Predicting 2D Materials with Machine Learning

The increasing interest and research on two-dimensional materials has not yet translated into a reality of diverse materials applications. To go beyond graphene and transition metal dichalcogenides for several applications, candidates with desirable properties must be proposed. 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 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 we perform a screening of materials suitable for photoelectrocatalytic water splitting, identifying the potential candidate Sn₂SeTe generated by our model and PbTe, both not yet reported for this application.