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• Machine learning interatomic potential enhanced thermochemistry predictions across organic small compound space
  This Figshare deposit contains the training datasets and model checkpoints from the sixth active-learning iteration of our universal molecular thermochemistry GNN, as well as the DA and HAT benchmark filters used in the paper.
  datacite Bowen Deng, Thijs Stuyver 2026 置信度 0.66 Computational chemistry
• Machine learning interatomic potential enhanced thermochemistry predictions across organic small compound space
  This Figshare deposit contains the training datasets and model checkpoints from the sixth active-learning iteration of our universal molecular thermochemistry GNN, as well as the DA and HAT benchmark filters used in the paper.
  datacite Bowen Deng, Thijs Stuyver 2026 置信度 0.66 Computational chemistry
• Machine learning interatomic potential enhanced thermochemistry predictions across organic small compound space
  This Figshare deposit contains the training datasets and model checkpoints from the sixth active-learning iteration of our universal molecular thermochemistry GNN, as well as the DA and HAT benchmark filters used in the paper.
  datacite Bowen Deng, Thijs Stuyver 2026 置信度 0.66 Computational chemistry
• Water machine learning interatomic potentials: Kernel-regression and MACE models for different density functionals
  This repository contains reference datasets and trained machine-learning interatomic potentials for atomistic simulations of water and ice. The models represent several density-functional-theory approximations and were developed to enable systematic comparison…
  datacite Montero de Hijes, Pablo, Neubeck, Leon, Kresse, Georg, Dellago, Christoph 等 2026 置信度 0.66 machine learning interatomic potentials
• Water machine learning interatomic potentials: Kernel-regression and MACE models for different density functionals
  This repository contains reference datasets and trained machine-learning interatomic potentials for atomistic simulations of water and ice. The models represent several density-functional-theory approximations and were developed to enable systematic comparison…
  datacite Montero de Hijes, Pablo, Neubeck, Leon, Kresse, Georg, Dellago, Christoph 等 2026 置信度 0.66 machine learning interatomic potentials
• AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations
  Machine-learning interatomic potentials (MLIPs) bridge the accuracy of first-principles calculations and the efficiency required for large-scale molecular dynamics (MD) simulations. However, existing MLIP software remains fragmented across different model arch…
  datacite Liu, Hanyu, Zhu, Linggang, Zhang, Xuanguang, Yang, Ning 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Precipitate phase selection and grain boundary morphology in Cu-Ni-Si-Mn alloys: A machine-learning interatomic potential study
  Alloys inevitably contain interphase boundaries, whose energetics govern nucleation processes and precipitate morphology. In Cu-Ni-Si alloys, Mn addition markedly changes grain boundary (GB) precipitation behavior. While GB precipitation of stable Ni$_2$Si in …
  datacite Wani, Aadil Fayaz, Jeong, Il-Seok, Jeon, Haekwan, Kim, Jaesun 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces
  Accurate and efficient modeling of magnetic potential energy surfaces remains challenging because spin-polarized first-principles calculations for diverse non-collinear spin-lattice configurations are computationally demanding. Here we introduce the Spin Tenso…
  datacite Gao, Yuanqing, Luo, Wen-Hao, Zhang, Lei, Cao, Kun 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Data-driven learning and prediction of inorganic crystal structures.
  Crystal structure prediction algorithms, including ab initio random structure searching (AIRSS), are intrinsically limited by the huge computational cost of the underlying quantum-mechanical methods. We have recently shown that a novel class of machine learnin…
  datacite Deringer, Volker L, Proserpio, Davide M, Csányi, Gábor, Pickard, Chris J 2018 置信度 0.66 34 Chemical SciencesMachine Learning and Artificial IntelligenceNetworking and Information Technology R&D (NITRD)
• Machine-checked, energy-conserving delta-correction of a spin-blind foundation MLIP at a magnetic Fe-vacancy: reaction-coordinate generalization and vibrational consequences
  Initial public release accompanying the manuscript Machine-checked, energy-conserving delta-correction of a spin-blind foundation MLIP at a magnetic Fe-vacancy. Contents: delta-GPR force-correction harness + LOGO cross-validation (delta/), foundation-MLIP driv…
  datacite Morozov, Igor N. 2026 置信度 0.66 foundation machine-learning potentialsMACE-OMat24delta learningGaussian process regressionspin-blind interatomic potential
• Machine-checked, energy-conserving delta-correction of a spin-blind foundation MLIP at a magnetic Fe-vacancy: reaction-coordinate generalization and vibrational consequences
  Initial public release accompanying the manuscript Machine-checked, energy-conserving delta-correction of a spin-blind foundation MLIP at a magnetic Fe-vacancy. Contents: delta-GPR force-correction harness + LOGO cross-validation (delta/), foundation-MLIP driv…
  datacite Morozov, Igor N. 2026 置信度 0.66 foundation machine-learning potentialsMACE-OMat24delta learningGaussian process regressionspin-blind interatomic potential
• Circumventing the miscibility gap: kinetic pathways to ensure true bandage tunability in metal halide perovskites
  Mixed-halide metal halide perovskites offer tunable bandgaps and are promising candidates for photovoltaic applications. However, the practical use of formamidinium (FA)-based mixed-halide perovskites has been limited by the presence of a compositional miscibi…
  datacite Choudhary, SF 2026 置信度 0.66
• Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials
  The formation of extended sulfur vacancies in MoS2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond-scale molecular dynamics simulations using machine learning interatomic potentials (ML…
  datacite Flötotto, Aaron, Spetzler, Benjamin, von Stackelberg, Rose, Ziegler, Martin 等 2025 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Self-organized defect clustering and concentration-dependent vacancy diffusion in MoS$_2$
  Sulfur vacancy migration has a crucial impact on electronic transport and the functional behavior of MoS$_2$-based devices such as memristors and memtransistors. According to recent atomistic simulations, vacancy migration proceeds via cooperative, vacancy-ass…
  datacite Flötotto, Aaron, Spetzler, Benjamin, Ziegler, Martin, Runge, Erich 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• COMPUTATIONAL STUDY OF ION CONDUCTION IN LITHIUM LANTHANUM TITANATE SOLID-STATE ELECTROLYTES
  Ceramic-type solid-state electrolytes such as lithium lanthanum titanate (LLTO) are actively investigated for their high ionic conductivity and thermal stability. However, their ionic conductivity still falls short of that of commercial liquid electrolytes, mo…
  datacite Wang, Jiacheng 2026 置信度 0.66
• Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
  The characterization of nanostructued materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformations. Global optimization approaches allow predicting stable structures for targete…
  datacite Bjørk Hammer, Mads-Peter Christiansen, Albert Bruix, Jon Quinlivan Domínguez 等 2026 置信度 0.66 Chemical sciences
• Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
  The characterization of nanostructued materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformations. Global optimization approaches allow predicting stable structures for targete…
  datacite Bjørk Hammer, Mads-Peter Christiansen, Albert Bruix, Jon Quinlivan Domínguez 等 2026 置信度 0.66 Chemical sciences
• A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation
  Gallium oxide (Ga₂O₃) is a wide-bandgap semiconductor with promising applications in high-power and high-frequency electronics. However, its complex polymorphic nature poses substantial challenges for fundamental studies, particularly in understanding phase-tr…
  datacite Lijun Xu, Linyang Jiang, Yaohui Gu, Haizhou Xue 等 2026 置信度 0.66 Physical sciences
• A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation
  Gallium oxide (Ga₂O₃) is a wide-bandgap semiconductor with promising applications in high-power and high-frequency electronics. However, its complex polymorphic nature poses substantial challenges for fundamental studies, particularly in understanding phase-tr…
  datacite Lijun Xu, Linyang Jiang, Yaohui Gu, Haizhou Xue 等 2026 置信度 0.66 Physical sciences
• XMCQDPT2-Fidelity Transfer-Learning Potentials and a Wavepacket Oscillation Model with Power-Law Decay for Ultrafast Photodynamics
  A central pursuit in theoretical chemistry is the accurate simulation of photochemical reactions, which are governed by nonadiabatic transitions through conical intersections. Machine learning has emerged as a transformative tool for constructing the necessary…
  datacite Dudakov, Ivan V., Radzikovitsky, Pavel M., Popov, Dmitry S., Firsov, Denis A. 等 2025 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciences
• Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity
  Aromatic organic solutes in water exhibit a delicate balance between hydrophobic solvation and directional O-H$\cdots π$ hydrogen bonds, yet widely used force fields and state-of-the-art density functional approaches struggle to provide a consistent picture of…
  datacite Stolte, Nore, Forbert, Harald, Lysogorskiy, Yury, Drautz, Ralf 等 2026 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciences
• AI-Driven expansion and application of the Alexandria database
  We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV atom-1 of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Ma…
  datacite Cavignac, Théo, Schmidt, Jonathan, De Breuck, Pierre-Paul, Loew, Antoine 等 2026 置信度 0.66 DFThigh-throughputthermodynamicsmaterialsphase diagram
• Adaptive-precision interatomic potentials for large-scale atomistic simulations
  Large-scale atomistic simulations rely on interatomic potentials providing an efficient representation of atomic energies and forces. Modern machine-learning (ML) potentials provide the most precise representation compared to electronic structure calculations …
  datacite Immel, David 2026 置信度 0.66
• Data for "Machine Learning Driven Simulations of Hyperthermal Atomic Oxygen Impacts on (0001) Al2O3 for Low Altitude Satellite Design"
  This repository contains the data for the paper "Machine Learning Driven Simulations of Hyperthermal Atomic Oxygen Impacts on (0001) Al2O3 for Low Altitude Satellite Design".<br> This includes both the simulation results as well as workflows and input fi…
  datacite Segreto, Nico, Kästner, Johannes, Boskovice, Jovan, Beck, Andrea 2026 置信度 0.66 ChemistryComputer and Information SciencePhysicsMolecular DynamicsMachine Learned Interatomic Potentials
• Data-efficient and fast machine learning molecular dynamics through integrated active learning and knowledge distillation
  We develop data-efficient machine learning interatomic potentials (MLIPs) for fast molecular dynamics simulations combining DeePMD and MACE models within an active learning and knowledge distillation framework. Using liquid water as a case study, we first inde…
  datacite Lian, Xiliang, Pasquarello, Alfredo 2026 置信度 0.66 machine learning interatomic potentialknowledge distillationactive learningDeePMDMACE
• Data-efficient and fast machine learning molecular dynamics through integrated active learning and knowledge distillation
  We develop data-efficient machine learning interatomic potentials (MLIPs) for fast molecular dynamics simulations combining DeePMD and MACE models within an active learning and knowledge distillation framework. Using liquid water as a case study, we first inde…
  datacite Lian, Xiliang, Pasquarello, Alfredo 2026 置信度 0.66 machine learning interatomic potentialknowledge distillationactive learningDeePMDMACE
• Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials
  Phonons, quantized vibrations of the atomic lattice, are fundamental to understanding thermal transport, structural stability, and phase behavior in crystalline solids. Despite advances in computational materials science, most predictions of vibrational proper…
  datacite Lee, Huiju, Li, Zhi, he, Jiangang, Xia, Yi 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Tiling decomposition multiplicity predicts stability of GaN(0001) surface reconstructions
  The stable adatom configurations of a semiconductor surface have traditionally been sought by sampling: density functional theory (DFT) energies steer a heuristic or Bayesian search through a configuration space far too large to cover. Here we show that, for t…
  datacite Kuboyama, Tetsuji, Kusaba, Akira, Kawka, Karol, Kempisty, Pawel 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy
  Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT). However, inference time of MLPs is orders of magnitude slower than that of classi…
  datacite Eckwert, Jan, Zavadlav, Julija 2026 置信度 0.66 Chemical Physics (physics.chem-ph)Machine Learning (cs.LG)Biomolecules (q-bio.BM)FOS: Physical sciencesFOS: Computer and information sciences
• Fine tuning machine learning potentials for searching the structures of charge-neutral Cu, Ag, and Au clusters
  The structure of a cluster plays a decisive role in determining its physical and chemical properties. However, as cluster size increases, the number of possible isomers grows exponentially, and first-principles calculations become computationally demanding, po…
  datacite Panpan Lun, Fan Zhang, Weiwei Gao, Jijun Zhao 2026 置信度 0.66 Environmental Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedScience Policy
• Fine tuning machine learning potentials for searching the structures of charge-neutral Cu, Ag, and Au clusters
  The structure of a cluster plays a decisive role in determining its physical and chemical properties. However, as cluster size increases, the number of possible isomers grows exponentially, and first-principles calculations become computationally demanding, po…
  datacite Panpan Lun, Fan Zhang, Weiwei Gao, Jijun Zhao 2026 置信度 0.66 Environmental Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedScience Policy
• A deep insight into electronic and ionic transport properties of solid-state sodium electrolyte Na3SbSe4
  Low electronic and high ionic conductivities are indispensable for viable solid-state electrolytes (SSEs), being the key component of all-solid-state batteries. Here, we provide a deep atomistic insight into electronic and ionic transport properties of antimon…
  datacite Suk-Gyong Hwang, Chol-Jun Yu, Tae-Il Ri, Il-Jin Kim 等 2026 置信度 0.66 Chemical sciences
• A deep insight into electronic and ionic transport properties of solid-state sodium electrolyte Na3SbSe4
  Low electronic and high ionic conductivities are indispensable for viable solid-state electrolytes (SSEs), being the key component of all-solid-state batteries. Here, we provide a deep atomistic insight into electronic and ionic transport properties of antimon…
  datacite Suk-Gyong Hwang, Chol-Jun Yu, Tae-Il Ri, Il-Jin Kim 等 2026 置信度 0.66 Chemical sciences
• 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. The MLIP was generated by fitting with a neural network method a large database of energi…
  datacite Abou El Kheir, Omar 2026 置信度 0.66 machine learninginteratomic potentialdatabase
• 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. The MLIP was generated by fitting with a neural network method a large database of energi…
  datacite Abou El Kheir, Omar 2026 置信度 0.66 machine learninginteratomic potentialdatabase
• MLIP Studio: An Open Platform for Interactive Benchmarking and Atomistic Simulations Using Machine Learning Interatomic Potentials
  Universal machine learning interatomic potentials (MLIPs) are foundation AI models transforming atomistic simulations, but their practical use remains hindered by fragmented software ecosystems, dependency conflicts, and the lack of accessible benchmarking too…
  datacite Sharma, Manas, Punnathanam, Sudeep, Rajan, Ananth Govind 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Machine-learned interatomic potential for titanium carbide MXenes: Application to ion irradiation simulations
  A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for bare Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates good accuracy and …
  datacite Byggmästar, Jesper 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• Improved capabilities of the TurboGAP code for radiation induced cascade simulations: an illustration with silicon
  TurboGAP is a software package designed for efficient molecular dynamics simulations using Gaussian Approximation Potential (GAP) machine-learning interatomic potentials (MLIP). In this work, we enhance the capabilities of TurboGAP for radiation damage simulat…
  datacite Saha, Uttiyoarnab, Hamedani, Ali, Caro, Miguel A., Sand, Andrea E. 2025 置信度 0.66 Applied Physics (physics.app-ph)FOS: Physical sciences
• Predicting homogeneous nucleation rate from atomistic simulations
  Predictive modelling and quantitative understanding of nucleation is essential for predicting phase transformation processes in nature and precisely controlling material synthesis and processing. Atomistic modeling is a powerful tool for capturing the dynamica…
  datacite Cheng, Bingqing 2019 置信度 0.66 nucleationphase transitioninterfacefree energy methodsmachine learning potential
• A high-accuracy machine-learning interatomic potential for modeling rhenium in tungsten
  Tungsten (W), the primary plasma-facing material in fusion reactors, undergoes neutron irradiation that induces transmutation to rhenium (Re). These Re impurities significantly alter microstructural evolution, thus it is necessary to study the atom-scale behav…
  crossref Dongdong Li, Tianyi Wang, Heng Chen, Qing An Li 等 2026-04-01T08:13:31Z 置信度 0.70
• Reproducibility Report for SC25 Paper TensorMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic Potential
  This reproducibility report provides details about the artifact evaluation done with regard to the Artifact Description and Evaluation appendix of the SC25 paper TensorMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic Pot…
  crossref Minh Chung 2025-11-12T16:05:39Z 置信度 0.70
• Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
  Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as “designer solvents” as they can be mixed to precisely tailor the physiochemical properties. As using ma…
  crossref 2024-07-18T11:30:56Z 置信度 0.70
• Potential correlation between thermal transport and catalytic performance in single metal atom catalysts: A machine-learning interatomic potential and density functional theory study
  crossref Yuxi Zhu, Bo Xu, Chaolin Han, Qiang Ma 等 2024-12-07T16:37:44Z 置信度 0.70
• An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-ion Battery Electrolytes in Solution
  Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at \textit{ab initio} level accuracy. One such process is the critical yet still poorly under…
  crossref Yujing Wei, John L. Weber, James M. Stevenson, Zachary K. Goldsmith 等 2025-10-16T13:01:57Z 置信度 0.70
• An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-ion Battery Electrolytes in Solution
  Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at \textit{ab initio} level accuracy. One such process is the critical yet still poorly under…
  crossref Yujing Wei, John L. Weber, James M. Stevenson, Zachary K. Goldsmith 等 2026-02-11T07:44:52Z 置信度 0.70
• 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 2025-04-25T04:30:21Z 置信度 0.70
• Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems
  Abstract Machine-Learning Interatomic Potentials (MLIPs) have surged in popularity due to their promise of expanding the spatiotemporal scales possible for simulating molecules with high fidelity. The accuracy of any MLIP is dependent on the data used for its …
  crossref Natalie Hooven, Arthur Lin, Rose Cersonsky 2025-10-27T12:46:09Z 置信度 0.70
• Navigating Compositional Space in Palladium Hydride: Benchmarking Machine Learning Interatomic Potentials
  Palladium plays a crucial role in advancing the hydrogen economy, serving in applications such as hydrogen purification, sensing, catalysis, and conversion. While machine learning approaches have shown great promise to accelerate first-principles calculations,…
  crossref Poonam Parkar, Sourabh Singha, Sudarshan Vijay, Abhijit Chatterjee 2026-07-20T04:54:42Z 置信度 0.70
• Decision letter for "Accelerating Explicit Solvent Models of Heterogeneous Catalysts with Machine Learning Interatomic Potentials"
  crossref 2023-07-13T17:00:54Z 置信度 0.70
• Supplementary data 1
  crossref 2026-03-10T09:26:39Z 置信度 0.70
• Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis
  Heterogeneous catalysts are crucial in modern societies as they promote sustainability by enabling lower-energy pathways for various chemical reactions. While Density Functional Theory (DFT) computations can provide critical insights into how heterogeneous cat…
  crossref Gbolagade Olajide, Khagendra Baral, Sophia Ezendu, Ademola Soyemi 等 2025-02-24T23:40:16Z 置信度 0.70
• Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
  Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hi…
  crossref 2024-03-20T08:10:16Z 置信度 0.70
• Author response for "Machine Learning Interatomic Potentials in Engineering Perspective for Developing Cathode Materials"
  crossref Dohyeong Kwon, Duho Kim 2024-07-30T17:13:50Z 置信度 0.70
• Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations
  $\rm{SO}(3)$-equivariant networks are the dominant models for machine learning interatomic potentials (MLIPs). The key operation of such networks is the Clebsch-Gordan (CG) tensor product, which is computationally expensive. To accelerate the computation, we d…
  crossref Yuchao Lin, Cong Fu, Zachary Krueger, Haiyang Yu 等 2026-08-06T14:44:29Z 置信度 0.70
• Assessment and Application of Universal Machine Learning Interatomic Potentials in Solid-State Electrolyte Research
  High-performance solid-state electrolytes (SSEs) are crucial for next-generation lithium batteries. However, conventional methods like density functional theory and empirical force fields face challenges in computational cost, scalability, and transferability …
  crossref 2025-09-09T11:50:23Z 置信度 0.70
• Integrating Machine Learning Interatomic Potentials with MMPBSA for Accurate ProteinLigand Binding Free Energy Calculations
  crossref 2026-05-04T19:00:51Z 置信度 0.70
• GB-Score: Minimally Designed Machine Learning Scoring Function Based on Distance-weighted Interatomic Contact Features
  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 a new sc…
  crossref Milad Rayka, Rohoullah Firouzi 2022-02-22T01:41:56Z 置信度 0.70
• Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials Surfaces
  Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecule…
  crossref 2024-07-11T13:31:54Z 置信度 0.70
• Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile
  Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable…
  crossref 2024-07-03T10:30:12Z 置信度 0.70
• Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning
  Abstract Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are consi…
  crossref Jiaqi Wang, Seungha Shin, Sangkeun Lee 2019-12-18T02:18:50Z 置信度 0.70
• Interatomic Potential Parametrization for Graphene
  crossref Kritesh Kumar Gupta, Sudip Dey, Tanmoy Mukhopadhyay 2025-10-30T18:09:12Z 置信度 0.70
• Self-Consistent Biased Fine-Tuning for Highly Accurate Reaction-Specific Machine-Learning Interatomic Potentials
  crossref 2026-08-11T15:26:16Z 置信度 0.70
• Accelerated modeling of interfacial phases in the Ni-Bi system with machine learning interatomic potential
  crossref Vadim Korolev, Artem Mitrofanov, Yaroslav Kucherinenko, Yurii Nevolin 等 2020-05-20T02:25:13Z 置信度 0.70
• Decision letter for "Accelerating Explicit Solvent Models of Heterogeneous Catalysts with Machine Learning Interatomic Potentials"
  crossref 2023-07-13T17:00:54Z 置信度 0.70
• Effect of Four-Phonon Scattering on Anisotropic Thermal Transport in Bulk Hexagonal Boron Nitride by Machine Learning Interatomic Potential
  crossref Jialin Tang, Guotai Li, Qi Wang, Jiongzhi Zheng 等 2022-12-18T03:08:34Z 置信度 0.70
• SchNet_IIA: Potential Energy Surface Fitting by Interatomic Interactions Attention Based on Transfer Learning Analysis
  Machine learning methods for fitting potential energy surfaces and molecular dynamics simulations are becoming increasingly popular due to their potentially high accuracy and savings in computational resources. However, existing application models often rely o…
  crossref 2024-12-30T10:10:12Z 置信度 0.70
• LiquidVapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials
  Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these proper…
  crossref 2025-01-13T01:52:16Z 置信度 0.70
• Review 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
• Unveiling the Atomic Scale Synthesis Dynamics of Lithium Thiophosphate Electrolytes via Machine Learning Interatomic Potentials
  The rational design of inorganic materials is often hindered by the complexity of solid-state synthesis, which remains largely empirical and reliant on trial-and-error optimization. Bridging the gap between atomistic mechanisms and macroscopic processing, we p…
  crossref Seonhye Park, Joonhee Kang 2026-06-23T13:37:02Z 置信度 0.70
• Universally Accurate or Specifically Inadequate? Stress-testing General Purpose Machine Learning Interatomic Potentials
  Machine learning interatomic potentials (MLIPs) have revolutionized the field of atomistic materials simulation, both due to their remarkable accuracy and their computational efficiency compared to established \textit{ab initio} methods. Very recently, several…
  crossref Konstantin Jakob, Karsten Reuter, Johannes T. Margraf 2025-03-04T07:41:46Z 置信度 0.70
• Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis
  crossref Gbolagade Olajide, Khagendra Baral, Sophia Ezendu, Ademola Soyemi 等 2025-03-06T08:39:19Z 置信度 0.70
• Kinetic Consequences of Quasi-Harmonic Entropies Calculated with Machine Learning Interatomic Potentials for Microkinetic Modeling
  Microporous catalysts are ubiquitous in chemical processes including sustainable transformations of biobased feedstocks into fuels and fine chemicals. The mechanistic insights needed to design next-generation microporous catalysts can be obtained with ab initi…
  crossref 2024-11-14T10:41:57Z 置信度 0.70
• Author response for "Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance"
  crossref Hyunsung Cho, Minseok Moon, Jaehoon Kim, Eunkyung Koh 等 2025-11-10T21:07:27Z 置信度 0.70
• Decision letter 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 "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
• Research on Machine Learning Interatomic Potentials for Titanium Oxide Ceramic Materials
  crossref Jiali Ren, Guanghao Zhang, Xuemei Wang, Ye Han 2025-02-08T16:42:18Z 置信度 0.70
• Accelerating Catalytic Reaction Network Exploration via Local Fine-tuning with Universal Machine Learning Interatomic Potentials
  crossref 2026-03-24T20:20:43Z 置信度 0.70
• Review 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
• Thermal transport properties of monolayer GeS and SnS: A comparative study based on machine learning and SW interatomic potential models
  Phonon transport properties of two-dimensional materials can play a crucial role in the thermal management of low-dimensional electronic devices and thermoelectric applications. In this study, both the empirical Stillinger–Weber (SW) and machine learning inter…
  crossref Wentao Li, Chenxiu Yang 2022-08-17T14:51:40Z 置信度 0.70
• Crystallization of h-BN by molecular dynamics simulation using a machine learning interatomic potential
  crossref Yu-Qi Liu, Hai-Kuan Dong, Ying Ren, Wei-Gang Zhang 等 2024-12-27T20:55:57Z 置信度 0.70
• Machine learning interatomic potentials for monolayer hexagonal boron nitride: Thermal transport and defect effects via deep potential molecular dynamics
  crossref Guanghao Zhang, Zicheng Wang, Caihua Shi, Ye Han 2025-12-17T23:15:02Z 置信度 0.70
• utils4VASP: Setup and Evaluation of Electronic Structure and Machine-Learned Interatomic Potential Simulations with VASP
  We present an open source collection of scripts and programs for the setup, management and evaluation of calculations with the Vienna ab-initio simulation package (VASP), called utils4VASP. It contains 20 independent Python scripts and Fortran programs, all wi…
  crossref Julien Steffen, Andreas Mölkner, Maximilian Bechtel 2025-06-13T04:48:47Z 置信度 0.70
• MLIPX: Machine Learned Interatomic Potential eXploration
  The rapid advancement in machine-learned interatomic potentials (MLIPs) and the proliferation of uni- versal MLIPs (uMLIPs) have significantly broadened their application scope. Community benchmarks and leaderboard rankings are frequently updated, providing st…
  crossref Fabian Zills, Sheena Agarwal, Tiago Goncalves, Srishti Gupta 等 2025-06-26T02:45:57Z 置信度 0.70
• Unveiling strain-dependent adhesion behavior at TaN-Cu interface using machine learning interatomic potential
  crossref Jinyoung Jeong, Jiwon Sun, Eunseog Cho, Kyoungmin Min 2025-01-27T16:55:15Z 置信度 0.70
• Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
  Many rotational invariants for crystal structure representations have been used to describe the structure-property relationship by machine learning. The machine learning interatomic potential (MLIP) is one of the applications of rotational invariants, which pr…
  crossref Atsuto Seko, Atsushi Togo, Isao Tanaka 2019-06-26T10:44:40Z 置信度 0.70
• Gold-Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential
  Mono-layer 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 synthe- sized and characterized in crystalline forms…
  crossref Caitlin McCandler, Antti Pihlajamäki, Sami Malola, Hannu Häkkinen 等 2024-03-07T05:20:13Z 置信度 0.70
• Review 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
• Author response for "Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance"
  crossref Hyunsung Cho, Minseok Moon, Jaehoon Kim, Eunkyung Koh 等 2025-11-10T21:07:27Z 置信度 0.70
• Decision letter 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 interatomic potential for finite-size graphene
  crossref Yan Zhang, Lifeng Wang, Zhuoqun Zheng 2025-09-23T00:00:12Z 置信度 0.70
• Probing Surface Degradation Pathways of Charged Nickel-Oxide Cathode Materials Using Machine-Learning Interatomic Potentials
  While nickel-based layered oxide cathodes offer promising energy and power densities in lithium-ion batteries, they suffer from instability when fully delithiated upon charge. Ex situ studies often report a structural degradation of the charged cathode materia…
  crossref 2025-09-25T07:30:44Z 置信度 0.70
• Kinetic Consequences of Quasi-Harmonic Entropies Calculated with Machine Learning Interatomic Potentials for Microkinetic Modeling
  Microporous catalysts are ubiquitous in chemical processes including sustainable transformations of biobased feedstocks into fuels and fine chemicals. The mechanistic insights needed to design next-generation microporous catalysts can be obtained with ab initi…
  crossref 2024-11-14T10:41:57Z 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
• Machine-Learned Interatomic Potential Insights into the Effect of H₂O on CO₂ Adsorption in HEU-Type Zeolites
  Abstract The rapid increase in atmospheric CO₂ concentrations remains a major driver of global climate change, making the development of high-performance carbon capture materials essential. These materials must be capable of operating efficiently under realist…
  crossref Anthony Pembere, Fred Sifuna 2026-04-22T10:37:42Z 置信度 0.70
• Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials
  crossref 2026-04-23T10:00:17Z 置信度 0.70
• How Long Is Long Enough? Extrapolation of Machine-Learning Interatomic Potentials for Oligomeric and Polymeric Systems
  Machine-learning interatomic potentials (MLIPs) have surged in popularity due to their promise of expanding the spatiotemporal scales possible for simulating molecules with high fidelity. The accuracy of any MLIP is dependent on the data used for its training;…
  crossref 2026-06-19T08:20:36Z 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
• Decision letter 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
• utils4VASP: Setup and Evaluation of Electronic Structure and Machine-Learned Interatomic Potential Simulations with VASP
  We present an open source collection of scripts and programs for the setup, management and evaluation of calculations with the Vienna ab-initio simulation package (VASP), called utils4VASP. It contains 20 independent Python scripts and Fortran programs, all wi…
  crossref Julien Steffen, Andreas Mölkner, Maximilian Bechtel 2025-10-08T05:55:11Z 置信度 0.70
• Benchmarking Universal Machine-Learning Interatomic Potentials for Accelerated Adsorption Energy Evaluation with DFT Single-Point Calculations
  crossref 2026-08-20T12:19:13Z 置信度 0.70