  Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of the
several possible structures, with 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 combine high-throughput quantum
mechanical computations with machine learning algorithms to discover novel TMDs
and study their chemical stability, as well as their phase preferences. Our
analysis provides insights into determining physiochemical factors that dictate
the phase-preference of a TMD, identifying and even going beyond the attributes
considered by earlier researchers in predicting crystal structures. We show
that the 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.
