Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles

作者：Mikołaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti, Abhijeet Sadashiv Gangan, Hendrik H. Heenen, Joseph Kioseoglou, Ivor Lončarić, Hemanadhan Myneni, Janosh Riebesell, M. Rossi, Matthias Rupp, Jonathan Schmidt, Sharma, S, K, Benjamin X. Shi, Antoni Wadowski, Lukas Hörmann, Venkat Kapil · 发表于：arXiv (Cornell University) · 年份：2026 · 研究领域：Machine Learning in Materials Science、Quantum many-body systems、Block Copolymer Self-Assembly

Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials at length and time scales that were previously inaccessible. However, due to lack of ground truth data, their accuracy on structural and dynamical observables in finite thermodynamic ensembles is yet to be established. Here, we introduce Dyna-Mat-v1.0, a benchmark dataset of condensed-phase first-principles molecular dynamics trajectories designed to test foundation MLIPs at realistic finite-temperature conditions. Using this dataset, we evaluate 15 foundation MLIPs across four model tiers by comparing both single-point energy and force errors on first-principles configurations and observables generated from MLIP-driven trajectories. We find that "on average" models with lower single-point force errors also yield lower errors for structural and dynamical observables. However, there are individual systems for which low force errors lead to qualitative failures in the predicted structure. Pressure remains poorly described across most models, pointing to limitations in the density functional theory stress labels available in current large-scale training datasets. Finally, we construct an accuracy-cost Pareto frontier to identify the best trade-offs for molecular dynamics with foundation MLIPs, finding that the latest generation of cross-trained models is close to Pareto-optimal according to the accurac...