TAIP：测试时间适应分子动力学的原子间潜力

我们提出了一种新颖的在线测试时间适应策略（TAIP），以提高机器学习间原子势的准确性和普遍性（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.

我们的工作介绍了一种新型的在线测试时间适应策略，以提高原子间潜能对看不见的分子结构的性能。该方法分别应用于两种广泛使用的不变式机器学习间的原子势，即Schnet和Painn。我们证明了我们在多个数据集上的出色性能，包括小有机分子，C7O2H10的异构体，不同浓度的液态水，冰和电解质溶液。这些数据集中的测试数据旨在与培训数据具有更大的分布变化，因此用于研究通过我们的方法实现的概括性的提高。我们在固定数据集和分子动力学模拟中评估模型的性能。结果表明，我们的方法可以提高原子间电位在看不见的分子结构上的准确性，并且与具有周期性边界条件的复杂系统兼容。此外，我们通过降低维度探索了我们方法对特征分布的影响，从功能嵌入的角度来看，它为改进的机制提供了见解。