Search methods for inorganic materials crystal structure prediction

Crystal structure prediction (CSP) is the problem of determining the most stable crystalline arrangements of materials given their chemical compositions. In general, CSP methodologies include two algorithmic steps, namely a method for assessing material stability of any given design, and a search algorithm for exploring the design space. For inorganic crystals, in particular, the most critical aspect is to develop an effective search algorithm. This paper summarizes previous research and discusses recent progress in search methods developed for inorganic CSP. Empirical methods, guided-sampling algorithms, and more recent data-driven approaches are discussed. Additionally, we describe a mathematical optimization-based search paradigm that has been recently introduced as an alternative CSP approach. A semiconductor nanowire design approach is then presented to illustrate this paradigm.

Automated summary

Crystal structure prediction (CSP) involves determining the most stable crystalline arrangements of materials based on their chemical compositions. CSP methodologies typically involve assessing material stability and conducting a search for exploring the design space. Developing an effective search algorithm is crucial, especially for inorganic crystals. Previous research on inorganic CSP has discussed empirical methods, guided-sampling algorithms, data-driven approaches, and a mathematical optimization-based search paradigm.