npj Computational Materials (Sep 2022)

A flexible and scalable scheme for mixing computed formation energies from different levels of theory

  • Ryan S. Kingsbury,
  • Andrew S. Rosen,
  • Ayush S. Gupta,
  • Jason M. Munro,
  • Shyue Ping Ong,
  • Anubhav Jain,
  • Shyam Dwaraknath,
  • Matthew K. Horton,
  • Kristin A. Persson

DOI
https://doi.org/10.1038/s41524-022-00881-w
Journal volume & issue
Vol. 8, no. 1
pp. 1 – 11

Abstract

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Abstract Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory (DFT), which now contain millions of calculations at the generalized gradient approximation (GGA) level of theory. It is now feasible to carry out high-throughput calculations using more accurate methods, such as meta-GGA DFT; however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing (GGA) calculations. Instead, we propose here a general procedure by which higher-fidelity, low-coverage calculations (e.g., meta-GGA calculations for selected chemical systems) can be combined with lower-fidelity, high-coverage calculations (e.g., an existing database of GGA calculations) in a robust and scalable manner. We then use legacy PBE(+U) GGA calculations and new r2SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions, and discuss practical considerations for its implementation.