Year: 2022
Type: article
Abstract: The development of new materials by computational materials science relies to a large degree on the prediction of material properties by simulation at different time and length scales.A common challenge at the atomic scale is the need for large numbers of calculations in order to sample, e.g., different chemical compositions, different crystal structures or different simulation settings.Typical examples of the required high-throughput calculations are (i) the sampling of the combinatorial space of structure and composition for determining the most stable structure of a mixture of chemical elements, (ii) the generation of a data set for constructing an interatomic interaction model or (iii) the generation of a data set for inferring properties by machine learning.Depending on the problem at hand, the number of required calculations may range from hundreds to millions.
DOI: 10.21105/joss.04719
Institution: Ruhr University Bochum
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4310235764