Interatomic Potentials

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7 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

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

    Oct 5, 2026Viktor Zaverkin, Payman Goodarzi, Sergey V. Sukhomlinov +4Interatomic PotentialsHigh-Order Correlations

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

  2. BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials

    Oct 1, 2026Laura Zichi, Gil Harari, Chuin Wei Tan +6Interatomic PotentialsMolecular Dynamics

    Harvard University · Robert Bosch LLC Research and Technology Center

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

    Sep 23, 2026Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan +7Interatomic PotentialsMolecular Dynamics

    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.

  4. iSDFT: Information-Proximal Self-Distillation for Continual Learning in LLMs

    Sep 21, 2026Ahmed Khaled Khamis, Xiaotong Ji, Hassan Jaber +4Self-Distillation FrameworkSelf-Teacher

    Imperial College London · Politecnico di Torino · UCL Centre for AI

  5. Truncated automatic sparse differentiation for machine learning interatomic potentials

    Sep 17, 2026Marcel F. Langer, Adrian Hill, Michele CeriottiInteratomic PotentialsMolecular Dynamics

    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

  6. El Agente Potente: High-Throughput Agentic Atomistic Simulations

    Sep 13, 2026Tsz Wai Ko, Jiaru Bai, Thomas Swanick +6Interatomic PotentialsMolecular Dynamics

    Department of Chemistry, University of Toronto, 80 St. George St., Toronto, ON M5S 3H6, Canada · 2Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON M5G 0C6, Canada · Department of Computer Science, University of Toronto, 40 St George St., Toronto, ON M5S 2E4, Canada +7

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

    Sep 8, 2026Ryuhei Okuno, Nontawat Charoenphakdee, Kaoru Hisama +1Interatomic PotentialsLatent Failure Patterns

    Preferred Networks

  8. Equivariance Breaks the Learning Rate

    Sep 8, 2026Andrei Manolache, Mathias NiepertAdamEquivariant Neural Networks

    University of Stuttgart, Germany · Bitdefender, Romania

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

    Sep 4, 2026Bumju Kwak, Jeonghee JoInteratomic PotentialsConformational Ensembles

    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 WangInteratomic PotentialsMessage Passing Neural Networks

    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 YueInteratomic PotentialsActive Learning

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

  12. Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation

    Jul 30, 2026Paul Hagemann, Katharina Ueltzen, Simon Müller +2Crystal StructureInteratomic Potentials

  13. An Ontology for Machine Learning Interatomic Potentials

    Jul 25, 2026Daniel Hernández, Jong Hyun Jung, Yuji Ikeda +11Interatomic PotentialsDensity Functional Theory

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

    Jul 12, 2026Jan Eckwert, Julija ZavadlavTransferable Implicit Solvent Machine Learning PotentialInteratomic Potentials

    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.

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

    Jul 12, 2026Zemin Xu, Wenbo Xie, P. HuInteratomic PotentialsLie Group

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

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

    Jul 10, 2026Mingxiang Luo, Xinnan Mao, Lu Wang +3Interatomic PotentialsMolecular Dynamics

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

  17. Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates

    Jul 8, 2026Jianing Liu, Dong H. ZhangNewtonGeodesic

    State Key Laboratory of Chemical Reaction Dynamics and Department of Chemical Physics, University of Science and Technology of China, Hefei, 230026, China · State Key Laboratory of Chemical Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China. · University of Chinese Academy of Sciences, Beijing, 100049, China +1

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

    Jul 2, 2026Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng +4Interatomic PotentialsAdam

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

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

    Jun 28, 2026Can Polat, Erchin Serpedin, Mustafa Kurban +1Interatomic PotentialsCosine Similarity

    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

  20. SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery

    Jun 21, 2026Bin CaoCrystalData-Driven Materials Discovery

    The Hong Kong University of Science and Technology (Guangzhou), China · Huawei Noah’s Ark Lab, UK

  21. LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms

    Jun 17, 2026Masaya Hagai, Yuta Suzuki, Tomoya Murata +2Quantum ChemistryData-Driven Materials Discovery

    Lattice Lab, Toyota Motor Corporation · Department of Chemistry, Graduate School of Science, Nagoya University · National Institute of Informatics

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

    Jun 17, 2026Youngwoo Cho, Seunghoon Yi, Wooil Yang +6Interatomic PotentialsMaterials

    KAIST · Seoul National Univ. · KIAS +2

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

    Jun 14, 2026Jia Bi, Alin Marin Elena, Samuel PinillaInteratomic PotentialsMolecular Dynamics

    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.

  24. Distilling latent electrostatics from foundation machine learning interatomic potentials

    Jun 12, 2026Xiaoyu Wang, Bingqing ChengInteratomic PotentialsDensity Functional Theory

    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

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

    Jun 6, 2026Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6Interatomic PotentialsMolecular Dynamics

    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

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

    Jun 2, 2026Joanna Zou, Fraser Birks, Dallas Foster +1Interatomic PotentialsMolecular Dynamics

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

  27. MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials

    May 29, 2026Etinosa Osaro, Santosh Adhikari, Stamatia Zavitsanou +2Interatomic PotentialsPilot

    PsiQuantum, 700 Hansen Way, Palo Alto, CA 94304

  28. Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs

    May 22, 2026Ihor Neporozhnii, Sjoerd Hoogland, Oleksandr VoznyyInteratomic PotentialsQuantum Chemistry

    Department of Physical and Environmental Sciences, University of Toronto, 1065 Military Trail, Toronto, M1C 1A4, Ontario, Canada. · The Alliance for AI-Accelerated Materials Discovery, 10 King’s College Road, Toronto, M5S 3G4, Ontario, Canada. · Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, M5S 3G4, Ontario, Canada.

  29. TriForces: Augmenting Atomistic GNNs for Transferable Representations

    May 20, 2026Ali Ramlaoui, Alexandre Duval, Hannah Bull +4Interatomic PotentialsDensity Functional Theory

    Entalpic, Paris, France · Universit´e Paris-Saclay, CentraleSup´elec, Inria, Gif-sur-Yvette, France

  30. Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building

    May 15, 2026Sauradeep Majumdar, Miguel Steiner, Johannes C. B. Dietschreit +4Interatomic PotentialsFree Energy Principle

    Department of Material Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States. · Institute of Theoretical Chemistry, University of Vienna, Währinger Str. 17, 1090, Vienna, Austria. · Department of Physical and Macromolecular Chemistry, Charles University, Hlavova 8, Praha 2, 12800, Czech Republic.

  31. Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows

    May 14, 2026Wenwen Li, Yuki Orimo, Nontawat CharoenphakdeeInteratomic PotentialsMolecular Dynamics

    Preferred Networks, Inc., Tokyo, Japan.

  32. Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs

    May 13, 2026Eszter Varga-Umbrich, Zachary Weller-Davies, Paul Duckworth +3Interatomic PotentialsPool-Based Active Learning

    InstaDeep, UK · Department of Engineering, University of Cambridge, Cambridge, UK

  33. Towards Discovery of Polymers for Insulin Delivery via Physics-Grounded Agentic Workflows

    May 12, 2026Martins OtunPolymer Property PredictionAgentic Workflows

    1Algonix AI Ltd., Scotland, United Kingdom

  34. Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials

    May 9, 2026Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni +1Interatomic PotentialsMolecular Property Prediction

    Cardiff University Cardiff, CF10 3AT, UK

  35. CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models

    May 9, 2026Chengqian Zhang, Yucheng Jin, Duo Zhang +2CrystalInteratomic Potentials

    AI for Science Institute, Beijing, China · Center for Data Science, Peking University, Beijing, China · LMAM and School of Mathematical Sciences, Peking University, Beijing, China +3

  36. Polarizable atomic multipoles for learning long-range electrostatics

    May 7, 2026Yoonjae Park, Dongjin Kim, Daniel S. King +5Interatomic PotentialsRaman

    Department of Chemistry, UC Berkeley, California 94720, United States · Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States · Lawrence Livermore National Laboratory, Livermore, CA, USA +1

  37. Gradient Flow Structure and Quantitative Dynamics of Multi-Head Self-Attention

    May 5, 2026Ayan PendharkarMulti-Head AttentionTransformer Attention

  38. Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs

    May 5, 2026Eszter Varga-Umbrich, Shikha Surana, Paul Duckworth +3Interatomic PotentialsActive Learning

    1InstaDeep · University of Cambridge

  39. Knowing when to trust machine-learned interatomic potentials

    May 1, 2026Shams Mehdi, Ilkwon Cho, Olexandr IsayevInteratomic PotentialsMolecular Property Prediction

    Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA. · Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA. · Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA.

  40. VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

    Apr 30, 2026Rogério Almeida Gouvêa, Gian-Marco RignaneseInteratomic PotentialsCrystal

    Institute of Condensed Matter and Nanosciences, Universit´e Catholique de Louvain, Louvain-la-Neuve,2026 Belgium. · WEL Research Institute, Avenue Pasteur 6, Wavre, Belgium.

  41. Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

    Apr 28, 2026Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9Interatomic PotentialsMolecular Dynamics

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

  42. Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass

    Apr 23, 2026Sai Harshit Balantrapu, Atul C. Thakur, Chris Benmore +1Interatomic PotentialsPowder X-Ray Diffraction

    Department of Material Design and Innovation, University at Buffalo, Buffalo, NY 14260, United States of America · NVIDIA Corp., 2788 San Tomas Expressway, Santa Clara, CA 95051, United States of America · X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, United States of America

  43. Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials

    Apr 17, 2026Yuanchang Zhou, Hongyu Wang, Yiming Du +12Interatomic PotentialsHigh-Performance Computing

    State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · Independent Researcher · Sun Yat-Sen University

  44. Shoot from the HIP: Hessian Interatomic Potentials without derivatives

    Sep 25, 2025Andreas Burger, Luca Thiede, Nikolaj Rønne +5HessianInteratomic Potentials

    University of Toronto · Vector Institute for Artificial Intelligence · Technical University of Denmark +4