Atomic configurations for materials research: A case study of some simple binary compounds

Data obtained from computational studies are crucial in building the necessary infrastructure for materials informatics. This computational foundation supplemented with experimental observations can then be employed in the extraction of possible hidden structure–property relationships through machine learning. There are limited attempts to sample the materials configuration space, even for the simplest chemical formulas. Advances in computational methods have now made it possible to accomplish this task. In this study, we analyze four chemical formulas, i.e., BSb, AlSb, MgSi2, and Sn3S, using first-principles computations. We show that numerous thermodynamically more stable crystal structures can be predicted computationally for these relatively simple chemical formulas, while the configuration space can be significantly and effectively mapped out. This approach allows for the prediction of new ground state structures, thereby expanding the available data on these materials. It also provides an understanding of the underlying potential energy topography and adds quality data for materials informatics.