Subset of stochastically generated interacting molecules for CH GAP interatomic potential

This is a subset of the dataset used to train general-purpose CH GAP interatomic potential [1].

This subset contains the interacting molecules generated stochastically in a following manner. The subset is generated using active learning and uncertainty-based configuration selection. We started with randomly chosen pairs of CH-containing molecules from the QM9 database up to 7 carbon atoms. The probability of selecting the molecules is set based on the energy and size of the molecule.  The probability is lower as the energy above the convex hull is higher.  A bigger size of the molecule also lowers the probability to favor the inclusion of small structures. Then, we estimate the uncertainties for the new structures based on how far away from the existing interacting molecules in the training set they are (in configuration space), and identify those with the largest expected errors. This way, we generated about 3k structures.

本数据集为训练通用CH GAP原子间势（interatomic potential）所用原始数据集的子集[1]。

该子集包含以下述方式随机生成的相互作用分子体系，并通过主动学习与基于不确定性的构型选择流程构建完成。我们首先从QM9数据库中随机选取含碳数不超过7的含CH分子对作为初始样本，分子选取概率由其能量与分子尺寸共同决定：相对于凸包（convex hull）的能量越高，选取概率越低；分子尺寸越大，选取概率同样越低，以此优先纳入小型结构体系。随后，我们基于新构型与训练集中已有相互作用分子体系在构型空间（configuration space）中的距离估算其不确定性，并筛选出期望误差最大的样本。通过该流程，我们共生成约3000个结构。