A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning

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.

基于从头计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用于创建相应训练集的第一性原理方法的准确性和普适性相结合。在本研究中，我们实现并训练了一个MLP，以获得对有机化合物势能表面和性质预测的精确描述，包括单分子和凝聚态。我们设计了一种基于原子簇展开（ACE）的双重描述符，该描述符结合了信息丰富的短程描述与捕捉弱分子间相互作用的较粗糙的长程描述。我们采用不确定性引导的主动学习来生成训练集，创建了一个相对于应用范围较为紧凑的数据集，包括醇、烷烃和一种脂酸。利用该MLP，我们计算了不同链长度的系统密度作为温度的函数，与实验值相比差异小于4%。使用MLP计算的振动频率与DFT相比，均方根误差小于1 THz。凝聚态系统的热容与实验结果相差不超过11%，这有力地证明了双重描述符为预测短程分子内和长程分子间相互作用提供了一个精确的框架。