Combinations of density functionals for accurate molecular properties of Be/W/H compounds. (September 2021)

• Record Type: Journal Article
• Authors: Chen, L.; Probst, A.; Kaiser, A.; Süß, D.; Mauracher, A.; Maihom, T.; Probst, M.
• Is Part Of: Nuclear materials and energy. Volume 28(2021)
• Publication Date: 2021-09
• Subjects: Machine learning -- Density functional theory -- Be/W/H compounds -- Plasma wall interaction -- ITER; Nuclear energy -- Periodicals; Nuclear fuels -- Periodicals; Nuclear reactors -- Materials -- Periodicals; Radioactive substances -- Periodicals; 621.4833
• DOI: 10.1016/j.nme.2021.101026
• Languages: English
• ISSNs: 2352-1791

Abstract

Highlights: A recipe for providing accurate properties of fusion relevant Be and W compounds. A vast number of density functional combinations is searched by means of LASSO. Linear combinations of two or three functionals are better than each single one. We improve dealing with fusion relevant Be and W compounds at nearly no extra cost. Energies and structure of Be/W compounds as examples but the method is general. Abstract: 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, Ben Hm, Wn, Wn Bem, and Wn Hm 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.