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

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