标题： 高效数据构建高保真图深度学习原子间势能模型 显示英文标题

标题： Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials

摘要： 机器学习势（MLPs）由于能够以极低的计算成本准确再现从头算势能面（PESs），已成为大规模原子模拟中不可或缺的工具。为了计算效率，目前大多数MLPs的训练数据是使用相对廉价的密度泛函理论（DFT）方法计算的，例如Perdew-Burke-Ernzerhof（PBE）广义梯度近似（GGA）泛函。像最近开发的强约束适当归一化（SCAN）泛函这样的元GGA已被证明可以显著改进多种键合系统的原子相互作用描述，但它们较高的计算成本仍然是其在MLP开发中应用的障碍。在这项工作中，我们概述了一种数据高效的多保真度方法，用于构建整合不同理论层次的Materials 3体图网络（M3GNet）原子间势能。以硅和水为例，我们展示了在包含10%高保真SCAN计算的低保真GGA计算组合数据集上训练的多保真度M3GNet模型，可以达到与在包含8倍SCAN计算的数据集上训练的单保真度M3GNet模型相当的精度。这项工作通过利用现有的低保真数据集，为以经济有效的方式开发高保真MLPs铺平了道路。 显示英文摘要

摘要： Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations because of their ability to reproduce ab initio potential energy surfaces (PESs) very accurately at a fraction of computational cost. For computational efficiency, the training data for most MLPs today are computed using relatively cheap density functional theory (DFT) methods such as the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) functional. Meta-GGAs such as the recently developed strongly constrained and appropriately normed (SCAN) functional have been shown to yield significantly improved descriptions of atomic interactions for diversely bonded systems, but their higher computational cost remains an impediment to their use in MLP development. In this work, we outline a data-efficient multi-fidelity approach to constructing Materials 3-body Graph Network (M3GNet) interatomic potentials that integrate different levels of theory within a single model. Using silicon and water as examples, we show that a multi-fidelity M3GNet model trained on a combined dataset of low-fidelity GGA calculations with 10% of high-fidelity SCAN calculations can achieve accuracies comparable to a single-fidelity M3GNet model trained on a dataset comprising 8x the number of SCAN calculations. This work paves the way for the development of high-fidelity MLPs in a cost-effective manner by leveraging existing low-fidelity datasets.