Report
Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models
| العنوان: | Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models |
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| المؤلفون: | Speckhard, Daniel T., Carbogno, Christian, Ghiringhelli, Luca, Lubeck, Sven, Scheffler, Matthias, Draxl, Claudia |
| سنة النشر: | 2023 |
| المجموعة: | Condensed Matter Physics (Other) Statistics |
| مصطلحات موضوعية: | Physics - Computational Physics, Condensed Matter - Materials Science, Statistics - Machine Learning |
| الوصف: | The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached with an infinitely large basis set, i.e., in the limit of a complete basis set (CBS). Our aim in this work is to find a machine-learning model that extrapolates finite basis-size calculations to the CBS limit. We start with a data set of 63 binary solids investigated with two all-electron DFT codes, exciting and FHI-aims, which employ very different types of basis sets. A quantile-random-forest model is used to estimate the total-energy correction with respect to a fully converged calculation as a function of the basis-set size. The random-forest model achieves a symmetric mean absolute percentage error of lower than 25% for both codes and outperforms previous approaches in the literature. Our approach also provides prediction intervals, which quantify the uncertainty of the models' predictions. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2303.14760 |
| رقم الانضمام: | edsarx.2303.14760 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |