Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors on held out test sets do not always translate to improved results on downstream physical property prediction tasks. In this paper, we propose testing MLIPs on their practical ability to conserve energy during molecular dynamic simulations. If passed, improved correlations are found between test errors and their performance on physical property prediction tasks. We identify choices which may lead to models failing this test, and use these observations to improve upon highly-expressive models. The resulting model, eSEN, provides state-of-the-art results on a range of physical property prediction tasks, including materials stability prediction, thermal conductivity prediction, and phonon calculations.

翻译：机器学习原子间势函数（MLIPs）在近似量子力学计算方面已日益高效，且计算成本仅为后者的一小部分。然而，在保留测试集上较低的误差并不总能转化为在下游物理性质预测任务中结果的改进。本文提出通过分子动力学模拟中实际能量守恒能力来测试MLIPs。若通过测试，则发现测试误差与物理性质预测任务性能之间的相关性得到改善。我们识别了可能导致模型未通过此测试的设计选择，并利用这些观察结果对高表现力模型进行改进。最终得到的模型eSEN，在包括材料稳定性预测、热导率预测和声子计算等一系列物理性质预测任务中取得了最先进的结果。