., Xiaohan Yu, Sihoon Choi 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Open_Direct_Air_Capture_ODAC2025_Val_Filtered
  This is the filtered validation split of ODAC25. Open Direct Air Capture 2025 (ODAC25) is the largest high-quality DFT dataset for Direct Air Capture, containing over 15,000 Metal-Organic Frameworks (MOFs), including experimental, defective, synthetic, and ami…
  datacite Anuroop Sriram, Brabson, Logan M., Xiaohan Yu, Sihoon Choi 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• MP-ALOE
  MP-ALOE is a dataset of nearly 1 million DFT calculations computed with the r2SCAN meta-generalized gradient approximation, covering 89 elements. The dataset was constructed using active learning via Query by Committee (QBC) and downsampling via the DIRECT met…
  datacite Kuner, Matthew C., Kaplan, Aaron D., Persson, Kristin A., Asta, Mark 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• OPoly26-train
  Training set of the Open Polymers 2026 (OPoly26) dataset. OPoly26 contains over 6.57 million density functional theory (DFT) calculations on cluster fragments of up to 360 atoms derived from polymeric systems, comprising over 1.2 billion total atoms. The datas…
  datacite Levine, Daniel S., Liesen, Nicholas, Chua, Lauren, Diffenderfer, James 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• MatPES-PBE-2025.1
  MatPES (Materials Potential Energy Surface) is a foundational PES dataset developed collaboratively by the Materials Virtual Lab and Materials Project. The v2025.1 PBE release contains 434,712 structures sampled via the DIRECT method from 300 K NpT molecular d…
  datacite Kaplan, Aaron D., Runze Liu, Qi, Ji, Tsz Wai Ko 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Perovskite_Nanorod_Li_Na_Transport
  VASP single-point (SCF) DFT calculations underpinning a mechanistic study of A-site doping in lithium-lanthanum-titanate (LMTO/LLTO) perovskite nanorods and their interfaces with a p(MTFSI) polymer electrolyte, aimed at understanding interfacial Li-ion and Na-…
  datacite Shepard, Lauren B., Ji-young Ock, Bhattacharya, Amit, Wang, Tao 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Hydrogen_Fe_Steel
  Spin-polarized density functional theory structural relaxations probing hydrogen-induced lattice strain in a chemically complex Fe-based (bcc) hybrid steel. A 55-atom supercell of composition VMoCrMnFe47NiAlSiC is relaxed with 0, 1, 2, and 5 hydrogen atoms ins…
  datacite Aksoy, Ammar, Örnek, Cem, Payam, Beste, Şeşen, Bilgehan M. 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• ANI-1xBB
  ANI-1xBB is a dataset of approximately 13.1 million nonequilibrium conformers of small organic molecules (H, C, N, O only; up to 7 heavy atoms; up to 23 atoms total), designed to support the training of reactive machine learning interatomic potentials. Single-…
  datacite Shuhao Zhang, Zubatyuk, Roman, Yinuo Yang, Roitberg, Adrian 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Massive_Atomic_Diversity_MAD-1.5_r2SCAN_Train
  Training split of the MAD-1.5 (Massive Atomic Diversity version 1.5) dataset, a highly curated collection designed for training broadly applicable atomistic machine-learning models across the full periodic table. MAD-1.5 extends the original MAD dataset with t…
  datacite Malosso, Cesare, Bigi, Filippo, Pegolo, Paolo, Abbott, Joseph W. 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Open_Catalyst_2025_OC25_Train
  The training split of the Open Catalyst 2025 (OC25) dataset for solid-liquid interfaces. OC25 consists of single-point DFT calculations of catalyst/solvent/ion/adsorbate structures, covering 88 elements, 8 solvents (water, methanol, CCl4, DMSO, benzene, hexane…
  datacite Sushree Jagriti Sahoo, Maroschin, Mikael, Levine, Daniel S., Ulissi, Zachary 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• MatPES-R2SCAN-2025.2
  MatPES (Materials Potential Energy Surface) is a foundational PES dataset developed collaboratively by the Materials Virtual Lab and the Materials Project. The v2025.2 r2SCAN release contains 386,544 structures sampled via the DIRECT method from 300 K NpT mole…
  datacite Kaplan, Aaron D., Runze Liu, Qi, Ji, Tsz Wai Ko 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• MatPES-PBE-2025.2
  MatPES (Materials Potential Energy Surface) is a foundational PES dataset developed collaboratively by the Materials Virtual Lab and the Materials Project. The v2025.2 PBE release contains 433,189 structures sampled via the DIRECT method from 300 K NpT molecul…
  datacite Kaplan, Aaron D., Runze Liu, Qi, Ji, Tsz Wai Ko 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• FeAl-mMTP-Verification
  Verification set for magnetic Moment Tensor Potentials (mMTPs) for the bcc Fe-Al system. Contains 336 configurations of 16-atom Fe-Al supercells with collinear atomic magnetic moments, used to validate mMTPs trained on the companion training set (FeAl-mMTP-Tra…
  datacite Kotykhov, Alexey S., Gubaev, Konstantin, Hodapp, Max, Tantardini, Christian 等 2023 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• FeAl-mMTP-MagneticForces
  Training set for magnetic Moment Tensor Potentials (mMTPs) that fit to magnetic forces for the bcc Fe-Al system. Contains 2632 configurations of 16-atom Fe-Al supercells with collinear atomic magnetic moments and magnetic forces (negative derivatives of energy…
  datacite Kotykhov, Alexey S., Gubaev, Konstantin, Sotskov, Vadim, Tantardini, Christian 等 2024 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• OMol25_test
  The test set of OMol25. OMol25 (Open Molecules 2025) is a large dataset of structures with up to 350 atoms, calculated at a high level of DFT theory (ωB97M-V/def2-TZVPD). This dataset is intended to provide a broad sampling of chemical complexity and structura…
  datacite Levine, Daniel S., Shuaibi, Muhammed, Spotte-Smith, Evan Walter Clark, Taylor, Michael G. 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Vector-QM24_DMC
  Lowest-energy structures with up to 4 heavy atoms from Vector-QM24 (VQM24) with properties calculated using diffusion quantum Monte Carlo (DMC) after DFT optimization. Vector-QM24 is a quantum chemistry dataset of ~836 thousand small organic and inorganic mole…
  datacite Danish Khan, Benali, Anouar, Kim, Scott Y. H., von Rudorff, Guido Falk 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Vector-QM24_DFT_uniques
  Structures from Vector-QM24 (VQM24) that represent constitutional isomers, or the most stable conformers, with properties calculated using DFT. Vector-QM24 is a quantum chemistry dataset of ~836 thousand small organic and inorganic molecules. Dataset covers al…
  datacite Danish Khan, Benali, Anouar, Kim, Scott Y. H., von Rudorff, Guido Falk 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Vector-QM24_DFT_saddles
  Structures from Vector-QM24 (VQM24) that converged to saddle points during relaxation, with properties calculated using DFT. Vector-QM24 is a quantum chemistry dataset of ~836 thousand small organic and inorganic molecules. Dataset covers all possible neutral …
  datacite Danish Khan, Benali, Anouar, Kim, Scott Y. H., von Rudorff, Guido Falk 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Vector-QM24_DFT_all
  All structures calculated for Vector-QM24 (VQM24) with properties calculated using DFT. Vector-QM24 is a quantum chemistry dataset of ~836 thousand small organic and inorganic molecules. Dataset covers all possible neutral closed-shell small organic and inorga…
  datacite Danish Khan, Benali, Anouar, Kim, Scott Y. H., von Rudorff, Guido Falk 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Carbon-24_Unique_with_Enantiomorphs
  This dataset is a companion dataset to Carbon-24 Unique, containing enantiomorph pairs discovered within the Carbon-24 dataset. Carbon-24_Unique_with_Enantiomorphs has been cultivated from Carbon-24 (Pickard 2020, doi: 10.24435/materialscloud:2020.0026/v1). Co…
  datacite Martirossyan, Maya M., Egg, Thomas, Hoellmer, Philipp, Karypis, George 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Carbon_NXL
  This dataset is a companion dataset to Carbon-24 Unique. Carbon NXL is intended for use in training of minimal “overfitting” testing cases. Contains 353 carbon structures of duplicates which have different numbers of atoms per unit cell (N=6—16), different cel…
  datacite Martirossyan, Maya M., Egg, Thomas, Hoellmer, Philipp, Karypis, George 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Carbon_X
  This dataset is a companion dataset to Carbon-24 Unique. Carbon X contains 480 carbon structures of duplicates which have the same cell shape and same number of atoms per unit cell (N=6), with different translations (X) of the fractional coordinates. Carbon_X …
  datacite Martirossyan, Maya M., Egg, Thomas, Hoellmer, Philipp, Karypis, George 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Ferrimagnetism_induced_by_thermal_vibrations_in_oxygen-deficient_manganite_heterostructures
  Data from the paper 'Ferrimagnetism induced by thermal vibrations in oxygen-deficient manganite heterostructures'. Includes Quantum ESPRESSO calculations of SrCaMnO3 and SrMnO3, stoichiometric and defective cells.
  datacite Moloud Kaviani, Ricca, Chiara, Aschauer, Ulrich 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Graphene-hBN_and_Graphene-Graphene_QMC
  The QMC-calculated split of the Graphene-hBN_and_Graphene-Graphene dataset. This dataset family (see other Graphene-hBN_and_Graphene_Graphene datasets) contains data for Graphene-Graphene and Graphene-hexagonal boron nitride (hBN) ab initio calculations for st…
  datacite Kittithat Krongchon, Wagner, Lucas K., Tawfiqur Rakib, Palmer, Daniel 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Graphene-hBN_and_Graphene-Graphene_DFT_D2
  The DFT with D2 vdW corrections split of the Graphene-hBN_and_Graphene-Graphene dataset. This dataset family (see other Graphene-hBN_and_Graphene_Graphene datasets) contains data for Graphene-Graphene and Graphene-hexagonal boron nitride (hBN) ab initio calcul…
  datacite Kittithat Krongchon, Wagner, Lucas K., Tawfiqur Rakib, Palmer, Daniel 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Graphene-hBN_and_Graphene-Graphene_DFT_D3
  The DFT with D3 vdW corrections split of the Graphene-hBN_and_Graphene-Graphene dataset. This dataset family (see other Graphene-hBN_and_Graphene_Graphene datasets) contains data for Graphene-Graphene and Graphene-hexagonal boron nitride (hBN) ab initio calcul…
  datacite Kittithat Krongchon, Wagner, Lucas K., Tawfiqur Rakib, Palmer, Daniel 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Open_Molecular_Crystals_2025_OMC25_validation
  The validation split of OMC25. Open Molecular Crystals 2025 (OMC25) is a molecular crystal dataset produced by Meta. The OE62 dataset was used as a source for sampling molecules; crystals were generated with Genarris 3.0; from these, relaxation trajectories we…
  datacite Gharakhanyan, Vahe, Barroso-Luque, Luis, Yang, Yi, Shuaibi, Muhammed 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Open_Molecular_Crystals_2025_OMC25_train
  The training split of OMC25. Open Molecular Crystals 2025 (OMC25) is a molecular crystal dataset produced by Meta. The OE62 dataset was used as a source for sampling molecules; crystals were generated with Genarris 3.0; from these, relaxation trajectories were…
  datacite Gharakhanyan, Vahe, Barroso-Luque, Luis, Yang, Yi, Shuaibi, Muhammed 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• rQM9
  133885 molecular structures from the QM9 with revised bond and charges in the SDF format. Bond information can be gathered from the metadata column of the parquet files, a map where the key bonds contains the bond indices as they appear in the final rows of an…
  datacite Zeng, Cheng, Jirui Jin, Karypis, George, Transtrum, Mark 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Complex Ga2O3 Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials
  Abstract Ga 2 O 3 is a wide-bandgap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the properties of complex coexisting Ga 2 O 3 polymorphs and low-symmetry disordered structures is missi…
  crossref Junlei Zhao, Jesper Byggmästar, Huan He, Kai Nordlund 等 2022-12-10T00:22:21Z 置信度 0.70
• Effective interatomic potential for modelling shock waves in carbon nanotube bundles
  Carbon nanotubes (CNTs) are grown as forests on a variety of substrates and can be horizontally aligned and compacted to produce super-strong ropes and yarns. CNT bundles can be used for shock and vibration protection. It is of interest to analyze the energy a…
  crossref Sergey V. Dmitriev, Denis I. Borisov 2022-09-14T17:00:21Z 置信度 0.70
• Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials
  Abstract Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs’ performance over a broad spectrum of material prope…
  crossref Yunsheng Liu, Yifei Mo 2024-07-20T05:02:25Z 置信度 0.70
• Building a new generation of multiscale materials models with machine-learned interatomic potentials.
  This paper describes objective technical results and analysis.
  crossref Megan McCarthy, Aidan Thompson, Mitchell Wood 2023-11-04T03:09:48Z 置信度 0.70
• Author response for "Applicability of universal machine learning interatomic potentials to the simulation of steels"
  crossref Restrepo, Sebastian Echeverri, Mohandas, Naveen K., Sluiter, Marcel H. F., Paxton, Anthony T. 2025-10-22T01:13:00Z 置信度 0.70
• Review for "Modelling Silica using MACE-MP Machine Learnt Interatomic Potentials"
  crossref 2025-07-21T21:05:55Z 置信度 0.70
• Review for "Modelling Silica using MACE-MP Machine Learnt Interatomic Potentials"
  crossref 2025-07-21T21:05:55Z 置信度 0.70
• Machine-learned interatomic potential for Fe–Co–X (X = Re, W) alloys: Ductility–brittleness behavior at finite temperatures
  crossref Haixia Cheng, Yanzhou Wang, Ke-Ke Song, Song Minhui 等 2025-11-25T05:46:51Z 置信度 0.70
• STING, guided active learning for Machine Learned Interatomic Potentials examined on Lithium Thiophosphate solid state electrolytes
  All solid state batteries, based on lithium thiophosphate (LiPS) solid state electrolytes, offer a promising route to safer, higher energy density storage. Computational characterisation of these materials, and their interfaces with electrode materials, demand…
  crossref Roman Shantsila, Chantal Michelle Ilse Baer Baer, Albert Bartók-Pártay, Bora Karasulu 2026-04-02T12:22:18Z 置信度 0.70
• Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys
  Gaining atomistic understanding of mechanical behavior of heat-resistant structural materials such as Fe–Cr–Ni-based alloys requires an approach with an accuracy close to density functional theory (DFT) that considers the intrinsic properties of the bulk latti…
  crossref Shiqiang Hao, Prashant Singh, A. V. Smirnov, Duane D. Johnson 等 2025-08-22T10:31:06Z 置信度 0.70
• Thermoelectric properties of lead-free anti-perovskites X3BN (B = Bi, Sb, X = Mg, Ca, Sr): A theoretical study based on first-principles calculations and machine learning interatomic potential
  The search for non-toxic and low-cost perovskites with high thermoelectric performance is still a challenge despite low thermal conductivity. The thermoelectric properties of nitride anti-perovskites X3BN (B = Bi, Sb, X = Mg, Ca, Sr) with a cubic structure wer…
  crossref Tao Hu, Yanxiao Hu, Wenqiu Shang, Li Li 等 2024-04-10T16:15:55Z 置信度 0.70