| Autor: |
Riemelmoser, Stefan, Verdi, Carla, Kaltak, Merzuk, Kresse, Georg |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Chemical Theory and Computation 2023 19 (20), 7287-7299 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1021/acs.jctc.3c00848 |
| Popis: |
Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in application due to their large computational cost. Here, we construct a DFT substitute functional for the RPA using supervised and unsupervised machine learning (ML) techniques. Our ML-RPA model can be interpreted as a non-local extension to the standard gradient approximation. We train an ML-RPA functional for diamond surfaces and liquid water and show that ML-RPA can outperform the standard gradient functionals in terms of accuracy. Our work demonstrates how ML-RPA can extend the applicability of the RPA to larger system sizes, time scales and chemical spaces. |
| Databáze: |
arXiv |
| Externí odkaz: |
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