Predicting phase preferences of transition metal dichalcogenides using machine learning techniques

Unlike most other 2D materials, transition metal dichalcogenides (TMDs) can adopt several phases that possess dramatically different electronic structure properties. One of the natural questions that arises is: What dictates the observed phase preference of TMDs? In this talk, I will discuss our recent work [Phys. Rev. Materials 6, 094007 (2022)], which combines high-throughput quantum mechanical computations with machine learning algorithms to address this old problem. Our analysis not only rediscovers known physicochemical attributes considered by earlier researchers, but goes beyond these attributes to discover other factors that were not previously known to influence the structural preferences displayed by TMDs. This work demonstrates how machine learning can be used to tackle old problems in Condensed Matter Physics.