Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning

The
increasing interest and research on two-dimensional (2D) materials
has not yet translated into a reality of diverse materials applications.
To go beyond graphene and transition metal dichalcogenides for several
applications, suitable candidates with desirable properties must be
proposed. Here we use machine learning techniques to identify thermodynamically
stable 2D materials, which is the first essential requirement for
any application. According to the formation energy and energy above
the convex hull, we classify materials as having low, medium, or high
stability. The proposed approach enables the stability evaluation
of novel 2D compounds for further detailed investigation of promising
candidates, using only composition properties and structural symmetry,
without the need for information about atomic positions. We demonstrate
the usefulness of the model generating more than a thousand novel
compounds, corroborating with DFT calculations the classification
for five of these materials. To illustrate the applicability of the
stable materials, we then perform a screening of electronic materials
suitable for photoelectrocatalytic water splitting, identifying the
potential candidate Sn2SeTe generated by our model, and
also PbTe, both not yet reported for this application.