Report
Machine learning density functionals from the random-phase approximation
| العنوان: | Machine learning density functionals from the random-phase approximation |
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| المؤلفون: | Riemelmoser, Stefan, Verdi, Carla, Kaltak, Merzuk, Kresse, Georg |
| المصدر: | Journal of Chemical Theory and Computation 2023 19 (20), 7287-7299 |
| سنة النشر: | 2023 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Condensed Matter - Materials Science, Physics - Chemical Physics |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1021/acs.jctc.3c00848 |
| URL الوصول: | http://arxiv.org/abs/2308.00665 |
| رقم الانضمام: | edsarx.2308.00665 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1021/acs.jctc.3c00848 |
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