Machine Learning Interatomic Potentials

Also known as MLIP

Ask about this topic

Momentum

5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28
View on map

Latest papers 55

All topics
CardsList

  1. 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

  2. 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

  3. 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

  4. 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

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

    Sep 30, 2026Jörg Behler, Michele Ceriotti, Cecilia Clementi +46Machine Learning Interatomic PotentialsScientific ML

  6. 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.

  7. 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

  8. 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

  9. Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

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

    Independent researcher, Seoul, Republic of Korea

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. 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

  20. Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials

    Jul 2, 2026Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng +4Machine Learning Interatomic PotentialsNeural Network Optimization

    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA · Robert Bosch LLC Research and Technology Center, Watertown, MA, USA

  21. Spin-Weighted Spherical Harmonics Enable Complete and Scalable E(3)\mathrm{E}(3)-Equivariant Networks

    Jul 1, 2026Chenxing Liang, Yuchao Lin, Andrii Kryvenko +5Machine Learning Interatomic PotentialsEquivariant Neural Networks

    Department of Computer Science and Engineering, Texas A&M University · Lambda, Inc. · Department of Materials Science and Engineering, Texas A&M University +3

  22. Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases

    Jul 1, 2026Weiliang Luo, Heather J. KulikMachine Learning Interatomic PotentialsQuantum Chemistry

    Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

  23. Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials

    Jun 28, 2026Can Polat, Erchin Serpedin, Mustafa Kurban +1Machine Learning Interatomic PotentialsEquivariant GNNs

    Dept. of Electrical & Computer Eng. Texas A&M Univ. College Station, TX, USA · Dept. of Prosthetics & Orthotics College of Science & Eng. Ankara Univ., Ankara, Turkey · Hamad Bin Khalifa Univ. and Texas A&M Univ. at Qatar Doha, Qatar

  24. ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential

    Jun 23, 2026Linying Zhang, Julija ZavadlavMachine Learning Interatomic PotentialsMolecular Property Prediction

    Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Germany · Atomistic Modeling Center (AMC), Munich Data Science Institute (MDSI), Technical University of Munich, Germany

  25. Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning

    Jun 17, 2026Youngwoo Cho, Seunghoon Yi, Wooil Yang +6Machine Learning Interatomic PotentialsEquivariant Neural Networks

    KAIST · Seoul National Univ. · KIAS +2

  26. Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials

    Jun 14, 2026Jia Bi, Alin Marin Elena, Samuel PinillaMachine Learning Interatomic PotentialsEquivariant GNNs

    Science and Technology Facilities Council, Harwell Campus, Didcot, OX11 0QX, Oxfordshire, United Kingdom. · Science and Technology Facilities Council, Keckwick Lane, Daresbury, WA4 4AD, Cheshire, United Kingdom. · Diamond Light Source, Harwell Science and Innovation Campus,2026 Didcot, OX11 0QX, Oxfordshire, United Kingdom.

  27. Distilling latent electrostatics from foundation machine learning interatomic potentials

    Jun 12, 2026Xiaoyu Wang, Bingqing ChengMachine Learning Interatomic PotentialsMaterials Property Prediction

    Department of Chemistry, UC Berkeley, California 94720, United States · Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States · Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, United States

  28. Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction

    Jun 8, 2026Limin YuMachine Learning Interatomic PotentialsMulti-Scale Feature Fusion

    School of Medical Imaging, Division of Medical Technology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300203, China

  29. Inverse design of bespoke interatomic potentials via active learning by information-matching

    Jun 6, 2026Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6Uncertainty QuantificationMachine Learning Interatomic Potentials

    Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA · Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA. · Lawrence Livermore National Laboratory, Livermore, CA, USA +7

  30. Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

    Jun 2, 2026Joanna Zou, Fraser Birks, Dallas Foster +1Machine Learning Interatomic PotentialsActive Learning

    Center for Computational Science & Engineering, Schwarzman College of Computing, MIT · Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick · NVIDIA