利用原子间键的领域知识对化学空间中八重态AB型二元化合物进行机器学习预测
Machine-learning octet $AB$-type binary compounds across chemical space with domain knowledge of the interatomic bond
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中文总结 AI 辅助
该研究将原子间键的领域知识融入机器学习模型，提升了八重态AB型二元化合物结构稳定性的预测精度，形成能差预测较以往方法显著改善。
中文摘要 AI 辅助
八重态AB型二元化合物的结构稳定性预测是经典的材料信息学问题。其挑战在于捕捉闪锌矿（β-ZnS）结构中4配位原子与岩盐（NaCl）结构中6配位原子的相对稳定性，这种稳定性受电荷转移和原子尺寸差异的调控。以往的结构图谱和机器学习方法采用价电子数、电离能、原子半径等原子特征，要么基于物理直觉，要么采用符号回归。本文证明，明确纳入原子间键的领域知识可显著且系统地提升β-ZnS/NaCl稳定性的预测性能。我们通过紧束缚键模型的递归求解得到的局域电子结构粗粒化表示来编码这种键合信息。底层的成对哈密顿量取自双原子分子密度泛函理论计算的折叠本征态，从而包含特定A-B对之间键的领域知识。我们将该描述的优势通过一组独立训练的核岭回归或符号回归模型结合序列特征选择来验证。将所得模型与以往采用相同八重态二元化合物从头算计算作为训练数据的符号回归模型进行比较，发现与以往工作相比，AB化合物的形成能差预测有显著提升，且证明增加键感知递归特征的数量可提高预测精度。
英文摘要
The prediction of the structural stability of octet $AB$-type binary compounds is a classical materials informatics problem. The challenge is to capture the relative stability of 4-fold coordinated atoms in zincblende ($β$-ZnS) structure and 6-fold coordinated atoms in rocksalt (NaCl) structure, modulated by charge transfer and atomic-size differences. Previous structure maps and machine-learning approaches used atomic features such as valence-electron count, ionization potential and atomic radii, using either physical intuition or symbolic regression. Here, we demonstrate that explicitly incorporating the domain knowledge of the interatomic bonds can significantly and systematically improve the prediction of $β$-ZnS/NaCl stability. We encode this bonding information through a coarse-grained representation of the local electronic structure obtained by a recursive solution of a tight-binding bond model. The underlying pairwise Hamiltonians are taken from downfolded eigenstates of density-functional theory calculations for diatomic molecules and thereby include domain knowledge of the bond between specific $A-B$ pairs. The benefit of this description is demonstrated with an ensemble of independently trained Kernel Ridge or symbolic regression models combined with sequential feature selection. The obtained models are compared to a previous symbolic-regression model using the same set of \emph{ab initio} calculations for octet binaries as training data. We find a significant improvement in the prediction of the formation energy difference of $AB$ compounds as compared to previous works and demonstrate that an increasing amount of bond-informed recursion features improves the predictive accuracy.
