标题： 专用机器学习间歇势能：精度与计算成本之间的权衡 显示英文标题

标题： Application-specific Machine-Learned Interatomic Potentials: Exploring the Trade-off Between Precision and Computational Cost

摘要： 机器学习原子间势 (MLIP) 正在彻底改变计算材料科学和化学，为从头算分子动力学 (MD) 模拟提供了一种有效的替代方案。 然而，拟合高质量的 MLIP 仍然是一项极具挑战性、耗时且计算密集的任务，需要考虑诸多因素，例如：训练集中应该包含多少原子构型以及原子构型的类型？ 应该使用哪种从头算收敛水平来生成训练集？ 应该使用哪种损失函数来拟合 MLIP？ 应该使用哪种机器学习架构来训练 MLIP？ 这些问题的答案会显著影响 MLIP 训练的计算成本以及后续 MLIP MD 模拟的准确性和计算成本。 在本研究中，我们强调，同时考虑训练集选择策略、能量与力的权重、{\em ab initio} 参考模拟的精度，以及 MLIP 的模型复杂度和计算成本，可以显著降低与训练和评估 MLIP 相关的总体计算成本。 这为高效计算生成高质量 MLIP 开辟了道路，适用于各种需要不同精度与训练和评估成本权衡的应用。 显示英文摘要

摘要： Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to {\em ab initio} molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g. How much and what kind of atomic configurations should be included in the training set? Which level of {\em ab initio} convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we highlight that simultaneously considering training set selection strategies, energy versus force weighting, precision of the {\em ab initio} reference simulations, as well as model complexity and computational cost of MLIPs can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.