多层符号回归法识别钙钛矿体相性质的关键物理参数
Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites
摘要：
符号回归通过揭示非线性分析表达式的相关性来识别描述材料特性的关键物理参数。然而，表达式池随着复杂性而快速增长，从而影响了其效率。我们通过分层的方法来应对这一挑战：识别的表达式被用作输入参数，以获得更复杂的表达式。至关重要的是，这个框架可以在属性之间传递知识，突出物理关系。我们证明了这一战略，通过使用确定的独立性筛选和稀疏化算子（SISSO）的方法来确定与晶格常数和内聚能，然后用于模拟ABO3钙钛矿的体积模量相关的表达式。
英文摘要：
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.
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