| Autor: |
L. Chen, A. Probst, A. Kaiser, D. Süß, A. Mauracher, T. Maihom, M. Probst |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Nuclear Materials and Energy, Vol 28, Iss , Pp 101026- (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2352-1791 |
| DOI: |
10.1016/j.nme.2021.101026 |
| Popis: |
Beryllium and tungsten species can form by plasma-induced erosion of the walls of a fusion reactor. Accurate and fast evaluation of energies and geometries of Be/W/H compounds is needed for direct molecular dynamics of the plasma-wall interface or for generating training data for potential energy surfaces. Density functional calculations can serve this purpose but within the magnitude of suggested functionals no single one is the obvious choice. We investigate the performance of compact linear combinations of density functionals on some Be/W/H compounds by statistical machine learning.Equilibrium geometries and atomization energies of the neutral molecules Ben, BenHm, Wn, WnBem, and WnHm with m+n≤4 from 16 density functionals were compared with their counterparts from coupled cluster calculations. A statistical learning method was used to find combinations of these functionals that can accurately reproduce the results of the much more costly coupled cluster method. Linear models of two or three functionals predict the coupled cluster data quite well with an accuracy of 98.2% and 99.7%, respectively, much better than any of the functionals alone. This simple procedure is, for example, useful for the calculation of species concentrations in reaction networks of molecules close to plasma facing components in a fusion device. Accurate molecular energies are crucial for determining the species concentrations which depend exponentially on their differences. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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