Recent Advancements in SISSO as Applied to Thermal Conductivity

ORAL

Abstract

Symbolic regression is a promising class of methods for both explainable artificial-intelligence (AI) and materials discovery [1]. The sure-independence screening and sparsifying operator (SISSO) approach [2,3] represents a deterministic way of finding these models as it combines symbolic regression with compressed sensing. Here we present new concepts for the feature creation step that introduce basic grammatical rules for the generated expressions. The utility of these new conditions are demonstrated via toy problems, and then rigorously tested by creating new models for the thermal conductivity of a material [4].

[1] Z. Li and J. Ji arXiv: 2111.12210 (2021)

[2] R. Ouyang et al. Phys. Rev. Mater. 2, 083802 (2018)

[3] T. A. R. Purcell et al. J. Chem. Phys. 159, 114110 (2023)

[4] T. A. R. Purcell et al. npj Comput. Mater. 9, 112 (2023)

* Funded by the NOMAD Center of Excellence (Horizon 2020 Nº 951786), TEC1p (ERC Advanced Grant Nº 740233), and the AvH Postdoctoral Fellowship Program

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Publication: T. A. R. Purcell et al. J. Chem. Phys. 159, 114110 (2023)
T. A. R. Purcell et al. npj Comput. Mater. 9, 112 (2023)

Presenters

  • Thomas A Purcell

    The NOMAD Laboratory at the FHI of the MPG, The University of Arizona

Authors

  • Thomas A Purcell

    The NOMAD Laboratory at the FHI of the MPG, The University of Arizona

  • Matthias Scheffler

    The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, The NOMAD Laboratory at the Fritz Haber Institute of the MPG, The NOMAD Laboratory at the FHI of the Max Planck Society