论文标题
用于识别关键物理参数的层次结构符号回归与Perovskites的整体特性相关
Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites
论文作者
论文摘要
符号回归可以通过将相关性视为非线性分析表达式来识别描述材料特性的关键物理参数。然而，表达式库迅速增长，以复杂性，损害了其效率。我们通过分层方法来应对这一挑战：确定的表达式用作获得更复杂表达式的输入参数。至关重要的是，该框架可以在属性之间转移知识，从而突出物理关系。我们通过使用确定的独立筛选和超拟合操作员（SISSO）方法来识别与晶格常数和凝聚力相关的表达式来证明这种策略，然后将其用于模拟Abo3 perovskites的整体模量。
Symbolic regression identifies key physical parameters describing materials properties by uncovering correlations as nonlinear analytical expressions. However, the pool of expressions grows rapidly with complexity, compromising its efficiency. We tackle this challenge by a hierarchical approach: identified expressions are used as input parameters for obtaining more complex expressions. Crucially, this framework can transfer knowledge among properties, highlighting physical relationships. We demonstrate this strategy by using the Sure-Independence-Screening-and-Sparsifying-Operator (SISSO) approach to identify expressions correlated with the lattice constant and cohesive energy, which are then used to model the bulk modulus of ABO3 perovskites.
