Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Affenzeller:2022:GPTP,

• author = "Bogdan Burlacu and Michael Kommenda and Gabriel Kronberger and Stephan M. Winkler and Michael Affenzeller",
• title = "Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data",
• booktitle = "Genetic Programming Theory and Practice XIX",
• year = "2022",
• editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf",
• series = "Genetic and Evolutionary Computation",
• pages = "1--30",
• address = "Ann Arbor, USA",
• month = jun # " 2-4",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-981-19-8459-4",
• URL = "https://arxiv.org/abs/2206.06422",
• DOI = "doi:10.1007/978-981-19-8460-0_1",
• abstract = "Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and the understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow calculating the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however, their applicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful “white-box” approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data.",
• notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023",

}

Genetic Programming entries for Bogdan Burlacu Michael Kommenda Gabriel Kronberger Stephan M Winkler Michael Affenzeller

Citations