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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Article number195
Journalnpj Computational Materials
Issue number1
StatePublished - Dec 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • General Materials Science
  • Mechanics of Materials
  • Computer Science Applications


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