Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations
Abstract First principles approaches have revolutionized our ability in using computers to predict, explore, and design materials. A major advantage commonly associated with these approaches is that they are fully parameter-free. However, numerically solving the underlying equations requires to choo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01388-2 |
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| Summary: | Abstract First principles approaches have revolutionized our ability in using computers to predict, explore, and design materials. A major advantage commonly associated with these approaches is that they are fully parameter-free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations, it becomes exceedingly important to achieve a truly parameter-free approach. Utilizing uncertainty quantification (UQ) and linear decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters for plane wave density functional theory (DFT) calculations. Based on this formalism we implement a fully automated approach that requires as input the target precision rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice. |
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| ISSN: | 2057-3960 |