Machine Learning Interatomic Potentials

Also known as MLIP

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

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GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
Jun 2, 2026Parth Verma, Parv P. Singh, Vipul Garg +3Graph Neural Networks Machine Learning Interatomic Potentials
Department of Computer Science, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India · Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India · Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
Benchmark Dataset for Catalysis on 2D MXenes
May 30, 2026Pavlo Melnyk, Anmar Karmush, Mårten Wadenbäck +4Machine Learning Interatomic Potentials Benchmark Design
Computer Vision and Learning Systems, Department of Electrical Engineering (ISY) & AI4X, Linköping University (LiU), 58183 Linköping, Sweden. · Materials Design Division, Department of Physics, Chemistry and Biology (IFM), and Wallenberg Initiative Materials Science for Sustainability (WISE); Linköping University (LiU), 58183 Linköping, Sweden. · Wallenberg Initiative Materials Science for Sustainability (WISE); Linköping University (LiU), 58183 Linköping, Sweden.
MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
May 29, 2026Etinosa Osaro, Santosh Adhikari, Stamatia Zavitsanou +2Machine Learning Interatomic Potentials Large Language Model-Guided Optimization
PsiQuantum, 700 Hansen Way, Palo Alto, CA 94304
Machine Learning Multiscale Interactions
May 25, 2026Àlex Solé, Sergio Suárez-Dou, Albert Mosella-Montoro +4Machine Learning Interatomic Potentials Hierarchical Representation Learning
Image Processing Group – Signal Theory and Communications Department, Universitat Politècnica de Catalunya, Barcelona, Spain. · Inorganic and Organic Chemistry Department and Institute of Theoretical and Computational Chemistry, Universitat de Barcelona, Barcelona, Spain. · Department of Physics and Materials Science, University of Luxembourg, Luxembourg City, Luxembourg.
Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs
May 22, 2026Ihor Neporozhnii, Sjoerd Hoogland, Oleksandr VoznyyMachine Learning Interatomic Potentials Multi-Task Learning
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.
TriForces: Augmenting Atomistic GNNs for Transferable Representations
May 20, 2026Ali Ramlaoui, Alexandre Duval, Hannah Bull +4Disentangled Representation Learning Machine Learning Interatomic Potentials
Entalpic, Paris, France · Universit´e Paris-Saclay, CentraleSup´elec, Inria, Gif-sur-Yvette, France
Generative Pseudo-Force Fields for Molecular Generation
May 18, 2026Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4Machine Learning Interatomic Potentials Diffusion Model Sampling
Machine Learning Group, Technische Universität Berlin, Berlin, Germany · BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany · Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany +6
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
May 15, 2026Sauradeep Majumdar, Miguel Steiner, Johannes C. B. Dietschreit +4Free Energy Calculation Machine Learning Interatomic Potentials
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.
All-atomistic Transferable Neural Potentials for Protein Solvation
May 14, 2026Rishabh Dey, Salvina Sharipova, Konstantin PopovNeural Surrogate Modeling Machine Learning Interatomic Potentials
University of North Carolina at Chapel Hill - Eshelman School of Pharmacy
Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
May 14, 2026Wenwen Li, Yuki Orimo, Nontawat CharoenphakdeeMulti-Agent LLM Systems AI Agents for Scientific Discovery
Preferred Networks, Inc., Tokyo, Japan.
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
May 13, 2026Eszter Varga-Umbrich, Zachary Weller-Davies, Paul Duckworth +3Machine Learning Interatomic Potentials Distribution Shift
InstaDeep, UK · Department of Engineering, University of Cambridge, Cambridge, UK
PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interface
May 13, 2026Ryan DongMemory-Augmented Neural Networks Machine Learning Interatomic Potentials
Independent Research
Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
May 12, 2026Sanya Murdeshwar, Sanjit Shashi, Kevin Bachelor +3Machine Learning Interatomic Potentials Molecular Dynamics Simulation
University of California, Santa Cruz · GiwoTech Inc. · Pennsylvania State University
Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials
May 9, 2026Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni +1Machine Learning Interatomic Potentials OOD Generalization
Cardiff University Cardiff, CF10 3AT, UK
Diagnosing Spectral Ceilings in Equivariant Neural Force Fields
May 8, 2026Hyunmog KimMachine Learning Interatomic Potentials Spectral Bias
Independent Researcher.
Polarizable atomic multipoles for learning long-range electrostatics
May 7, 2026Yoonjae Park, Dongjin Kim, Daniel S. King +5Machine Learning Interatomic Potentials Materials 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 · Lawrence Livermore National Laboratory, Livermore, CA, USA +1
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
May 5, 2026Eszter Varga-Umbrich, Shikha Surana, Paul Duckworth +3Machine Learning Interatomic Potentials Active Learning
1InstaDeep · University of Cambridge
Knowing when to trust machine-learned interatomic potentials
May 1, 2026Shams Mehdi, Ilkwon Cho, Olexandr IsayevMachine Learning Interatomic Potentials Selective 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.
Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
Apr 28, 2026Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9Machine Learning Interatomic Potentials Multifidelity Modeling
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
Enhancing molecular dynamics with equivariant machine-learned densities
Apr 27, 2026Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto +3Machine Learning Interatomic Potentials Quantum Chemistry
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
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 · Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 +5
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

Jun 2, 2026, cs.LGJ/K move · Enter open · S save

GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond

Parth Verma, Parv P. Singh, Vipul Garg, Ishita Thakre+2

Department of Computer Science, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India · Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India · Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India

Graph Neural Networks Machine Learning Interatomic Potentials Message Passing Neural Networks Model Merging

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Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new chemical systems requires expensive retraining of foundation models. Inspired by model merging in vision and language processing, we introduce GFFMERGE, the first principled framework for closed-form model merging in GNNs. We exploit the linear structure of message-passing layers and formulate merging as a convex embedding-alignment problem with an analytical solution. Through the first systematic benchmarking of model merging for GNNs, we show that existing methods designed for vision and language catastrophically fail on force field regression, while GFFMERGE recovers performance approaching gold standard joint training. Across molecular (MD17, MD22), solid-state (LiPS20), and large-scale graph benchmarks, GFFMERGE and GNNMERGE (its generic GNN counterpart) achieve 5-27

\times

speedups while enabling modular composition of specialized models. Remarkably, our closed-form solution alone outperforms all baseline methods before fine-tuning and provides superior initialization for faster, data-efficient convergence.

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