Hierarchical Symbolic Regression for Identifying Key Physical Parameters Correlated with Bulk Properties of Perovskites

Lucas Foppa, Thomas A.R. Purcell, Sergey V. Levchenko, Matthias Scheffler, Luca M. Ghringhelli

Research output: Contribution to journalArticlepeer-review

Abstract

Symbolic regression identifies nonlinear, analytical expressions relating materials properties and key physical parameters. However, the pool of expressions grows rapidly with complexity, compromising its efficiency. We tackle this challenge hierarchically: identified expressions are used as inputs for further obtaining more complex expressions. Crucially, this framework can transfer knowledge among properties, as demonstrated using the sure-independence-screening-and-sparsifying-operator approach to identify expressions for lattice constant and cohesive energy, which are then used to model the bulk modulus of ABO3 perovskites.

Original languageEnglish
Article number055301
JournalPhysical Review Letters
Volume129
Issue number5
DOIs
Publication statusPublished - 29 Jul 2022

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