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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| Format: | Article |
| Language: | English |
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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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| _version_ | 1849221018524057600 |
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| author | Jan Janssen Edgar Makarov Tilmann Hickel Alexander V. Shapeev Jörg Neugebauer |
| author_facet | Jan Janssen Edgar Makarov Tilmann Hickel Alexander V. Shapeev Jörg Neugebauer |
| author_sort | Jan Janssen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9ec8299619fc4a12a8a30b8fccbf30b3 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-9ec8299619fc4a12a8a30b8fccbf30b32024-11-24T12:35:39ZengNature Portfolionpj Computational Materials2057-39602024-11-0110111110.1038/s41524-024-01388-2Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculationsJan Janssen0Edgar Makarov1Tilmann Hickel2Alexander V. Shapeev3Jörg Neugebauer4Max Planck Institute for Sustainable MaterialsSkolkovo Institute of Science and Technology, Skolkovo Innovation CenterMax Planck Institute for Sustainable MaterialsSkolkovo Institute of Science and Technology, Skolkovo Innovation CenterMax Planck Institute for Sustainable MaterialsAbstract 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.https://doi.org/10.1038/s41524-024-01388-2 |
| spellingShingle | Jan Janssen Edgar Makarov Tilmann Hickel Alexander V. Shapeev Jörg Neugebauer Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations npj Computational Materials |
| title | Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations |
| title_full | Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations |
| title_fullStr | Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations |
| title_full_unstemmed | Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations |
| title_short | Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations |
| title_sort | automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations |
| url | https://doi.org/10.1038/s41524-024-01388-2 |
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