论文标题
用于机器学习潜力的系统原子结构数据集：镁缺陷的应用
Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium
论文作者
论文摘要
我们提出了一种出于身体动机的策略，用于建设可转移机器学习间潜能的培训集。它基于对随机晶体结构中所有可能的空间组的系统探索，以及细胞形状，大小和原子位置的变形。最终的电势被证明是公正的，通常适用于散装缺陷的研究，而无需在训练集中包含任何缺陷结构或采用任何其他主动学习。使用这种方法，我们为纯镁构建了可转移的电位，该电势可以很好地重现六角形封闭式包装（HCP）和身体中心立方（BCC）多晶型物的性能。在此过程中，我们研究了不同类型的训练结构如何影响所产生潜力的性质和预测能力。
We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional Active Learning. Using this approach we construct transferable potentials for pure Magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how different types of training structures impact the properties and the predictive power of the resulting potential.
