Machine Learning Interatomic Potentials

Also known as MLIP

Ask about this topic

Overview

Machine learning interatomic potentials approximate quantum-mechanical energies and forces from atomic structures, making molecular dynamics and materials simulations less computationally expensive. Common approaches include local descriptors, equivariant graph neural networks and transformers, with newer models adding long-range electrostatics, dispersion, and pretrained representations. Researchers use them to study molecules, reactions, crystals, interfaces, and materials at finite temperature, while active learning and uncertainty estimates help select useful training data and flag risky predictions. A central challenge is balancing speed with reliable transfer to unfamiliar structures and conditions: low benchmark errors do not necessarily guarantee accurate dynamics, thermodynamics, or out-of-distribution predictions.

Latest papers

CardsList

91 papers13 papers in the last four weeks, up 86% on the four weeks before. 0.1% of all new papers.

  1. Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling

    Oct 16, 2026Yicheng Chen, Xiaoyun Yu, Hyejeong Song +5Machine Learning Interatomic PotentialsComputational Materials Science

    Department of Materials Science and Engineering, National University of Singapore, Singapore · Department of Physics, National University of Singapore, Singapore

  2. Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials

    Oct 14, 2026Kun Ryu, Yeo Jin Jung, Shyue Ping Ong +2Selective PredictionComputational Materials Science

    Pritzker School of Molecular Engineering, The University of Chicago · Department of Statistics, The University of Chicago · Department of Material Science and Engineering, National University of Singapore

  3. LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs

    Oct 13, 2026Yuanchang Zhou, Mingzhen Li, Chen Wang +3Machine Learning Interatomic PotentialsGPU Kernel Optimization

    State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences

  4. Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?

    Oct 13, 2026Pavlo O. Dral, Yi-Fan Hou, Yuxinxin Chen +4Machine Learning Interatomic PotentialsTransfer Learning

    State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, Xiamen, Fujian 361005, China · Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in Toruń, ul. Grudziadzka 5, 87-100 Toruń, Poland · Institute of Advanced Studies, Nicolaus Copernicus University in Toruń, ul. Wileńska 4, 87-100 Toruń, Poland +1

  5. Solvating machine-learning interatomic potentials for solution-phase chemistry

    Oct 12, 2026Anton Z. Ni, Joonho LeeMachine Learning Interatomic PotentialsMolecular Property Prediction

    Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA

  6. On the Theoretical Limits of Learning Unconstrained Interatomic Potentials

    Oct 10, 2026Prajwal Ananth, Shuwen YueMachine Learning Interatomic PotentialsMolecular Dynamics Simulation

    Center for Applied Mathematics, Cornell University, Ithaca, NY · R. F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY

  7. Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks

    Oct 9, 2026Sena Kalabalik, Sukru Caglar, Abdullah Umut Hamzaogullari +1Lagrangian Neural NetworksMachine Learning Interatomic Potentials

    Department of Physics, Bo˘gazi¸ci University, Istanbul, Türkiye · Department of Computer Engineering, Bo˘gazi¸ci University, Istanbul, Türkiye

  8. NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

    Oct 7, 2026Jay L. Kaplan, Samuel Varner, Rebecca Willett +1Machine Learning Interatomic PotentialsEquivariant GNNs

    Courant Institute School of Mathematics, Computing, and Data Science, New York University, New York, NY 10012, USA · Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA · Department of Statistics, The University of Chicago, Chicago, IL 60637, USA +3

  9. Origins of Universal Machine Learning Force-Field Errors in Multicomponent Materials

    Oct 7, 2026Hongwei Du, Dingyang Lv, Baole Wei +11Machine Learning Interatomic PotentialsMaterials Science

    Zhongguancun Academy, Beijing 100094, China. · Zhongguancun Institute of Artificial Intelligence, Beijing 100094, China. · Kairos Materials, Beijing, 100094, China. +4

  10. FlashCart: Fast Cartesian Tensor Products for Equivariant Interatomic Potentials

    Oct 5, 2026Viktor Zaverkin, Payman Goodarzi, Sergey V. Sukhomlinov +4Machine Learning Interatomic PotentialsGPU Kernel Optimization

    INM – Leibniz Institute for New Materials · Saarland University · German Research Center for Artificial Intelligence (DFKI) +2

  11. Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling

    Oct 5, 2026Sam McCallum, Niklas Rindtorff, Alexander Tong +1Stochastic Differential EquationsMachine Learning Interatomic Potentials

    University of Bath · AITHYRA

  12. BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials

    Oct 1, 2026Laura Zichi, Gil Harari, Chuin Wei Tan +6Machine Learning Interatomic PotentialsEquivariant Neural Networks

    Harvard University · Robert Bosch LLC Research and Technology Center

  13. A strategic roadmap for an atomistic machine-learning ecosystem

    Sep 30, 2026Jörg Behler, Michele Ceriotti, Cecilia Clementi +47Machine Learning Interatomic PotentialsScientific ML

  14. SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials

    Sep 23, 2026Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan +7Machine Learning Interatomic PotentialsMaterials Science

    Department of Energy Conversion and Storage, Technical University of Denmark, Kongens Lyngby, Denmark. · Pioneer Center for Accelerating P2X Materials Discovery (CAPeX), Technical University of Denmark, Kongens Lyngby, Denmark.

  15. Truncated automatic sparse differentiation for machine learning interatomic potentials

    Sep 17, 2026Marcel F. Langer, Adrian Hill, Michele CeriottiAutomatic DifferentiationMachine Learning Interatomic Potentials

    Laboratory of Computational Science and Modeling (COSMO), EPFL, Lausanne, Switzerland · Machine Learning Group, Technical University of Berlin, Berlin, Germany · BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

  16. MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials

    Sep 8, 2026Ryuhei Okuno, Nontawat Charoenphakdee, Kaoru Hisama +1AI Agents for Scientific DiscoveryMachine Learning Interatomic Potentials

    Preferred Networks

  17. A budget-dependent crossover between coverage- and response-based training-set selection for machine-learned interatomic potentials

    Sep 5, 2026Jia Bi, Alin-Marin ElenaTraining Data SelectionData Selection

    Science and Technology Facilities Council, Harwell Campus, Didcot, United Kingdom. · Science and Technology Facilities Council, Keckwick Lane, Daresbury, United Kingdom.

  18. Hessian-Informed Conformation Augmentation for Potential Energy Surface Curvature in Machine Learning Interatomic Potentials

    Sep 4, 2026Bumju Kwak, Jeonghee JoData AugmentationMachine Learning Interatomic Potentials

    Independent researcher, Seoul, Republic of Korea

  19. Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials

    Sep 1, 2026Pingbing Ming, Han WangMachine Learning Interatomic PotentialsGNN Expressivity

    SKLMS, Institute of Computational Mathematics and Scientific/Engineering Computing, AMSS, Chinese Academy of Sciences, No. 55, East Road Zhong-Guan-Cun, Beijing 100190, China · National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, P.R. China, and HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P.R. China

  20. AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials

    Aug 31, 2026Prajwal Ananth, Shuwen YueUncertainty QuantificationMachine Learning Interatomic Potentials

    Center for Applied Mathematics, Cornell University, Ithaca, NY, USA · R. F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA

  21. First-Principles Atomistic Structure and Dynamics of Polyethylene During High-Pressure Radical Polymerization via Machine Learning Force Fields

    Aug 22, 2026Bharatha K. Gunawardana, Teresa Shah, Bicha Azizova +6Materials ScienceComputational Materials Science

    Department of Chemistry, University of North Texas, Denton, TX 76203, USA · Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA

  22. Universal Thermodynamic Interatomic Potentials for Crystalline Materials

    Aug 14, 2026Juno Nam, Bowen Deng, Xiaochen Du +3Computational Materials ScienceFree Energy Calculation

    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Fundamental AI Research, Meta, San Francisco, CA 94105, USA

  23. Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations

    Jul 31, 2026Johannes Maeß, Leon Werner, J. Thorben Frank +5Implicit Neural RepresentationsMachine Learning Interatomic Potentials

    BIFOLD, Berlin, Germany. · Machine Learning Group, TU Berlin, Berlin, Germany. · Department of Artificial Intelligence, Korea University, Seoul, South Korea. +1

  24. An Ontology for Machine Learning Interatomic Potentials

    Jul 25, 2026Daniel Hernández, Jong Hyun Jung, Yuji Ikeda +11Machine Learning Interatomic Potentials

    Institute for Artificial Intelligence, University of Stuttgart, Germany · Institute for Materials Science, University of Stuttgart, Germany · Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-Universität Bochum, Germany and Department for Computational Materials Design, Max-Planck-Institut for Sustainable Materials, Germany

  25. Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems

    Jul 23, 2026Xiao Zhu, Srinivasan S. IyengarMachine Learning Interatomic PotentialsEquivariant Neural Networks

    Department of Chemistry, Department of Physics, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, 800 E. Kirkwood Ave, Bloomington, IN-47405

  26. Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields

    Jul 16, 2026Sheng Bi, Yi-Ze Wang, Jun ChengUncertainty QuantificationMachine Learning Interatomic Potentials

    College of Materials, Xiamen University, Xiamen, China. · Laboratory of AI for Electrochemistry (AI4EC), IKKEM, Xiamen, China. · State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.

  27. Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy

    Jul 12, 2026Jan Eckwert, Julija ZavadlavMachine Learning Interatomic PotentialsMolecular Dynamics Simulation

    1Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design and Department of Physics, TUM School of Natural Sciences, Technical University of Munich, Munich, 80333, Germany. · 2Atomistic Modeling Center, Munich Data Science Institute, Technical University of Munich, Garching, 85748, Germany.

  28. Edge Cluster Expansion with Radial Rotary Attention for Interatomic Potentials

    Jul 12, 2026Zemin Xu, Wenbo Xie, P. HuMachine Learning Interatomic PotentialsEquivariant Neural Networks

    School of Physical Science and Technology, ShanghaiTech University, Shanghai, China · School of Chemistry, Nanjing University, Nanjing, China

  29. Active rejection enables reliable generalization of universal machine-learning interatomic potentials

    Jul 10, 2026Mingxiang Luo, Xinnan Mao, Lu Wang +3Machine Learning Interatomic PotentialsSelective Prediction

    School of Future Information Innovation, Fudan University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China · Suzhou Laboratory, Suzhou, China +1

  30. EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation

    Jul 6, 2026Samuel Sahel-Schackis, Ken-ichi Nomura, Aiichiro Nakano +2Machine Learning Interatomic PotentialsEquivariant Neural Networks

    Department of Physics, Stanford University · Collaboratory for Advanced Computing and Simulation, University of Southern California · Linac Coherent Light Source, SLAC National Accelerator Laboratory +1