  Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of
several possible structures, with the most common being the trigonal prismatic
and octahedral symmetry phases. Since the structure determines the electronic
properties, being able to predict phase-preferences of TMDs from just the
knowledge of the constituent atoms is highly desired, but has remained a
long-standing problem. In this study, we applied high-throughput quantum
mechanical computations with machine learning algorithms to solve this old
problem. Our analysis provides insights into determining physiochemical factors
that dictate the phase-preference of a TMD, identifying and going beyond the
attributes considered by earlier researchers in predicting crystal structures.
A knowledge of these underlying physiochemical factors not only helps us to
rationalize, but also to accurately predict structural preferences. We show
that machine learning algorithms are powerful tools that can be used not only
to find new materials with targeted properties, but also to find connections
between elemental attributes and the target property/properties that were not
previously obvious.
