TAIP：測試時間適應分子動力學的原子間潛力

我們提出了一種新穎的在線測試時間適應策略（TAIP），以提高機器學習間原子勢的準確性和普遍性（MLIP）

Our work introduces a novel online test-time adaptation strategy for improving the performance of interatomic potentials on unseen molecular structures. The method is applied to two widely used invariant and equivariant machine learning interatomic potentials, namely SchNet and PaiNN, respectively. We demonstrate the superior performance of our method on multiple datasets, including small organic molecules, isomers of C7O2H10, liquid water, ice, and electrolyte solutions of different concentrations. The test data from these datasets are designed to have a larger distribution shift from the training data, and are therefore used to investigate the improvement in generalizability achieved by our method. We evaluate the performance of the models on fixed datasets and in molecular dynamics simulations. The results show that our method can enhance the accuracy of interatomic potentials on unseen molecular structures, and it is compatible with complex systems featuring periodic boundary conditions. Moreover, we explore the impact of our method on feature distributions through dimensionality reductions, which provides insights into the mechanism behind the improvement from the perspective of feature embedding.

我們的工作介紹了一種新型的在線測試時間適應策略，以提高原子間潛能對看不見的分子結構的性能。該方法分別應用於兩種廣泛使用的不變式機器學習間的原子勢，即Schnet和Painn。我們證明了我們在多個數據集上的出色性能，包括小有機分子，C7O2H10的異構體，不同濃度的液態水，冰和電解質溶液。這些數據集中的測試數據旨在與培訓數據具有更大的分佈變化，因此用於研究通過我們的方法實現的概括性的提高。我們在固定數據集和分子動力學模擬中評估模型的性能。結果表明，我們的方法可以提高原子間電位在看不見的分子結構上的準確性，並且與具有周期性邊界條件的複雜系統兼容。此外，我們通過降低維度探索了我們方法對特徵分佈的影響，從功能嵌入的角度來看，它為改進的機制提供了見解。