AI 驱动科学发现 · 全部实体（第 35 / 100 页）

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• Review for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
  crossref 2025-10-30T21:08:47Z 置信度 0.70
• Review for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
  crossref 2025-10-30T21:08:47Z 置信度 0.70
• Review for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref 2024-09-27T00:02:30Z 置信度 0.70
• Review for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref 2024-09-27T00:02:30Z 置信度 0.70
• Review for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref 2024-09-27T00:02:30Z 置信度 0.70
• Review for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref 2024-09-27T00:02:30Z 置信度 0.70
• Accelerated Modeling of Interfacial Phases in the Ni-Bi System with Machine Learning Interatomic Potential
  High-performance modeling of interfacial phases is a challenge because of the low scalability of first-principle methods. Here we present a data-driven approach based on using the machine learning potential to address this problem. The developed model quantita…
  crossref Vadim Korolev, Artem Mitrofanov, Yaroslav Kucherinenko, Yurii Nevolin 等 2020-03-24T07:44:46Z 置信度 0.70
• Decision letter for "Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance"
  crossref 2025-11-10T21:07:27Z 置信度 0.70
• Review for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref 2024-09-27T00:02:30Z 置信度 0.70
• Decision letter for "Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance"
  crossref 2025-11-10T21:07:27Z 置信度 0.70
• Accelerated Discovery Protocol for Electrodeposited HER Catalysts through Machine Learning Interatomic Potential Screening and Quantum Chemical Refinement
  Discovering electrocatalysts for proton exchange membrane water electrolysis (PEMWE) requires navigating a vast space of multimetallic surfaces while remaining compatible with scalable synthesis. Here, we establish a general discovery framework that couples br…
  crossref Daehyun Kim, Suyeon Lee, HyoungJoon Jeon, Soo-Kil Kim 等 2026-08-24T10:38:17Z 置信度 0.70
• Author response for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref Lejia Zeng, Xintong Zhang, Yuchan Pei, Lifeng Zhao 等 2026-06-08T15:13:01Z 置信度 0.70
• Conformational Analysis of Macrocyclic Compounds Using a Machine-Learned Interatomic Potential
  Macrocyclic compounds play a vital role in many chemical and biological systems, yet their conformational analysis remains a significant challenge. In this work, we investigate the conformational landscape of macrocyclic compounds using a machine-learned inter…
  crossref 2025-10-15T21:40:12Z 置信度 0.70
• Grain Size Effects on the Deformation of Α-Fe Nanopolycrystals: Massively Large-Scale Molecular Dynamics Simulations Using Machine Learning Interatomic Potential
  crossref Kazuma Ito, Tatsuya Yokoi, Katsutoshi Hyodo, Hideki Mori 2024-11-21T17:39:52Z 置信度 0.70
• Review for "Machine Learning Interatomic Potentials in Engineering Perspective for Developing Cathode Materials"
  crossref 2024-07-30T17:13:50Z 置信度 0.70
• Machine-Learning Interatomic Potentials for Ge, Sb and Ge-Sb Mixtures
  crossref Tom Joseph Arbaugh 2024-09-05T13:11:50Z 置信度 0.70
• Review for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref 2025-10-08T21:09:03Z 置信度 0.70
• Conformational Analysis of Macrocyclic Compounds Using a Machine-Learned Interatomic Potential
  Macrocyclic compounds play a vital role in many chemical and biological systems, yet their conformational analysis remains a significant challenge. In this work, we investigate the conformational landscape of macrocyclic compounds using a machine-learned inter…
  crossref 2025-10-15T21:40:12Z 置信度 0.70
• Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential
  Polymers are a class of materials that are highly challenging\nto\ndeal with using first-principles methods. Here, we present an application\nof machine-learned interatomic potentials to predict structural and\ndynamical properties of dry and hydrated perfluor…
  crossref 2023-04-05T14:50:25Z 置信度 0.70
• Review for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref 2025-10-08T21:09:03Z 置信度 0.70
• Universal Machine Learning Interatomic Potentials: Surveying Solid Electrolytes
  We\napply ab initio molecular dynamics (AIMD) with on-the-fly machine\nlearning (ML) of interatomic potentials using the sparse Gaussian\nprocess regression (SGPR) algorithm for a survey of Li diffusivity\nin hundreds of ternary crystals as potential electroly…
  crossref 2021-08-19T12:18:11Z 置信度 0.70
• Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
  Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing. We report AIMNet2-Pd, a machine lear…
  crossref Dylan Anstine, Roman Zubatyuk, Liliana Gallegos, Robert Paton 等 2025-03-18T08:38:31Z 置信度 0.70
• Influence of Na-Substitution on Thermal Conductivity of Cathode Material Lifepo4:A Machine Learning Interatomic Potential-Assisted Investigation
  crossref Guofeng Xie, Shi-Yi Li, Qian Liu, Yu-Jia Zeng 等 2024-07-26T00:19:38Z 置信度 0.70
• Review for "Machine Learning Interatomic Potentials in Engineering Perspective for Developing Cathode Materials"
  crossref 2024-07-30T17:13:50Z 置信度 0.70
• Review for "Applicability of universal machine learning interatomic potentials to the simulation of steels"
  crossref 2025-10-22T01:13:00Z 置信度 0.70
• Machine learning interatomic potential can infer electrical response
  Abstract Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods, but do not by themse…
  crossref Peichen Zhong, Dongjin Kim, Daniel S. King, Bingqing Cheng 2025-12-22T01:52:52Z 置信度 0.70
• Review for "Accelerating Explicit Solvent Models of Heterogeneous Catalysts with Machine Learning Interatomic Potentials"
  crossref 2023-07-13T17:00:54Z 置信度 0.70
• Review for "Accelerating Explicit Solvent Models of Heterogeneous Catalysts with Machine Learning Interatomic Potentials"
  crossref 2023-07-13T17:00:54Z 置信度 0.70
• Review for "Applicability of universal machine learning interatomic potentials to the simulation of steels"
  crossref 2025-10-22T01:13:00Z 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 nonlocal electronic effects play a dec…
  crossref 2026-06-20T14:00:37Z 置信度 0.70
• Review for "Applicability of universal machine learning interatomic potentials to the simulation of steels"
  crossref 2025-10-22T01:13:00Z 置信度 0.70
• Performance and Cost Assessment of Machine Learning Interatomic Potentials
  Machine learning of the quantitative\nrelationship between local environment descriptors and the potential\nenergy surface of a system of atoms has emerged as a new frontier\nin the development of interatomic potentials (IAPs). Here, we present\na comprehensiv…
  crossref 2020-04-07T07:24:52Z 置信度 0.70
• Review for "Accelerating Explicit Solvent Models of Heterogeneous Catalysts with Machine Learning Interatomic Potentials"
  crossref 2023-07-13T17:00:54Z 置信度 0.70
• A ∆-Learning Approach to Machine Learning Interatomic Potentials for Intermolecular Interactions
  Machine-learning interatomic potentials (MLIPs) trained by directly learning the total interatomic interaction energies can suffer from limited transferability, unphysical behavior beyond a finite cutoff, and large errors for out-of-distribution geometries suc…
  crossref Nguyen Thien Phuc Tu, Christopher N. Rowley 2026-05-08T05:51:38Z 置信度 0.70
• Review for "Accelerating Explicit Solvent Models of Heterogeneous Catalysts with Machine Learning Interatomic Potentials"
  crossref 2023-07-13T17:00:54Z 置信度 0.70
• Machine-Learning Based Interatomic Potential for Studying the Properties of Crystal Structures
  Abstract In the process of modeling multilayer semiconductor nanostructures, an important role is played by the rapid acquisition of accurate values of the characteristics of the structure under consideration. One of these characteristics is the value of the i…
  crossref O. V. Uvarova, S. I. Uvarov 2022-01-27T11:03:50Z 置信度 0.70
• Review for "Applicability of universal machine learning interatomic potentials to the simulation of steels"
  crossref 2025-10-22T01:13:00Z 置信度 0.70
• Development of machine learning interatomic potential for zinc
  crossref Haojie Mei, Luyao Cheng, Liang Chen, Feifei Wang 等 2023-12-08T16:36:04Z 置信度 0.70
• Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential
  The\nphase-change material, Ge2Sb2Te5,\nis the canonical material ingredient for next-generation storage-class\nmemory devices used in novel computing architectures, but fundamental\nquestions remain regarding its atomic structure and physicochemical\nproperti…
  crossref 2020-04-09T07:05:21Z 置信度 0.70
• GoldThiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential
  Monolayer protected metal clusters comprise a rich class of molecular systems and are promising candidate materials for a variety of applications. While a growing number of protected nanoclusters have been synthesized and characterized in crystalline forms, th…
  crossref 2024-07-10T15:40:34Z 置信度 0.70
• Fast and accurate Fe–H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms
  Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H binary system. Howev…
  crossref Kazuma Ito 2026-05-20T19:34:31Z 置信度 0.70
• Decision letter for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
  crossref 2025-10-30T21:08:47Z 置信度 0.70
• Review for "Machine Learning Inversion of Interatomic Force Constants from Single-Crystal Inelastic Neutron Scattering"
  crossref 2026-03-17T21:11:08Z 置信度 0.70
• Machine learning for interatomic potential models
  The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler publis…
  crossref Tim Mueller, Alberto Hernandez, Chuhong Wang 2020-02-05T16:16:40Z 置信度 0.70
• Decision letter for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
  crossref 2025-10-30T21:08:47Z 置信度 0.70
• Review for "Machine Learning Inversion of Interatomic Force Constants from Single-Crystal Inelastic Neutron Scattering"
  crossref 2026-03-17T21:11:08Z 置信度 0.70
• Decision letter for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
  crossref 2025-10-30T21:08:47Z 置信度 0.70
• A Machine-Learning Interatomic Potential for Iron Under High Pressure and its Application to Shock Response
  crossref Xin Zeng, Shifang Xiao, Yangchun Chen, Xiaofan Li 等 2025-05-13T18:39:41Z 置信度 0.70
• Review for "Machine Learning Inversion of Interatomic Force Constants from Single-Crystal Inelastic Neutron Scattering"
  crossref 2026-03-17T21:11:08Z 置信度 0.70
• Review for "Machine Learning Inversion of Interatomic Force Constants from Single-Crystal Inelastic Neutron Scattering"
  crossref 2026-03-17T21:11:08Z 置信度 0.70
• Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures at ab initio Accuracy
  This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-ab initio accuracy across diverse architectures. Benchmarking five leading MLIP frameworks (MACE, GRACE, SevenNet, Matter…
  crossref 2026-03-04T21:31:41Z 置信度 0.70
• Decision letter for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
  crossref 2025-10-30T21:08:47Z 置信度 0.70
• Primary Damage Cascade Simulations in Tungsten Using the Allegro Machine Learning Interatomic Potential
  crossref Danna Jia, Myles Stapelberg, Michael Short 2026-06-06T00:17:49Z 置信度 0.70
• Author response for "Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery"
  crossref Ju, Suyeon, You, Jinmu, Kim, Gijin, Park, Yutack 等 2025-05-07T17:16:36Z 置信度 0.70
• Unveiling Defect Motifs in Amorphous GeSe Using Machine Learning Interatomic Potentials
  Ovonic threshold switching (OTS) selectors are pivotal in nonvolatile memory devices due to their nonlinear electrical characteristics and polarity-dependent threshold voltages. However, the atomic-scale origins of the defect states responsible for these behav…
  crossref 2025-10-01T14:00:26Z 置信度 0.70
• Review 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
• Probing Machine Learning Interatomic Potentials On Ion Transport Properties
  Machine learning interatomic potentials (MLPs) are promising for accelerating simulation of ion transport in all-solid-state battery materials, but their accuracy across diverse material compositions and symmetries remains unquantified. Here, we systematically…
  crossref Ogheneyoma Aghoghovbia, Ming Hu, Adji Bousso Dieng 2025-12-12T03:29:44Z 置信度 0.70
• Decision letter for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref 2025-10-08T21:09:03Z 置信度 0.70
• Self-consistent biased fine-tuning for highly accurate reaction-specific machine-learning interatomic potentials
  An algorithm for the black-box generation of high-quality system-specific machinelearning interatomic potentials (MLIPs) for gas phase reactions in the electronic ground state is presented. It relies on the self-consistent fine-tuning of a MLIP foundation mode…
  crossref Julien Steffen 2026-04-15T06:03:31Z 置信度 0.70
• Decision letter for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref 2025-10-08T21:09:03Z 置信度 0.70
• High-Pressure Inelastic Neutron Spectroscopy: Experimental Validation of Machine-Learned Interatomic Potential Energy Landscapes
  Machine-learned interatomic potentials (MLIPs) promise near density-functional theory accuracy at a fraction of the computational cost, offering a route toward predictive atomistic modeling of molecular and condensed-phase materials. Yet their reliability beyo…
  crossref 2026-06-05T20:00:25Z 置信度 0.70
• Reactive Active Learning: An Efficient Approach for Training Machine Learning Interatomic Potentials for Reacting Systems
  Discovering chemical reaction pathways using quantum mechanics is impractical for many systems of practical interest because of unfavorable scaling and computational cost. While machine learning interatomic potentials (MLIPs) trained on quantum mechanical data…
  crossref 2025-09-03T11:21:11Z 置信度 0.70
• Benchmarking Universal Machine Learning Interatomic Potentials for Alkali-Ion Battery Kinetics
  crossref 2026-05-28T14:00:26Z 置信度 0.70
• Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe
  Abstract To advance the development of high-strength polycrystalline metallic materials towards achieving carbon neutrality, it is essential to design materials in which the atomic level control of general grain boundaries (GGBs), which govern the material pro…
  crossref Kazuma Ito, Tatsuya Yokoi, Katsutoshi Hyodo, Hideki Mori 2024-11-13T05:56:02Z 置信度 0.70
• Learning Long-Range Interactions in Equivariant Machine Learning Interatomic Potentials via Electronic Degrees of Freedom
  Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-…
  crossref 2025-08-26T11:10:48Z 置信度 0.70
• Unifying Quantum Materials Modeling and Experiments: The Role of Machine Learning Interatomic Potentials
  Computational experiments have emerged as a powerful complement to traditional experiments in the design of new materials. The development of machine learning (ML) and deep learning techniques, combined with database construction and data mining, has significa…
  crossref Wissam Saidi 2025-02-23T03:09:29Z 置信度 0.70
• Decision letter for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref 2024-09-27T00:02:30Z 置信度 0.70
• Review 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
• Review for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref 2025-10-08T21:09:03Z 置信度 0.70
• Decision letter for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref 2025-10-08T21:09:03Z 置信度 0.70
• Computing Anharmonic Free Energies in Solids with Machine-Learning Interatomic Potentials
  crossref 2026-05-30T14:03:00Z 置信度 0.70
• Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials
  Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near-quantum-level accuracy with reduced computational costs. However, the high cost of assembling high-fidelity databas…
  crossref 2024-12-17T10:09:16Z 置信度 0.70
• Decision letter for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref 2024-09-27T00:02:30Z 置信度 0.70
• Maximizing Machine Learning Interatomic Potential Transferability for the Discovery of the Novel Stellated Octadecagon Bi18-Pt24 Cage Structure
  Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investi- gate data selection strategie…
  crossref Nguyen-Thi Van-Oanh, Raphaël Vangheluwe, Minh-Tue Truong, Dominik Domin 等 2025-12-23T22:01:09Z 置信度 0.70
• Review 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
• Machine-learning interatomic potential for radiation damage and defects in tungsten
  Tungsten will be used as a plasma-facing material in fusion power reactors, where the absorption of high-energy neutrons leads to permanent damage in the crystal structure. A comprehensive understanding of the atom-level damage in tungsten has been limited by …
  crossref J. Byggmästar, A. Hamedani, K. Nordlund, F. Djurabekova 2019-10-17T10:49:35Z 置信度 0.70
• GENERATING ANALYTICAL FUNCTION FOR INTERATOMIC POTENTIAL USING MACHINE LEARNING APPROACH
  crossref Denis Svirin, Dmitriy Bazhanov 2024-10-23T18:07:46Z 置信度 0.70
• Machine learning interatomic potential reveals hydrogen embrittlement origins at general grain boundaries in α-iron
  Abstract Hydrogen embrittlement accompanied by cracking along general grain boundaries (GBs), which are characterized by a lack of crystallographic symmetry, is a persistent challenge in developing high-strength structural alloys. We develop a highly accurate …
  crossref Kazuma Ito, Takashi Otaki, Tatsuya Yokoi, Katsutoshi Hyodo 等 2025-12-26T11:14:58Z 置信度 0.70
• Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys
  For decades, atomistic simulation of chemically complex Ni‐based superalloys has remained beyond practical reach. Here, we apply the GRACE foundational machine‐learning interatomic potential to predict chemical ordering and stacking‐fault energetics in the and…
  crossref Aditya Vishwakarma, Sarath Menon, Fritz Körmann, Thomas Hammerschmidt 等 2026-07-21T17:36:13Z 置信度 0.70
• Learning Pairwise Interaction for Extrapolative and Interpretable Machine Learning Interatomic Potentials with Physics-Informed Neural Network
  Achieving both robust extrapolation and physical interpretability in machine learning interatomic potentials (ML-IPs) for atomistic simulation remains a significant challenge, particularly in data-scarce areas such as chemical reactions or complex, multicompon…
  crossref 2025-04-14T04:40:11Z 置信度 0.70
• Molecular dynamics study on magnesium hydride nanoclusters with machine-learning interatomic potential
  We introduce a machine-learning (ML) interatomic potential for Mg-H system based on Behler-Parrinello approach. In order to fit the complex bonding conditions in the cluster structure, we combine multiple sampling strategies to obtain training samples that con…
  crossref Ning Wang, Shiping Huang 2020-09-30T15:35:06Z 置信度 0.70
• Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2
  crossref 2026-02-24T15:50:42Z 置信度 0.70
• Machine learning interatomic potential for Li2TiO3 with comprehensive validation of lattice defect stability
  crossref Donggyu Lee, Takuji Oda 2026-07-03T14:13:01Z 置信度 0.70
• Review for "ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials"
  crossref 2024-11-02T08:01:41Z 置信度 0.70
• Review 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 "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
• Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials
  crossref 2025-12-26T18:31:10Z 置信度 0.70
• Review 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
• Review 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 "ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials"
  crossref 2024-11-02T08:01:41Z 置信度 0.70
• Review 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
• Review for "ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials"
  crossref 2024-11-02T08:01:41Z 置信度 0.70
• Decision letter for "Applicability of universal machine learning interatomic potentials to the simulation of steels"
  crossref 2025-10-22T01:13:00Z 置信度 0.70
• Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis
  crossref 2026-02-18T08:30:19Z 置信度 0.70
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
  Abstract Interatomic potentials (IAPs), which describe the potential energy surface of a collection of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applic…
  preprints Chi Chen, Shyue Ong 2022 置信度 0.74
• Review 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
• A Charge-Consistent Long-Range Machine-Learned Interatomic Potential Compatible with Quantum Linear System Algorithms
  Charge equilibration in machine-learning interatomic potentials (MLIPs) can be formulated as a linear-system problem, making it a promising target for quantum linear-system algorithms (QLSAs). Accurate charge equilibration is also essential for describing non-…
  crossref Da Bean Han, Hyun Woo Kim 2026-06-18T12:33:51Z 置信度 0.70
• MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials
  We present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and diso…
  crossref 2025-07-31T13:21:16Z 置信度 0.70
• Decision letter for "Applicability of universal machine learning interatomic potentials to the simulation of steels"
  crossref 2025-10-22T01:13:00Z 置信度 0.70
• Review for "ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials"
  crossref 2024-11-02T08:01:41Z 置信度 0.70