Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data.

ABSTRACT: Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data causes deterioration of the test performance of MLIPs, and even leads to collapse of MD simulations. In this work, we propose an online Test-time Adaptation Interatomic Potential (TAIP) framework to improve the generalization on test data. Specifically, we design a dual-level self-supervised learning approach that leverages global structure and atomic local environment information to align the model with the test data. Extensive experiments demonstrate TAIP's capability to bridge the domain gap between training and test dataset without additio

SUBMITTER: Cui T 

PROVIDER: S-EPMC11846885 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature