提出新评估标准，让机器学习势函数更真实地预测材料物理性质。
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
- 用分子动力学能量守恒测试模型实用性
- 新模型eSEN在多项物性预测上达当前最优
- 适合需要高精度物性预测的研究者
机器学习势函数（MLIPs）已能以极低计算成本逼近量子力学计算。然而，测试集误差更低并不总意味着下游物性预测性能更好。本文提出在分子动力学模拟中检验模型的能量守恒能力，通过该测试的模型，其测试误差与物性预测表现的相关性显著提升。我们识别出导致模型失败的关键因素，并据此改进高表达力模型。最终提出的eSEN模型，在材料稳定性预测、热导率预测和声子计算等任务上均达到当前最优水平。
原文摘要 · Abstract (English)
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.
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