AI 驱动科学发现 · 全部实体（第 28 / 100 页）

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• Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation
  The development of machine learning interatomic potentials (MLIPs) has revolutionized computational chemistry by enhancing the accuracy of empirical force fields while retaining a large computational speed-up compared to first-principles calculations. Despite …
  crossref Nils Gönnheimer, Karsten Reuter, Johannes T. Margraf 2025-01-08T05:50:06Z 置信度 0.70
• Decision letter for "Machine Learning Interatomic Potentials in Engineering Perspective for Developing Cathode Materials"
  crossref 2024-07-30T17:13:50Z 置信度 0.70
• Decision letter for "Machine Learning Interatomic Potentials in Engineering Perspective for Developing Cathode Materials"
  crossref 2024-07-30T17:13:50Z 置信度 0.70
• Author response for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
  crossref Valdas Vitartas, Hanwen Zhang, Veronika Jurásková, Tristan Johnston-Wood 等 2025-10-30T21:08:47Z 置信度 0.70
• pdb2reaction: End-to-End Reaction-Path Elucidation from PDB Structures Using Machine-Learning Interatomic Potentials
  Elucidating enzymatic reaction mechanisms requires a sequence of computational tasks comprising active-site extraction, minimum-energy path (MEP) search, transition-state (TS) refinement, intrinsic reaction coordinate (IRC) validation, and quasi-rigid-rotor ha…
  crossref Takuto Ohmura, Hajime Sato, Tohru Terada 2026-05-19T10:15:58Z 置信度 0.70
• Machine Learning Interatomic Potentials for Amorphous Zeolitic Imidazolate Frameworks
  The detailed understanding of the microscopic structure of amorphous phases of metal-organic frameworks (MOFs) remains a widely open question: characterization of these systems is very difficult, both from the experimental and computational point of view. In m…
  crossref Nicolas Castel, Dune André, Connor Edwards, Jack D. Evans 等 2024-01-03T04:43:16Z 置信度 0.70
• Decision letter for "Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals"
  crossref 2025-05-25T06:08:09Z 置信度 0.70
• Combining Electronic Structure Methods and Machine Learning Interatomic Potentials in Adsorption Studies
  Atomistic modelling methods can provide significant insights into the adsorption and drug delivery mechanisms of pharmaceuticals and related organic molecules in cation-exchanged zeolites, offering an atomic-level understanding of host–guest interactions and a…
  crossref Sujon Kumar Mitro, Hendrik Kraß, Jakob Brauer, Michael Fischer 2026-06-01T13:14:52Z 置信度 0.70
• Author response for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref Riccardo Dettori, Antonio Cappai, Claudio Melis, Luciano Colombo 2025-10-08T21:09:03Z 置信度 0.70
• Supplementary data 1
  crossref 2026-02-13T15:05:16Z 置信度 0.70
• Intercalation Chemistry of the Disordered Rocksalt Li3V2O5 Anode from Cluster Expansions and Machine Learning Interatomic Potentials
  crossref 2023-02-10T11:10:23Z 置信度 0.70
• Integrating Charge Equilibration with Equivariant Machine-Learning Interatomic Potentials
  Machine learning interatomic potentials (MLIPs) based on local atomic environments have achieved remarkable accuracy and efficiency, yet they often struggle in systems where long-range electrostatics, charge transfer, and non-local electronic effects play a de…
  crossref Martin Vondrák, William J Baldwin, Gábor Csányi, Karsten Reuter 等 2026-02-25T04:55:13Z 置信度 0.70
• Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by Machine Learning Interatomic Potentials
  Magnesium hydride (MgH2) is a promising material for solid-state hydrogen storage due to its high gravimetric hydrogen capacity as well as the abundance and low cost of magnesium. The material’s limiting factor is the high dehydrogenation temperature (over 300…
  crossref 2024-12-19T01:20:22Z 置信度 0.70
• Machine learning based interatomic potential for amorphous carbon
  We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory (DFT) potential-energy surface, such interatomic potentials en…
  crossref Volker L. Deringer, Gábor Csányi 2017-03-03T22:08:27Z 置信度 0.70
• Radiation Damage and Phase Stability of AlxCrCuFeNiy Alloys Using a Machine-Learned Interatomic Potential
  crossref Aslak Fellman, Jesper Byggmästar, Fredric Granberg, Flyura Djurabekova 等 2025-03-24T20:18:59Z 置信度 0.70
• Diffusion coefficients calculations of 110mAg in ZrC at very high temperature using machine-learning interatomic potential
  • Machine-learning potential for the Zr-C-Ag ternary system was developed. • Ag diffusion in ZrC 0.94 and ZrC 0.97 was computed using molecular dynamics simulations. • Ag diffusion accelerates with higher temperature and increased C vacancy concentration. The …
  crossref Jae Joon Kim, Eung-Seon Kim, Hyun Woo Seong, Ho Jin Ryu 2024-11-23T07:29:24Z 置信度 0.70
• Efficient molecular dynamics simulations of deep eutectic solvents with first-principles accuracy using machine learning interatomic potentials
  In recent years, deep eutectic solvents (DESs) emerged as highly tunable and eco-friendly alternatives to common organic solvents and liquid electrolytes. In the present work, the ability of machine learning (ML) interatomic potentials for molecular dynamics (…
  crossref Omid Shayestehpour, Stefan Zahn 2023-08-24T04:51:56Z 置信度 0.70
• Decision letter for "Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals"
  crossref 2025-05-25T06:08:09Z 置信度 0.70
• Author response for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref Riccardo Dettori, Antonio Cappai, Claudio Melis, Luciano Colombo 2025-10-08T21:09:03Z 置信度 0.70
• Balancing Diversity and Efficiency in Training Datasets for Robust Machine Learning Interatomic Potentials
  Machine learning interatomic potentials enable atomistic simulations at near first-principles accuracy. As model architectures mature, their reliability is increasingly constrained by training data quality. Here, we introduce Automated Training with Latent-spa…
  crossref Pol Sanz Berman, Lulu Li, Zan Lian, Núria López 2026-06-05T07:09:02Z 置信度 0.70
• The Free Energy Landscape of Li- and Na-Ion Transport in Nanoconfinement with Machine Learning Interatomic Potentials
  crossref 2026-04-07T20:30:19Z 置信度 0.70
• Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential
  Characterization of the primary damage is the starting point in describing and predicting the irradiation-induced damage in materials. So far, primary damage has been described by traditional interatomic potentials in molecular dynamics simulations. Here, we e…
  crossref A. Hamedani, J. Byggmästar, F. Djurabekova, G. Alahyarizadeh 等 2021-11-05T14:18:50Z 置信度 0.70
• Hierarchical Transfer Learning: An Agile and Equitable Strategy for Machine-Learning Interatomic Models
  Machine-learned interatomic models have growing in popularity due to their ability to afford near quantum-accurate predictions for complex phenomena, with orders-of-magnitude greater computational efficiency. However, these models struggle when applied to syst…
  crossref Rebecca Lindsey, Awwal Oladipupo, Sorin Bastea, Bradley Steele 等 2025-02-07T00:57:40Z 置信度 0.70
• Supplementary data 1
  crossref 2026-06-15T13:04:31Z 置信度 0.70
• Complex strengthening mechanisms in nanocrystalline Ni-Mo alloys revealed by a machine-learning interatomic potential
  crossref Xiang-Guo Li, Shuozhi Xu, Qian Zhang, Shenghua Liu 等 2023-04-05T16:44:56Z 置信度 0.70
• Temperature dependent thermo-mechanical properties of 3C and 6H silicon carbide from atomistic simulations on large-scale systems with DFT-derived machine learning interatomic potential
  This work presents a comprehensive, comparative study of two primary SiC polytypes, cu- bic (3C) and hexagonal (6H), using large-scale molecular dynamics simulations powered by a high-fidelity Machine Learning Interatomic Potential (MLIP) trained on a custom d…
  crossref Oleksandr Parfionov, Oleksandr Vasiliev 2026-05-08T06:16:56Z 置信度 0.70
• Machine learning interatomic potential for predicting the thermal properties of uranium nitride
  We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic potential (MLIP) using the moment tensor potential framework. The MLIP was train…
  crossref Beihan Chen, Zilong Hua, Jennifer K. Watkins, Linu Malakkal 等 2025-11-24T19:13:36Z 置信度 0.70
• De Novo Inverse Design Superhard CN Compounds via Global Machine Learning Interatomic Potentials and Multiobjective Optimization Algorithm
  A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C–N superhard materia…
  crossref 2025-04-24T15:00:15Z 置信度 0.70
• Thermosalient Phase Transitions from Machine Learning Interatomic Potential
  We developed an accurate machine learning interatomic potential for the thermosalient molecular crystal N -2-propylidene-4-hydroxybenzohydrazide. This crystal exhibits one of the largest mechanical responses during its thermosalient phase transition. Leveragin…
  crossref Bruno Mladineo, Ivor Lončarić 2024-10-07T19:17:27Z 置信度 0.70
• Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule
  We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications…
  crossref Elena Gelžinytė, Mario Öeren, Matthew D. Segall, Gábor Csányi 2023-06-29T03:06:17Z 置信度 0.70
• 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…
  crossref 2025-07-21T04:50:57Z 置信度 0.70
• Hierarchical Transfer Learning: An Agile and Equitable Strategy for Machine-Learning Interatomic Models
  Machine-learned interatomic models have growing in popularity due to their ability to afford near quantum-accurate predictions for complex phenomena, with orders-of-magnitude greater computational efficiency. However, these models struggle when applied to syst…
  crossref Rebecca Lindsey, Awwal Oladipupo, Sorin Bastea, Bradley Steele 等 2024-05-22T01:41:46Z 置信度 0.70
• De Novo Inverse Design Superhard CN Compounds via Global Machine Learning Interatomic Potentials and Multiobjective Optimization Algorithm
  A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C–N superhard materia…
  crossref 2025-04-24T15:00:15Z 置信度 0.70
• Thermal Boundary Conductance of Metal–Diamond Interfaces Predicted by Machine Learning Interatomic Potentials
  crossref Khalid Zobaid Adnan, Mahesh Raj Neupane, Tianli Feng 2024-05-21T07:19:26Z 置信度 0.70
• Cartesian atomic cluster expansion for machine learning interatomic potentials
  Abstract Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modeling in material science and chemistry. Many potentials use atomic cluster expansion or equivariant message-passing frameworks. Such frameworks typically u…
  crossref Bingqing Cheng 2024-07-18T10:01:58Z 置信度 0.70
• 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…
  crossref 2025-07-21T04:50:57Z 置信度 0.70
• GRACE-OFF: A machine-learned interatomic potential for organic liquids using the GRACE architecture
  Machine-learned interatomic potentials (MLIPs) have become an increasingly important tool for molecular dynamics (MD) simulations, enabling near quantum-mechanical accuracy at significantly reduced computational cost. In this work, we assess the Graph Atomic C…
  crossref Anna Katharina Picha, Johannes Karwounopoulos, Linus C. Erhard, Stefan Boresch 等 2026-04-02T08:58:05Z 置信度 0.70
• GRACE-OFF: A machine-learned interatomic potential for organic liquids using the GRACE architecture
  crossref Anna Katharina Picha, Johannes Karwounopoulos, Linus C. Erhard, Stefan Boresch 等 2026-08-18T07:31:22Z 置信度 0.70
• Exploring the structural stability, thermal and mechanical properties of nanoporous carbon nitride nanosheets using a transferrable machine learning interatomic potential
  Abstract Nanoporous carbon nitride nanosheets (NPCNNs) currently stand as one of the most promising classes of two-dimensional materials, exhibiting exceptional properties, wide range of applications and continuous experimental realization of novel structures.…
  crossref Bohayra Mortazavi, Timon Rabczuk, Xiaoying Zhuang 2024-11-26T08:30:03Z 置信度 0.70
• Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents with First-Principles Accuracy Using Machine Learning Interatomic Potentials
  crossref 2023-11-16T18:40:10Z 置信度 0.70
• Review for "Dynamic Origins of Cation-Modulated Stability in Tin-Based Perovskites Revealed through Combined Fine-tuned Machine Learning Interatomic Potentials and Experiments"
  crossref 2025-12-15T21:05:48Z 置信度 0.70
• Author response for "Accelerating Explicit Solvent Models of Heterogeneous Catalysts with Machine Learning Interatomic Potentials"
  crossref Benjamin W J Chen, Jia Zhang, Xinglong Zhang 2023-07-13T17:00:54Z 置信度 0.70
• Machine Learning Interatomic Potentials for Modeling Framework Flexibility and Water Uptake in NbOFFIVE-1-Ni MetalOrganic Framework
  crossref 2026-02-10T02:10:16Z 置信度 0.70
• Review for "Dynamic Origins of Cation-Modulated Stability in Tin-Based Perovskites Revealed through Combined Fine-tuned Machine Learning Interatomic Potentials and Experiments"
  crossref 2025-12-15T21:05:48Z 置信度 0.70
• Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling
  crossref Bohayra Mortazavi 2023-10-03T17:01:53Z 置信度 0.70
• Synthesizable Two-Dimensional Haeckelites with Auxetic Behavior and Multifunctional Properties: A Computational Study Integrating Machine Learning Interatomic Potentials
  Our recent high-throughput investigation has uncovered bulk semiconducting haeckelite structures that hold considerable promise for optoelectronic applications (Advanced Functional Materials, 10.1002/adfm.202402390). In this study, we perform a comprehensive s…
  crossref 2025-09-29T16:51:36Z 置信度 0.70
• On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential
  We developed a machine learning interatomic potential (MLIP) for Ge-rich GeSbTe alloys of interest for applications in phase change memories embedded in microcontrollers.
  crossref Omar Abou El Kheir, Dario Baratella, Marco Bernasconi 2026-07-07T10:23:14Z 置信度 0.70
• Correction to Density Functional Theory Calculations and Machine Learning Interatomic Potentials for Molten Salts to Achieve Experimental Accuracy
  Despite the considerable success of density functional theory (DFT) in a broad class of materials, there are no exchange–correlation functionals or dispersion corrections that can systematically achieve high accuracy in molten salt simulations; for example, th…
  crossref 2024-11-06T07:30:23Z 置信度 0.70
• Correction to Density Functional Theory Calculations and Machine Learning Interatomic Potentials for Molten Salts to Achieve Experimental Accuracy
  Despite the considerable success of density functional theory (DFT) in a broad class of materials, there are no exchange–correlation functionals or dispersion corrections that can systematically achieve high accuracy in molten salt simulations; for example, th…
  crossref 2024-11-06T07:30:23Z 置信度 0.70
• Transferability of machine-learning interatomic potential to α-Fe nanocrystalline deformation
  crossref Kazuma Ito, Tatsuya Yokoi, Katsutoshi Hyodo, Hideki Mori 2025-03-05T19:04:52Z 置信度 0.70
• A machine‐learning interatomic potential for Si‐C‐N‐H with application to polymer‐to‐ceramic processing of polysilazanes
  Abstract We developed a machine‐learning interatomic potential (MLIP) based on Moment Tensor Potentials for atomistic simulations in the Si‐C‐N‐H system. The MLIP was trained on ordered and disordered configurations—including crystalline phases, polymers, amor…
  crossref Shariq Haseen, Peter Kroll 2025-11-23T16:54:42Z 置信度 0.70
• Machine-learning based interatomic potential for studying of crystal structures properties
  In the process of modeling multilayer semiconductor nanostructures, it is important to quickly obtain accurate values the characteristics of the structure under consideration. One of these characteristics is the value of the interaction energy of atoms within …
  crossref O. V. Uvarova, S. I. Uvarov 2023-08-19T08:08:15Z 置信度 0.70
• Comprehensive Evaluation of Oxidation Resistance for Tertiary Phosphines Using Universal Machine Learning Interatomic Potential-Aided Molecular Dynamics Simulation
  Abstract Tertiary phosphines play important roles in a wide range of chemical reactions, but they suffer from oxidation in air. Here, we propose a computational workflow for the comprehensive prediction of oxidation resistance in tertiary phosphines based on m…
  crossref Toshiya Sato, Yuri Asai, Uika Koshimizu, Yuji Hakozaki 等 2026-08-28T12:24:36Z 置信度 0.70
• Foundational Machine Learning Interatomic Potential to Study Li-Ion Battery Cathode Phase Transformation with Charge Transfer
  Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modeling of electrochemical systems. Our recent work, Crystal Hamiltonian Graph Neural Network (CHGNet), presents foundational graph-neural-network-b…
  crossref Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell 等 2024-12-19T21:28:24Z 置信度 0.70
• FPGA Accelerator for Machine Learning Interatomic Potential-Based Molecular Dynamics of Gold Nanoparticles
  Molecular dynamics (MD) simulations involve computations of forces between atoms and the total energy of the chemical systems. The scientific community is dependent on high-end servers for such computations that are generally sequential and highly power hungry…
  crossref Satya S. Bulusu, Srivathsan Vasudevan 2022-04-07T19:26:05Z 置信度 0.70
• CeNEP: A general-purpose machine learning interatomic potential for cementitious materials
  crossref Fengzijun Pan, Guangyao Li, Jiaqiu Xu, Zuhao Wang 等 2025-11-20T11:54:51Z 置信度 0.70
• Mo–Si Alloys Studied by Atomistic Computer Simulations Using a Novel Machine‐Learning Interatomic Potential: Thermodynamics and Interface Phenomena
  A machine‐learning interatomic potential for Mo–Si alloys based on the atomic cluster expansion formalism is presented, its performance is validated, and it is applied for studying interface phenomena. Structural parameters, elastic constants, and melting temp…
  crossref Olena Lenchuk, Jochen Rohrer, Karsten Albe 2024-05-05T22:47:55Z 置信度 0.70
• Data-Driven Construction of Machine-Learning-Based Interatomic Potentials for GasSurface Scattering Dynamics: The Case of NO on Graphite
  crossref 2026-05-13T04:00:23Z 置信度 0.70
• Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities
  crossref 2023-12-04T10:31:15Z 置信度 0.70
• Insights into Lithium Diffusion in Crystalline and Amorphous Solid Electrolytes with Machine Learning Interatomic Potentials
  Amorphization is a widely used approach to tune the ionic conductivity in solid electrolytes, but its effect in different anion chemistries remains poorly understood. In this work, we employ molecular dynamics (MD) simulations with machine learning interatomic…
  crossref Adesh Rohan Mishra, Ji Qi, Shyue Ping Ong 2026-08-26T05:32:12Z 置信度 0.70
• Machine-learning interatomic potential for AlN for epitaxial simulation
  crossref Nicholas Taormina, Emir Bilgili, Jason Gibson, Richard Hennig 等 2025-12-29T12:06:44Z 置信度 0.70
• Abnormal strain-dependent thermal conductivity in biphenylene monolayer using machine learning interatomic potential
  Applying tensile strain on an intrinsic lattice always results in the reduction in thermal conductivity due to the red-shift of phonon frequency and enhanced phonon anharmonicity. However, in this work, we explored an unexpected strain-enhanced thermal conduct…
  crossref Guangyu Yang, Yanxiao Hu, Zhanjun Qiu, Bo-Lin Li 等 2023-02-23T11:15:50Z 置信度 0.70
• Author response for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref Castel, Nicolas, Andre, Dune, Edwards, Connor, Evans, Jack D. 等 2024-09-27T00:02:30Z 置信度 0.70
• Review for "Driving force of atomic ordering in Fe-Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations"
  crossref 2025-03-01T16:20:52Z 置信度 0.70
• Data-Driven Construction of Machine-Learning-Based Interatomic Potentials for GasSurface Scattering Dynamics: The Case of NO on Graphite
  crossref 2026-05-13T04:00:23Z 置信度 0.70
• Interatomic potential construction with self-learning and adaptive database
  We have developed an automatic machine learning potential (MLP) construction scheme, the self-learning and adaptive database (SLAD). The sample structures for training are collected by the molecular dynamics simulations using the MLP itself with the aid of the…
  crossref Kazutoshi Miwa, Hiroshi Ohno 2017-10-02T10:45:04Z 置信度 0.70
• Atomistic Insights into Cu/Amorphous-TaxN Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction
  Accurate understanding and control of interfacial adhesion between Cu and TaxN diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu interconnect systems in semiconductor devices. Amorphous tantalum nitride (a-TaxN) barri…
  crossref 2025-12-03T06:10:21Z 置信度 0.70
• Review for "Dynamic Origins of Cation-Modulated Stability in Tin-Based Perovskites Revealed through Combined Fine-tuned Machine Learning Interatomic Potentials and Experiments"
  crossref 2025-12-15T21:05:48Z 置信度 0.70
• Correction to Density Functional Theory Calculations and Machine Learning Interatomic Potentials for Molten Salts to Achieve Experimental Accuracy
  Despite the considerable success of density functional theory (DFT) in a broad class of materials, there are no exchange–correlation functionals or dispersion corrections that can systematically achieve high accuracy in molten salt simulations; for example, th…
  crossref 2024-11-06T07:30:23Z 置信度 0.70
• Model accuracy and data heterogeneity shape uncertainty quantification in machine learning interatomic potentials
  Abstract Machine learning interatomic potentials (MLIPs) enable accurate atomistic modeling, but reliable uncertainty quantification (UQ) remains elusive. In this study, we investigate two UQ strategies, ensemble learning and D-optimality, within the atomic cl…
  crossref Fei Shuang, Zixiong Wei, Kai Liu, Wei Gao 等 2026-01-26T22:53:32Z 置信度 0.70
• Review for "Driving force of atomic ordering in Fe-Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations"
  crossref 2025-03-01T16:20:52Z 置信度 0.70
• Decision letter for "Phonon transport and thermoelectric properties of Heusler-like Li2CuSb enabled by machine-learning interatomic potentials"
  crossref 2026-08-11T21:08:09Z 置信度 0.70
• Review for "Driving force of atomic ordering in Fe-Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations"
  crossref 2025-03-01T16:20:52Z 置信度 0.70
• Fe-TiC-H machine learning interatomic potential for predicting hydrogen segregation and diffusion in TiC/Fe
  Carbide particles act as effective hydrogen traps that can mitigate hydrogen embrittlement in steels, with their trapping efficacy governed by the binding energy of hydrogen at atomic-scale sites within the carbide and at the carbide/matrix interface. Within t…
  crossref Saurabh Sagar, Poulumi Dey, Francesco Maresca 2026-06-05T14:16:43Z 置信度 0.70
• Temperature- and pressure-dependent phonon transport properties of SnS across phase transition from machine-learning interatomic potential
  crossref Niuchang Ouyang, Chen Wang, Yue Chen 2022-04-10T08:59:04Z 置信度 0.70
• Machine-Learning Interatomic Potentials Achieving CCSD(T) Accuracy for Systems with Extended Covalent Networks and van der Waals Interactions
  crossref 2026-03-03T19:21:02Z 置信度 0.70
• Decision letter for "Phonon transport and thermoelectric properties of Heusler-like Li2CuSb enabled by machine-learning interatomic potentials"
  crossref 2026-08-11T21:08:09Z 置信度 0.70
• Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
  crossref 2024-01-10T16:30:26Z 置信度 0.70
• Review for "Driving force of atomic ordering in Fe-Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations"
  crossref 2025-03-01T16:20:52Z 置信度 0.70
• Atomistic Insights into Cu/Amorphous-TaxN Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction
  Accurate understanding and control of interfacial adhesion between Cu and TaxN diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu interconnect systems in semiconductor devices. Amorphous tantalum nitride (a-TaxN) barri…
  crossref 2025-12-03T06:10:21Z 置信度 0.70
• Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
  Abstract Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error and identifying informative training samples in active learning (AL) strategies. In this study, we evaluate uncertainty estimations of …
  crossref Matthias Holzenkamp, Dongyu Lyu, Ulrich Kleinekathöfer, Peter Zaspel 2025-09-22T22:52:23Z 置信度 0.70
• Review for "Driving force of atomic ordering in Fe-Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations"
  crossref 2025-03-01T16:20:52Z 置信度 0.70
• Review for "Driving force of atomic ordering in Fe-Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations"
  crossref 2025-03-01T16:20:52Z 置信度 0.70
• Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data^*
  Abstract The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum description of the electronic structure, while widely use…
  crossref Bowen Han, Yongqiang Cheng 2025-08-11T22:51:25Z 置信度 0.70
• Supplementary material to "Machine learning interatomic potentials with accurate long-range interactions for molecular dynamics collision simulations of atmospherically-relevant molecules"
  crossref Ivo Neefjes, Jakub Kubečka, Jonas Elm 2026-02-16T11:49:54Z 置信度 0.70
• CrySPR: A Python interface for implementation of crystal structure pre-relaxation and prediction using machine-learning interatomic potentials
  The functional properties of crystalline inorganic materials in a variety of applications including, but not limited to, catalysts, batteries, solar cells, electronics, fundamentally depend on their crystal structures. Discovery of novel materials could be tra…
  crossref Wei Nong, Ruiming Zhu, Kedar Hippalgaonkar 2024-08-08T07:47:46Z 置信度 0.70
• Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies
  We report that the single interatomic potential, developed using Gaussian regression of data from density functional theory calculations, has high accuracy and flexibility to describe phonon transport with ab initio accuracy in two different atomistic configur…
  crossref Hasan Babaei, Ruiqiang Guo, Amirreza Hashemi, Sangyeop Lee 2019-07-29T15:11:44Z 置信度 0.70
• Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials
  Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) simulations…
  crossref 2025-11-12T14:21:45Z 置信度 0.70
• Decision letter for "Accelerating the discovery of disordered multi-component solid-state electrolytes using machine learning interatomic potentials"
  crossref 2025-09-06T21:31:23Z 置信度 0.70
• Machine learning interatomic potential enables interface-level insights into cathode/solid electrolyte adhesion in sodium-ion batteries
  crossref Woojin Shin, Kyoungmin Min 2026-02-27T08:19:13Z 置信度 0.70
• Decision letter for "<b>Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials</b>"
  crossref 2025-12-23T21:07:04Z 置信度 0.70
• Machine-learning interatomic potential for radiation damage effects in bcc-iron
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• Accessible hybrid DFT-quality NMR crystallography via gas-phase Machine Learning Interatomic Potentials
  Nuclear magnetic resonance (NMR) crystallography is a robust method for structure determination, but its reliance on density functional theory (DFT) for geometry refinement limits its speed and accessibility. Recent machine‑learning predictors such as ShiftML3…
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• Author response for "<b>Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials</b>"
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• A general purposed machine learning interatomic potential for Mg-Al-Si-O system suitable for Earth materials at high pressure and temperature conditions
  Abstract Accurate phase diagrams and thermodynamic properties of Earth materials are essential for advancing geophysical, geodynamical and geological studies. Apart from experiment, atomistic simulations, particularly molecular dynamics, can be used to obtain …
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• Active Learning and Uncertainty Quantification for Machine Learning Interatomic Potentials.
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• Decision letter for "<b>Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials</b>"
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• The use of machine learning interatomic potentials for the verification of experimental molecular crystal structures
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• Machine learning interatomic potentials with accurate long-range interactions for molecular dynamics collision simulations of atmospherically-relevant molecules
  Recent advances in machine learning interatomic potentials have enabled the simulation of cluster formation from precursor vapor at a high level of theory. However, performing these simulations requires verifying that the models accurately describe cluster for…
  crossref Ivo Neefjes, Jakub Kubecka, Jonas Elm 2026-03-14T05:28:02Z 置信度 0.70
• Decision letter for "Accelerating the discovery of disordered multi-component solid-state electrolytes using machine learning interatomic potentials"
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