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• Computational Study of Na Diffusion and Conduction in P2- and O3-Na2x[Nixti1–X]O2 Materials with Machine-Learning Interatomic Potentials
  crossref Yining He, Qian Chen, Wei Lai 2023-04-28T07:23:05Z 置信度 0.70
• Full-stack quantification of variability in predicting ion transport properties using machine-learned interatomic potentials
  Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is, therefore, crucial to understand and quantify the reliability of MLIPs for downstream property predict…
  crossref Tawfiqur Rakib, Lucas K. Wagner, Elif Ertekin 2026-07-31T14:14:48Z 置信度 0.70
• Reproduction of Melting and Crystallization of Sodium by Machine-Learning Interatomic Potential Based on Artificial Neural Networks
  The training requirements for machine-learning interatomic potential based on artificial neural networks (ANN) are investigated to reproduce melting and crystallization of sodium. Only when the virial stress tensor, as well as the potential energy and atomic f…
  crossref Ayu Irie, Shogo Fukushima, Akihide Koura, Kohei Shimamura 等 2021-08-18T01:00:25Z 置信度 0.70
• The free energy landscape of Li- and Na-ion transport in nanoconfinement with machine learning interatomic potentials
  Nanoconfined systems such as cation-exchanged zeolites provide tunable geometric and chemical environments at the nanoscale that may facilitate regulated ion transport for energy storage applications, and enable materials to extract Li from solutions. However,…
  crossref Sauradeep Majumdar, Swagata Roy, KyuJung Jun, Miguel Steiner 等 2025-10-14T02:15:33Z 置信度 0.70
• Interatomic Potential
  crossref 2009-10-31T15:05:11Z 置信度 0.70
• Understanding and mitigating distribution shifts for universal machine learning interatomic potentials
  We find common distribution shifts that pose challenges for universal machine learning interatomic potentials (MLIPs). We develop test-time refinement strategies that mitigate the shifts and provide insights into why MLIPs struggle to generalize.
  crossref Tobias Kreiman, Aditi S. Krishnapriyan 2025-12-04T22:21:16Z 置信度 0.70
• SPARC: An Automated Workflow Toolkit for Accelerated Active Learning of Reactive Machine Learning Interatomic Potentials
  Verma et al., (2026). SPARC: An Automated Workflow Toolkit for Accelerated Active Learning of Reactive Machine Learning Interatomic Potentials. Journal of Open Source Software, 11(121), 9468, https://doi.org/10.21105/joss.09468
  crossref Rahul Verma, Nisarg Joshi, Jim Pfaendtner 2026-05-06T15:30:36Z 置信度 0.70
• Unraveling the Effects of Pore Size and Diamine Functionalization on CO2 Diffusion in Metal-Organic Frameworks using Machine-Learning Interatomic Potentials
  M2(dobdc) (dobdc4- = 2,5-dioxido-1,4-benzenedicarboxylate; M = Mg, Mn, Fe, Co, Ni, Cu, Zn), commonly referred to as M-MOF-74, and its variants have been extensively studied for their outstanding CO2 capture performance. In particular, diamine-functionalized M2…
  crossref Joharimanitra Randrianandraina, Chang Seop Hong, Jung-Hoon Lee 2025-02-21T06:06:45Z 置信度 0.70
• Accuracy and Uncertainty Quantification of Phase Diagrams Calculated from Universal Machine Learning Interatomic Potentials
  State-of-the-art universal machine learning interatomic potentials (UMLIPs) provide accurate surrogate potential energy surfaces based on Density functional theory (DFT) data and enable rapid and efficient calculations of phase diagrams and prediction of therm…
  crossref Wenhao Zhang, Mariano Forti, Thomas Hammerschmidt, J.-C. Crivello 等 2026-07-25T05:59:33Z 置信度 0.70
• Thermodynamical phase-stability of a Cu-Al binary system using machine-learning interatomic potentials
  The mechanical properties of structural materials are critically affected by the solid phases that are present in their microstructure, including both their crystallographic structure and chemical order. Predicting the stability of these phases as a function o…
  crossref Edwin A. Antillon, Noam Bernstein 2025-08-08T14:04:18Z 置信度 0.70
• Benchmarking machine learning interatomic potentials via phonon anharmonicity
  Abstract Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a …
  crossref Sasaank Bandi, Chao Jiang, Chris A Marianetti 2024-07-25T00:47:50Z 置信度 0.70
• Machine learning interatomic potentials with accurate long-range interactions for molecular dynamics collision simulations of atmospherically-relevant molecules
  Abstract. Molecular collisions and subsequent clustering events are fundamental to atmospheric cluster formation. Accurately modeling these processes requires interatomic potentials that capture long-range forces governing collision kinetics and short-range qu…
  crossref Ivo Neefjes, Jakub Kubečka, Jonas Elm 2026-02-16T11:49:54Z 置信度 0.70
• Author response for "Accelerating the discovery of disordered multi-component solid-state electrolytes using machine learning interatomic potentials"
  crossref Yanhao Deng, Yan Li, Gopalakrishnan Sai Gautam, Bonan Zhu 等 2025-09-06T21:31:23Z 置信度 0.70
• Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
  Abstract Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the co…
  europepmc Juno Nam, Jiayu Peng, Rafael Gómez-Bombarelli 2025 置信度 0.80
• Review for "Modelling Silica using MACE-MP Machine Learnt Interatomic Potentials"
  crossref 2025-07-21T21:05:55Z 置信度 0.70
• Machine learning a universal harmonic interatomic potential for predicting phonons in crystalline solids
  Phonons, as quantized vibrational modes in crystalline materials, play a crucial role in determining a wide range of physical properties, such as thermal and electrical conductivity, making their study a cornerstone in materials science. In this study, we pres…
  crossref Huiju Lee, Yi Xia 2024-03-04T12:17:50Z 置信度 0.70
• A machine learning interatomic potential for high entropy alloys
  crossref Lianping Wu, Teng Li 2024-04-05T18:15:32Z 置信度 0.70
• A critical review of machine learning interatomic potentials and Hamiltonian
  Machine learning interatomic potentials (ML-IAPs) and machine learning Hamiltonian (ML-Ham) have revolutionized atomistic and electronic structure simulations by offering near ab initio accuracy across extended time and length scales. In this Review, we summar…
  crossref Yifan Li, Xiuying Zhang, Mingkang Liu, Lei Shen 2025-07-17T01:19:01Z 置信度 0.70
• HB238 Adsorption on Coinage Metal Surfaces: Insights from a Combined Machine-Learning Interatomic Potential and Bayesian Optimization Framework for Interfacial Electronic Structure
  Organic donor–acceptor (D-A) molecules with permanent dipoles are attractive candidates for flexible electronics. Merocyanines represent a well-studied class of such systems, both experimentally and theoretically. In this study, the adsorption of a single mero…
  crossref Ritu Tomar, Thomas Bredow 2026-04-20T14:49:38Z 置信度 0.70
• Developing Machine Learning Interatomic Potential for Fe-Cr-Ni Alloys
  Accurate prediction of creep and fatigue behavior of stainless steel at elevated temperatures in hydrogen environment requires fundamental understanding of alloy-hydrogen interaction at cross-scale including bulk lattice and key defects such as vacancies, grai…
  crossref Shiqiang Hao, Saro San, Yi Wang, Michael Gao 2025-04-17T02:21:02Z 置信度 0.70
• Thermal conductivity of single-layer MoS2(1−x)Se2x alloys from molecular dynamics simulations with a machine-learning-based interatomic potential
  crossref Xiaokun Gu, C.Y. Zhao 2019-04-24T05:45:01Z 置信度 0.70
• Optimal Development of Transferable Machine Learning Interatomic Potentials using Active Learning
  crossref David Montes de Oca Zapiain, Mitchell Wood, Dionysios Sema, Aidan Thompson 2024-08-22T03:34:42Z 置信度 0.70
• Developing a machine-learning interatomic potential for non-covalent interactions in proteins
  A PAirwise Non-covalent Interaction Potential (PANIP) with accuracy comparable to the ωB97X-D3BJ/def2-TZVPP level for non-covalent interactions in proteins.
  crossref Lejia Zeng, Xintong Zhang, Yuchan Pei, Lifeng Zhao 等 2026-06-08T13:55:32Z 置信度 0.70
• Construction of Al–Si interatomic potential based on Bayesian active learning
  Nanoscale simulations for optimizing the performance and processing of Al–Si alloys are currently facing two major obstacles: the scarcity of high-quality semi-empirical potentials tailored to complex alloy systems , and the prohibitively high computational co…
  crossref Xuedong Liu, Yan Zhang, Hui Xu 2024-10-02T11:09:13Z 置信度 0.70
• Decision letter for "Do we really need machine learning interatomic potentials for modeling amorphous metal oxides?Case study on amorphous alumina by recycling an existing ab initio database"
  crossref 2024-04-15T08:01:51Z 置信度 0.70
• Optimal data generation for machine learned interatomic potentials
  Abstract Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human effort. A compu…
  crossref Connor Allen, Albert P Bartók 2022-10-17T22:21:16Z 置信度 0.70
• Generalization of SNAP to Arbitrary Machine-Learning Interatomic Potentials in LAMMPS.
  crossref Aidan Thompson 2022-03-25T02:16:35Z 置信度 0.70
• Lattice dynamics simulation using machine learning interatomic potentials
  crossref V.V. Ladygin, P.Yu. Korotaev, A.V. Yanilkin, A.V. Shapeev 2019-10-19T11:34:45Z 置信度 0.70
• Decision letter 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
• From O Adsorption to Fe Oxide Growth: Benchmarking Reactive Force Fields and Universal Machine Learning Interatomic Potentials against DFT for BCC Fe Surface Oxidation
  crossref Zixiong Wei, Fei Shuang, Poulumi Dey 2025-11-01T20:43:03Z 置信度 0.70
• Cartesian atomic moment machine learning interatomic potentials
  Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency. While leading MLIPs rely on representing atomic environments using spherica…
  crossref Mingjian Wen, Wei-Fan Huang, Jin Dai, Santosh Adhikari 2025-05-09T15:58:24Z 置信度 0.70
• Development of a machine-learning interatomic potential for uranium under the moment tensor potential framework
  crossref Hongjian Chen, Dingwang Yuan, Huayun Geng, Wangyu Hu 等 2023-07-27T15:03:58Z 置信度 0.70
• UMA-ASE: A FAIR-Aware Environment for Machine-Learned Interatomic Potential Workflows with UMA and ASE
  Machine-learned interatomic potentials (MLIPs) are rapidly changing the operating regime of computational chemistry by narrowing the traditional gap between first-principles accuracy and large-scale atomistic simulation. UMA-ASE was developed to make this tran…
  crossref Carles Bo, Gabriela Dias Da Silva, Farzaneh Hosseini, Ajmal Rahman Mullukkandy 等 2026-03-23T13:29:39Z 置信度 0.70
• The use of machine learning interatomic potentials for the verification of experimental molecular crystal structures
  crossref M. Hušák, F. Fňukal, J. Čejka 2026-07-27T13:01:52Z 置信度 0.70
• Machine Learned Interatomic Potential Development for W-ZrC.
  This paper describes objective technical results and analysis.Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S.
  crossref Ember Sikorski, Julien Tranchida, Mary Cusentino, Mitchell Wood 等 2023-11-03T03:02:39Z 置信度 0.70
• Efficient Automated Workflow for Radical Reaction Networks in Environmental Disinfectants: A Peroxyacetic Acid Case Study with Machine Learning Interatomic Potentials
  Peroxy-type disinfectants initiate chain reactions upon activation, but the underlying mechanisms of organic radical formation remain difficult to fully elucidate. In this study, we combine Density Functional Theory (DFT) with machine learning-based interatomi…
  crossref Fulin Shao, Weiying Li 2025-02-06T04:44:56Z 置信度 0.70
• Author response for "Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals"
  crossref Flaviano Della Pia, Benjamin X. Shi, Venkat Kapil, Andrea Zen 等 2025-05-25T06:08:09Z 置信度 0.70
• Interatomic Potential Development
  crossref G.J. Ackland 2012-02-12T12:16:15Z 置信度 0.70
• Toward machine learning interatomic potentials for modeling uranium mononitride
  Abstract Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at…
  crossref Lorena Alzate-Vargas, Kashi N Subedi, Nicholas Lubbers, Michael W D Cooper 等 2025-09-02T23:48:47Z 置信度 0.70
• Decision letter 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
• Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
  We show that the Gaussian Approximation Potential (GAP) machine-learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total energies, forces, and…
  crossref Daniele Dragoni, Thomas D. Daff, Gábor Csányi, Nicola Marzari 2018-01-30T10:32:36Z 置信度 0.70
• Deep-learning interatomic potential for iron at extreme conditions
  Atomistic simulations play an important role in elucidating the physical properties of iron at extreme pressure and temperature conditions, which in turn provide crucial insights into the present state and thermal evolution of the earth's and planetary cores. …
  crossref Zhi Li, Sandro Scandolo 2024-05-14T14:13:27Z 置信度 0.70
• Review for "Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials"
  crossref 2025-04-25T17:14:06Z 置信度 0.70
• Decision letter 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
• Machine Learning of Atomic Forces from Quantum Mechanics: a Model Based on Pairwise Interatomic Forces
  We present a new chemically intuitive approach, pairF-Net, to directly predict the atomic forces in a molecule to quantum chemistry accuracy using machine learning techniques. A residual artificial neural network has been designed and trained with features and…
  crossref Ismaeel Ramzan, Linghan Kong, Richard Bryce, Neil Burton 2021-04-20T06:15:11Z 置信度 0.70
• Development of a Classical Interatomic Potential for MAPbBr3
  We develop a classical\ninteratomic potential for MAPbBr3. The model belongs to\nthe class of MYP force-fields for hybrid perovskites\nbased on two-body Buckhingam-Coulomb and dispersive terms to describe\norganic–inorganic interactions and already successfull…
  crossref 2020-04-07T09:18:15Z 置信度 0.70
• Review for "Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials"
  crossref 2025-04-25T17:14:06Z 置信度 0.70
• Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk
  We analyse the efficacy of machine learning (ML) interatomic potentials (IP) in modelling gold (Au) nanoparticles. We have explored the transferability of these ML models to larger systems and established simulation times and size thresholds necessary for accu…
  crossref Marco Fronzi, Roger D. Amos, Rika Kobayashi 2023-06-09T08:37:33Z 置信度 0.70
• Fusing Delta Learning and Machine Learned Interatomic Potentials for Efficient High Throughput Calculation of Surface Kinetic Parameters
  Heterogeneous catalysis is critical in most industrial chemical processes. Microkinetic models can be used to greatly facilitate optimization of catalyst design and process conditions, but require thermochemical and kinetic parameters for all relevant species …
  crossref Matthew S Johnson, David H Bross, Lars Schaaf, Alvaro Vazquez-Mayagoitia 等 2026-06-02T09:09:12Z 置信度 0.70
• Author response for "ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials"
  crossref David, Rolf, Puente, Miguel de la, Gomez, Axel, Anton, Olaia 等 2024-11-02T08:01:41Z 置信度 0.70
• Fusing Delta Learning and Machine Learned Interatomic Potentials for Efficient High Throughput Calculation of Surface Kinetic Parameters
  Heterogeneous catalysis is critical in most industrial chemical processes. Microkinetic models can be used to greatly facilitate optimization of catalyst design and process conditions, but require thermochemical and kinetic parameters for all relevant species …
  crossref Matthew S Johnson, David H Bross, Lars Schaaf, Alvaro Vazquez-Mayagoitia 等 2026-02-24T05:56:36Z 置信度 0.70
• Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
  Abstract Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be…
  crossref Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky 等 2023-06-15T15:02:06Z 置信度 0.70
• Review for "Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials"
  crossref 2025-04-25T17:14:06Z 置信度 0.70
• Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction
  Molecular crystal structure prediction (CSP) faces a persistent computational bottleneck: it requires exhaustive sampling of vast packing landscapes while resolving energy differences of only a few kJ·mol-1. We introduce BOMLIP-CSP, an open-source Python frame…
  crossref Chengxi Zhao, Zhaojia Ma, Dingrui Fan, Siyu Hu 等 2025-08-26T05:05:09Z 置信度 0.70
• Decision letter 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
• Intermolecular Dynamics of Water: Suitability of Reactive Interatomic Potential
  Reactive interatomic\npotentials for water have been developed by\nresearchers based on their ability of bond breaking and formation,\nwhich have numerous advantages and applications in different fields.\nThe question that is being addressed in this work is wh…
  crossref 2020-04-08T14:57:17Z 置信度 0.70
• Review for "Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials"
  crossref 2025-04-25T17:14:06Z 置信度 0.70
• Structure–property relations of silicon oxycarbides studied using a machine learning interatomic potential
  Abstract Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure–property relations in the system. In this work, we fit an atomic cluster ex…
  crossref Niklas Leimeroth, Jochen Rohrer, Karsten Albe 2024-06-18T01:00:49Z 置信度 0.70
• (Invited) Design Insights into Solid Electrolytes with Machine Learning Interatomic Potentials
  Machine learning interatomic potentials (MLPs) have had a transformative impact in the computational modeling of materials by enabling accurate simulations at length and time scales beyond that accessible by ab initio methods. In this talk, I will share new in…
  crossref Shyue Ping Ong, Adesh Mishra, Ji Qi, Zihan Yu 2024-12-19T21:27:50Z 置信度 0.70
• Review for "Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials"
  crossref 2025-04-25T17:14:06Z 置信度 0.70
• Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential
  Abstract The phase change compound Ge 2 Sb 2 Te 5 (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule h…
  crossref Omar Abou El Kheir, Luigi Bonati, Michele Parrinello, Marco Bernasconi 2024-02-10T07:02:20Z 置信度 0.70
• Towards better efficiency of interatomic linear machine learning potentials
  crossref Alexandra M. Goryaeva, Jean-Bernard Maillet, Mihai-Cosmin Marinica 2019-05-11T17:42:35Z 置信度 0.70
• Investigation of Primary Radiation Damage in Nanocrystalline Tantalum Using Machine-Learning Interatomic Potential: An Atomistic Simulation Study
  crossref Mouparna Manna, Snehanshu Pal 2024-05-31T06:02:11Z 置信度 0.70
• Unified Graph-Based Interatomic Potential for Perovskite Structure Optimization
  crossref 2026-01-16T17:30:21Z 置信度 0.70
• Machine learning interatomic potential calculations for designing layered oxides in sodium-ion battery systems with low environmental impacts
  Abstract Layered P2-type structures of MnNi oxides are attracting attention as cathode materials for sodium-ion batteries. However, life-cycle assessment analysis showed that Ni usage has environmental impact, making its substitution necessary. This paper pres…
  crossref Kohei Tada, Riki Kataoka 2026-05-04T17:10:03Z 置信度 0.70
• An accurate and transferable machine learning interatomic potential for nickel
  Abstract Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but s…
  crossref Xiaoguo Gong, Zhuoyuan Li, A. S. L. Subrahmanyam Pattamatta, Tongqi Wen 等 2024-08-17T16:01:58Z 置信度 0.70
• GB‐score: Minimally designed machine learning scoring function based on distance‐weighted interatomic contact features
  Abstract In recent years, thanks to advances in computer hardware and dataset availability, data‐driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructing…
  crossref Milad Rayka, Rohoullah Firouzi 2023-02-01T08:40:00Z 置信度 0.70
• Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
  • ML potentials can be a reliably tool for simulating materials properties near dynamical instability • Elastic moduli of β-Ti 94-x Nb x Zr 6 alloys show strong non-linearity near the point of instability • The alloys exhibit the elinvar effect over a broad te…
  crossref Boburjon Mukhamedov, Ferenc Tasnádi, Igor A. Abrikosov 2025-03-21T19:59:55Z 置信度 0.70
• Research on machine learning interatomic potentials for titanium oxide ceramic materials
  crossref Jiali Ren, Guanghao Zhang, Xuemei Wang, Ye Han 2025-04-23T16:03:25Z 置信度 0.70
• Efficient Rigorous Stress Field Calculations for Machine Learning Interatomic Potentials
  The computation of atomistic stress fields plays a central role in linking atomistic simulations to continuum models. However, commonly used stress measures, such as the per-atom virial stress, do not constitute true spatial stress fields consistent with the c…
  crossref Nikhil Admal, Jaekwang Kim, Amit Gupta, Ellad Tadmor 2026-08-19T22:48:28Z 置信度 0.70
• Improving Aqueous Metal Salt Interactions Using Machine-Learned Interatomic Potentials
  Accurate modeling of aqueous metal salt solutions is essential for understanding processes relevant to environmental safety, energy storage, and separation technologies. Trace metals such as As3+ at low concentrations pose significant health and environmental …
  crossref 2025-09-26T12:20:20Z 置信度 0.70
• Quantification and Propagation of Uncertainties in Machine Learning Interatomic Potentials for Molecular Dynamics.
  This paper describes objective technical results and analysis.Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.
  crossref Khachik Sargsyan, Logan Williams, Katherine Johnston, Habib Najm 2023-11-03T03:14:00Z 置信度 0.70
• A Hessian-based assessment of atomic forces for training machine learning interatomic potentials
  In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PESs) with close to first-principles accuracy. Most current MLPs rely on atomic energy contributions…
  crossref Marius Herbold, Jörg Behler 2022-02-28T16:05:51Z 置信度 0.70
• Development of a machine-learning interatomic potential for molecular dynamics simulations of titanium-based MXenes under aqueous lubrication
  crossref Chi Nhan Tran, Thi Dinh Ta, Kiet Tieu, Cheng Lu 等 2026-06-26T23:33:31Z 置信度 0.70
• Transfer learning on universal interatomic potential embeddings improves generalization in structure-property defect models
  Oxygen vacancy formation energies govern the performance of metal oxides across energy conversion, catalysis, and electronics, yet predicting them accurately without density functional theory calculations for novel chemical systems remains challenging. Here, w…
  crossref Matthew D. Witman, Sebastian Pujet, Andrew J. E. Rowberg, Christopher Sutton 等 2026-05-27T05:26:17Z 置信度 0.70
• Efficient equivariant model for machine learning interatomic potentials
  In modern computational materials, machine learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional molecular dynamics (MD) simulations. However, existing models typically sacrifice either accuracy o…
  crossref Ziduo Yang, Xian Wang, Yifan Li, Qiujie Lv 等 2025-02-26T15:08:56Z 置信度 0.70
• Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models
  Transition state (TS) search is crucial for illuminating chemical reaction mechanisms but remains the major bottleneck in automated discovery because of the high computational cost. Recently, machine learning interatomic potentials (MLIPs) and generative model…
  crossref Qiyuan Zhao, Yunhong Han, Duo Zhang, Jiaxu Wang 等 2025-05-22T01:44:16Z 置信度 0.70
• Pauli Charges Improve the Training of Machine-Learned Interatomic Potentials
  Despite the utility of machine-learned interatomic potentials (MLIPs), they can be difficult to train and use due to a lack of well-defined methods to assess their accuracy. This problem can be mitigated by combining traditional MLIPs with pair potentials that…
  crossref Mark DelloStritto, Michael L. Klein 2026-07-17T08:19:14Z 置信度 0.70
• Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
  The original version of this Article contained some errors in the References. In references 12, 13, 19, 49 and 52 the article numbers were incorrectly given as ‘1’. This has been replaced with the correct article numbers in the corrected version. In the origin…
  crossref Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky 等 2023-07-11T11:02:11Z 置信度 0.70
• TrIPTransformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias
  Accurate interatomic energies and forces enable high-quality molecular dynamics simulations, torsion scans, potential energy surface mappings, and geometry optimizations. Machine learning algorithms have enabled rapid estimates of the energies and forces with …
  crossref 2023-12-27T16:40:10Z 置信度 0.70
• Machine Learning Interatomic Force Fields for Carbon Allotropic Materials
  crossref Xiangjun Liu, Quanjie Wang, Jie Zhang 2021-03-26T09:02:46Z 置信度 0.70
• Challenges and Opportunities of Machine Learning Interatomic Potentials in Heterogeneous Catalysis
  The design of novel catalysts gets the fundamental rational on accurate and efficient modeling of reactivity on surfaces and materials. To reach this detailed atomistic understanding density functional theory (DFT) has been the key computational technique. How…
  crossref Oliver Loveday, Kamila Kazmierczak, Núria López 2025-12-22T10:07:28Z 置信度 0.70
• Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
  Abstract Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and can therefore yield catastrophically incorrect predictions. Establishe…
  crossref Yuge Hu, Joseph Musielewicz, Zachary W Ulissi, Andrew J Medford 2022-11-30T22:41:35Z 置信度 0.70
• Investigating Ionic Diffusivity in Amorphous LiPON using Machine-Learned Interatomic Potentials
  Due to its immense importance as an amorphous solid electrolyte in thin-film devices, lithium phosphorus oxynitride (LiPON) has garnered significant scientific attention. However, investigating Li+ transport within the LiPON framework, especially across a Li||…
  crossref 2025-02-05T21:40:12Z 置信度 0.70
• SNAP and Beyond: Machine Learning Interatomic Potentials in LAMMPS.
  crossref Aidan Thompson 2023-11-04T03:37:48Z 置信度 0.70
• Node-equivariant message passing for efficient and accurate machine learning interatomic potentials
  A node-equivariant message-passing framework achieves high accuracy without costly edge-equivariant message passing.
  europepmc Yaolong Zhang, Hua Guo 2025-12-23T07:01:26Z 置信度 0.80
• Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models
  Transition state (TS) search plays a crucial role in reaction pathway analysis, offering insights into reaction mechanisms and aiding in the optimization of chemical processes. Recently, machine learning interatomic potentials (MLIPs) and generative models hav…
  crossref Qiyuan Zhao, Yunhong Han, Duo Zhang, Jiaxu Wang 等 2025-02-27T05:12:44Z 置信度 0.70
• Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
  We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor [Bart\'ok et al., Phys. Rev. B 87, 184115 (2013).]. Our aim is to improve the computational efficiency of SOAP-based similarity ke…
  crossref Miguel A. Caro 2019-08-01T09:16:07Z 置信度 0.70
• Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials
  crossref 2026-07-23T02:10:19Z 置信度 0.70
• LiPS Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
  crossref 2026-03-18T14:00:14Z 置信度 0.70
• Validation workflow for machine learning interatomic potentials for complex ceramics
  crossref Kimia Ghaffari, Salil Bavdekar, Douglas E. Spearot, Ghatu Subhash 2024-04-01T06:49:58Z 置信度 0.70
• Interatomic Potential Development
  crossref Graeme J. Ackland, Giovanni Bonny 2019-10-01T21:35:18Z 置信度 0.70
• Dual-Coordinate Relative Free Energy Simulations using Machine-Learned Interatomic Potentials
  The integration of machine-learned interatomic potentials (MLIPs) into free energy simulations (FES) offers the promise of near-quantum mechanical accuracy at a reduced computational cost. However, employing MLIPs for alchemical relative free energy calculatio…
  crossref Anna Katharina Picha, Stefan Boresch 2026-04-29T11:39:47Z 置信度 0.70
• Potential Application of Machine Learning in Forensic Anthropology
  Forensic anthropology plays an imperative part in examining skeletal remains and providing necessary insights into legal proceedings. With the advancement of technology, the assimilation of machine-learning (ML) algorithms with anthropological methods will pro…
  crossref Vineeta Saini, Arunima Dutta 2025-07-24T17:14:33Z 置信度 0.70
• Machine learning potential for serpentines
  Serpentines are layered hydrous magnesium silicates (MgO·SiO2·H2O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the …
  crossref Hongjin Wang, Chenxing Luo, Renata Wentzcovitch 2024-09-26T15:36:13Z 置信度 0.70
• Groundwater potential model using statistics and hybrid machine learning approaches
  The present study was conducted to delineate the groundwater potential in Kanchanaburi Province, Thailand based on groundwater yield, groundwater contamination risk, and groundwater quality. In this study, an ensemble model was created by combining Analytical …
  crossref Ngoc Nguyen 2024-05-29T08:20:43Z 置信度 0.70
• Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations
  Deep eutectic solvents have recently gained significant attention as versatile and inexpensive materials with many desirable properties and a wide range of applications. In particular, their characteristics, similar to those of ionic liquids, make them a promi…
  crossref Omid Shayestehpour, Stefan Zahn 2024-10-01T06:41:08Z 置信度 0.70
• Exploring the Potential of Sparse Coding for Machine Learning
  While deep learning has proven to be successful for various tasks in the field of computer vision, there are several limitations of deep-learning models when compared to human performance. Specifically, human vision is largely robust to noise and distortions, …
  crossref Sheng Lundquist 2021-01-29T21:04:57Z 置信度 0.70
• Discrepancies and error evaluation metrics for machine learning interatomic potentials
  Abstract Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properti…
  crossref Yunsheng Liu, Xingfeng He, Yifei Mo 2023-09-26T09:03:10Z 置信度 0.70
• Anchor-Based Relative Free Energy Simulations using Machine-Learned Interatomic Potentials
  The integration of machine-learned interatomic potentials (MLIPs) into free energy simulations (FES) offers the promise of near-quantum mechanical accuracy at a reduced computational cost. However, employing MLIPs for alchemical relative free energy calculatio…
  crossref Anna Katharina Picha, Stefan Boresch 2026-03-31T05:55:32Z 置信度 0.70