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A flexible and scalable scheme for mixing computed formation energies from different levels of theory
oleh: 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
Format: | Article |
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Diterbitkan: | Nature Portfolio 2022-09-01 |
Deskripsi
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.