Interatomic Potentials

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5 papers in the last 28 days · 0.1% of indexed attention

Twelve weeks of publication activity for this topic as it is defined today.

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Molecular DynamicsComputational ChemistryDensity Functional TheoryFirst-Principles Calculation

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Period ending 2026-09-14

2 new papers

A weekly snapshot of new work published in Interatomic Potentials.

Period ending 2026-09-07

2 new papers

A weekly snapshot of new work published in Interatomic Potentials.

37 papers

Latest in Interatomic Potentials

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

    Sep 8, 2026Ryuhei Okuno, Nontawat Charoenphakdee, Kaoru Hisama +1Interatomic PotentialsAdsorption Energies

  2. Equivariance Breaks the Learning Rate

    Sep 8, 2026Andrei Manolache, Mathias NiepertAdamEquivariance

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

    Sep 4, 2026Bumju Kwak, Jeonghee JoInteratomic PotentialsHessian Matrix

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

    Sep 1, 2026Pingbing Ming, Han WangInteratomic PotentialsMessage Passing Neural Networks

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

    Aug 31, 2026Prajwal Ananth, Shuwen YueInteratomic PotentialsActive Learning

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

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

  7. An Ontology for Machine Learning Interatomic Potentials

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

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

    Jul 12, 2026Jan Eckwert, Julija ZavadlavPrevious Machine-Learning-Based Implicit SolventInteratomic Potentials

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

    Jul 12, 2026Zemin Xu, Wenbo Xie, P. HuInteratomic Potentials

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

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

  11. 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

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

    Jun 28, 2026Can Polat, Erchin Serpedin, Mustafa Kurban +1Interatomic PotentialsMolecular Structure Elucidation

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

    Jun 21, 2026Bin CaoCrystalMaterials Discovery

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

    Jun 17, 2026Masaya Hagai, Yuta Suzuki, Tomoya Murata +2Computational ChemistryInteratomic Potentials

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

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

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

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

  17. Distilling latent electrostatics from foundation machine learning interatomic potentials

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

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

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

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

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

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

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

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

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

  22. TriForces: Augmenting Atomistic GNNs for Transferable Representations

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

  23. 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

  24. 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

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

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

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

    May 12, 2026Martins OtunPolymerAgentic Discovery

  27. Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials

    May 9, 2026Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni +1Interatomic PotentialsComputational Chemistry

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

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

  29. Polarizable atomic multipoles for learning long-range electrostatics

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

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

    May 5, 2026Ayan PendharkarMulti-Head AttentionTransformer Attention

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

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

  32. Knowing when to trust machine-learned interatomic potentials

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

  33. 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 RignaneseCrystal Structure PredictionInteratomic Potentials

  34. 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

  35. 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 PotentialsCopper Impurities

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

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

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

    Sep 25, 2025Andreas Burger, Luca Thiede, Nikolaj Rønne +5Hessian MatrixComputational Chemistry