Machine Learning Interatomic Potentials

Also known as MLIP

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

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

Enhancing molecular dynamics with equivariant machine-learned densities
Apr 27, 2026Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto +3Infrared Spectroscopy Machine Learning Interatomic Potentials
BIFOLD and Machine Learning Group, Technische Universität Berlin, Franklinstr. 28/29, 10587 Berlin, Germany · Department of Chemistry, New York University, New York, NY 10003, USA · Department of Artificial Intelligence, Korea University, Seoul 02841, Korea +5
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 +1Machine Learning Interatomic Potentials Materials Science
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
Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
Apr 17, 2026Yuanchang Zhou, Hongyu Wang, Yiming Du +12Machine Learning Interatomic Potentials Communication-Efficient Distributed Training
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · Independent Researcher · Sun Yat-Sen University
Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
Apr 15, 2026Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8Graph Foundation Models High-Performance Computing
Computational Sciences and Engineering Division, Oak Ridge National Laboratory · Computer Science and Mathematics Division, Oak Ridge National Laboratory · National Center for Computational Sciences Division, Oak Ridge National Laboratory +2
MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals
Apr 15, 2026Adam Lahouari, Shen Ai, Jihye Han +16Machine Learning Interatomic Potentials
Department of Chemistry, New York University, New York, NY 10003, USA · Department of Physics, New York University, New York, NY 10003, USA · Simons Center for Computational Physical Chemistry, New York University, New York, NY 10003, USA +5
Improving Molecular Force Fields with Minimal Temporal Information
Apr 14, 2026Ali Mollahosseini, Mohammed Haroon Dupty, Wee Sun LeeMachine Learning Interatomic Potentials Trajectory Representation Learning
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran · School of Computing, National University of Singapore, Singapore
Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
Apr 10, 2026Siqi Chen, Zhiqiang Wang, Yili Shen +8Graph Neural Networks Machine Learning Interatomic Potentials
Department of Chemical and Biomolecular Engineering, University of Massachusetts Amherst, Amherst, MA 01003 · Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003 · Also at Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 +5
EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers
Apr 10, 2026Yi-Lun Liao, Alexander J. Hoffman, Sabrina C. Shen +3Equivariant GNNs Graph Transformer
Massachusetts Institute of Technology (MIT) · Mirror Physics · Entalpic
Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS
Apr 8, 2026Luca Pennati, Andong Hu, Ivy Peng +2Molecular Dynamics Simulation High-Performance Computing
KTH Royal Institute of Technology, Stockholm, Sweden
AQVolt26: High-Temperature r $^2$ SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries
Apr 2, 2026Jiyoon Kim, Chuhong Wang, Aayush R. Singh +6Materials Science Machine Learning Interatomic Potentials
SandboxAQ, Palo Alto, California, United States · Nvidia Corporation, Santa Clara, CA
Hessian-informed machine learning interatomic potential towards bridging theory and experiments
Mar 26, 2026Bangchen Yin, Jian Ouyang, Zhen Fan +7Machine Learning Interatomic Potentials Computational Materials Science
ByteDance Seed · Tsinghua University · Peking University
Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
Mar 25, 2026Vivienne Pelletier, Vedant Bhat, Daniel J. Rivera +2Machine Learning Interatomic Potentials Data-Efficient Learning
Materials Science & Engineering, School for the Engineering of Matter, Transport, & Energy, Arizona State University, 551 E. Tyler Mall, Tempe Arizona, 85287, USA · Chemical Engineering, School for the Engineering of Matter, Transport, & Energy, Arizona State University, 551 E. Tyler Mall, Tempe Arizona, 85287, USA
Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials
Mar 24, 2026Shuyu Bi, Zhede Zhao, Qiangchao Sun +3Machine Learning Interatomic Potentials Graph Neural Networks
School of Materials Science and Engineering & State Key Laboratory of Advanced Refractories & State Key Laboratory of Advanced Special Steels, Shanghai University, Shanghai, 200444, P. R. China.
Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
Mar 23, 2026Joanna Zou, Youssef MarzoukTraining Data Curation Training Data Selection
Computational Science & Engineering Massachusetts Institute of Technology Cambridge, MA, 02139, USA
Data-driven construction of machine-learning-based interatomic potentials for gas-surface scattering dynamics: the case of NO on graphite
Mar 19, 2026Samuel Del Fré, Gilberto A. Alou Angulo, Maurice Monnerville +1Machine Learning Interatomic Potentials Molecular Dynamics Simulation
Univ. Lille, CNRS, UMR 8523 – PhLAM – Physique des Lasers Atomes et Molécules, F-59000 Lille, France
An SO(3)-equivariant reciprocal-space neural potential for long-range interactions
Mar 19, 2026Lingfeng Zhang, Taoyong Cui, Dongzhan Zhou +6Machine Learning Interatomic Potentials Computational Materials Science
The Chinese University of Hong Kong, Hong Kong, 999077, China. · Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. +1
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
Mar 16, 2026William J. Baldwin, Ilyes Batatia, Martin Vondrák +2Density Functional Theory Machine Learning Interatomic Potentials
Engineering Laboratory, University of Cambridge, Trumpington St, Cambridge, UK · University of Bayreuth, Bavarian Center for Battery Technology (BayBatt), Bayreuth, Germany · Fritz-Haber-Institute of the Max-Planck-Society, Berlin, Germany
Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials
Mar 12, 2026Abhinaba Basu, Pavan ChakrabortyMachine Learning Interatomic Potentials Materials Property Prediction
Indian Institute of Information Technology Allahabad (IIITA), Prayagraj, India · National Institute of Electronics and Information Technology (NIELIT), India
Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training
Mar 10, 2026Rick Oerder, Jan HamaekersMachine Learning Interatomic Potentials
Institute for Numerical Simulation, University of Bonn · Fraunhofer Institute for Algorithms and Scientific Computing SCAI · Institute for Numerical Simulation, University of Bonn, Friedrich-Hirzebruch-Allee 7, 53115 Bonn, Germany +1
Mixture of experts architectures for machine learning interatomic potentials
Mar 9, 2026Yuzhi Liu, Duo Zhang, Anyang Peng +3Machine Learning Interatomic Potentials Mixture-of-Experts Models
AI for Science Institute, Beijing 100080, P. R. China. · DP Technology, Beijing 100080, P. R. China. · Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, P. R. China. +3
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
Mar 6, 2026Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan +1Machine Learning Interatomic Potentials Self-Attention
UC Berkeley · FAIR at Meta · LBNL
Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials
Mar 4, 2026Austin Rodriguez, Justin S. Smith, Sakib Matin +3Machine Learning Interatomic Potentials Second-Order Optimization
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States. · Department of Chemical Engineering & Materials Science, Michigan State University, East Lansing, Michigan 48824, United States. · NVIDIA Corp., 2788 San Tomas Expy, Santa Clara, California 95051, United States. +3
MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
Mar 2, 2026Yuanchang Zhou, Siyu Hu, Xiangyu Zhang +3Machine Learning Interatomic Potentials Computational Materials Science
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, UCAS
MBD-ML: Many-body dispersion from machine learning for molecules and materials
Feb 25, 2026Evgeny Moerman, Adil Kabylda, Almaz Khabibrakhmanov +1Materials Property Prediction Quantum Chemistry
Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
Feb 23, 2026Ilyes Batatia, William J. Baldwin, Domantas Kuryla +10Molecular Foundation Models Machine Learning Interatomic Potentials
Engineering Laboratory, University of Cambridge, Trumpington St, Cambridge, UK · Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK · Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge, UK +6
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials
Feb 18, 2026G. Laskaris, D. Morozov, D. Tarpanov +6Machine Learning Interatomic Potentials Hybrid Quantum-Classical ML
Terra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, Switzerland · LIACS, Leiden University, Netherlands · Nanoscience Center and Department of Chemistry, University of Jyväskylä, P.O. Box 35, 40014 Jyväskylä, Finland +2
Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces
Feb 16, 2026Nicolaï Gouraud, Côme Cattin, Thomas Plé +3Molecular Dynamics Simulation Machine Learning Interatomic Potentials
Qubit Pharmaceuticals, Advanced Research Department, 75014 Paris, France · Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, 75005 Paris, France
AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules
Jan 2, 2026Stephen E. Farr, Stefan Doerr, Antonio Mirarchi +4Machine Learning Interatomic Potentials Drug Discovery
Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain · Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain · Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
Sep 28, 2025Evan Dramko, Yihuang Xiong, Yizhi Zhu +4Machine Learning Interatomic Potentials Materials Property Prediction
Department of Computer Science, Rice University, Houston, TX, USA · Thayer School of Engineering, Dartmouth College, Hanover, NH, USA · Department of Materials Science and Nanoengineering, Rice University, Houston, TX, USA +2
Shoot from the HIP: Hessian Interatomic Potentials without derivatives
Sep 25, 2025Andreas Burger, Luca Thiede, Nikolaj Rønne +5Machine Learning Interatomic Potentials Computational Materials Science
University of Toronto · Vector Institute for Artificial Intelligence · Technical University of Denmark +4

Apr 27, 2026J/K move · Enter open · S save

Enhancing molecular dynamics with equivariant machine-learned densities

Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto, Klaus-Robert Müller+2

BIFOLD and Machine Learning Group, Technische Universität Berlin, Franklinstr. 28/29, 10587 Berlin, Germany · Department of Chemistry, New York University, New York, NY 10003, USA · Department of Artificial Intelligence, Korea University, Seoul 02841, Korea+5

Infrared Spectroscopy Machine Learning Interatomic Potentials Quantum Chemistry Molecular Property Prediction Molecular Dynamics Simulation

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Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and polarizabilities inaccessible. We introduce DenSNet, a density-first approach to machine-learned electronic structure that learns the Hohenberg--Kohn map from nuclear configurations to the ground-state electron density. Our approach employs an SE(3)-equivariant neural network to predict density coefficients of a flexible atom-centered Gaussian basis, combined with a

Δ

-learning strategy that uses superposed atomic densities as a prior to accelerate training. A second equivariant network then maps the predicted density to the total energy, providing a unified framework for molecular dynamics and electronic structure. We validate DenSNet on ethanol, ethanethiol, and resorcinol, where infrared spectra from machine-learned trajectories show excellent agreement with experimental gas-phase measurements. To test scalability, we train on polythiophene oligomers with 1--6 monomers and extrapolate to chains of up to 12 monomers, generating stable long-time trajectories whose infrared spectra agree with reference density functional theory calculations. Here, we show that reinstating the electron density as the central learned quantity opens a practical route to transferable prediction of spectroscopic and electronic observables in large-scale molecular simulations.