  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 additional data. TAIP enhances the test
performance on various benchmarks, from small molecule datasets to complex
periodic molecular systems with various types of elements. Remarkably, it also
enables stable MD simulations where the corresponding baseline models collapse.
