通过不确定性引导主动学习获得的用于凝聚态有机体系的双截断机器学习势
A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning
AI总结：
本文基于 ACE 构建耦合短程与长程描述的双描述符 MLP，并通过不确定性引导主动学习生成精简数据集，实现了对单分子及凝聚相有机体系能量、密度、振动频率和热容的准确预测。
摘要：
基于从头算数据训练的机器学习势（MLP）将经典原子间势的计算效率，与构建相应训练集所采用的第一性原理方法的准确性和通用性结合起来。在本工作中，我们实现并训练了一个 MLP，以准确描述有机化合物的势能面并预测其性质，涵盖单分子和凝聚相两种情形。我们基于原子簇展开（ACE）设计了一种双描述符，将信息丰富的短程描述与较粗粒度的长程描述耦合起来，后者能够捕捉弱分子间相互作用。我们采用不确定性引导的主动学习来生成训练集，所构建的数据集相对于其应用广度而言规模较小，由醇类、烷烃和一种己二酸酯组成。利用该 MLP，我们计算了这些具有不同链长的体系的密度随温度的变化，所得结果与实验相比偏差小于 4%。用该 MLP 计算的振动频率与 DFT 相比均方根误差小于 1 THz。凝聚态体系的热容与实验结果相差不超过 11%，这有力地证明了双描述符为同时预测短程分子内相互作用和长程分子间相互作用提供了一个准确的框架。
英文摘要：
Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective training set. In this work, we implement and train a MLP to obtain an accurate description of the potential energy surface and property predictions for organic compounds, as both single molecules and in the condensed phase. We devise a dual descriptor, based on the atomic cluster expansion (ACE), that couples an information-rich short-range description with a coarser long-range description that captures weak intermolecular interactions. We employ uncertainty-guided active learning for the training set generation, creating a dataset that is comparatively small for the breadth of application and consists of alcohols, alkanes, and an adipate. Utilizing that MLP, we calculate densities of those systems of varying chain lengths as a function of temperature, obtaining a discrepancy of less than 4% compared with experiment. Vibrational frequencies calculated with the MLP have a root mean square error of less than 1 THz compared to DFT. The heat capacities of condensed systems are within 11% of experimental findings, which is strong evidence that the dual descriptor provides an accurate framework for the prediction of both short-range intramolecular and long-range intermolecular interactions.
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