Machine learning enabled electronic band-edge shapes and properties prediction of 2D transition metal dichalcogenide alloys

Abstract

Conventional ab initio calculations based on density functional theory (DFT), though accurate, are computationally expensive and time-consuming, paving the way for the emergence of data-driven models. While most existing works are confined to scalar electronic property estimation, such as band gaps or stability, whole-band structure prediction has remained relatively unexplored. Here, we derived a dataset from DFT computations of monolayer transition metal dichalcogenide (TMD) alloys composed of Tungsten (W), Molybdenum (Mo), and chalcogen (X) atoms, namely Sulfur (S), Selenium (Se), and Tellurium (Te), and developed a decision tree based model – Extra Trees, for predicting the conduction and valence band structures for binary and ternary TMDs. The model illustrated the dependency of band structure on crucial features, such as compositional ratios, lattice constant, and Fermi energy. We trained separate models for the conduction and valence bands to capture non-linear feature-target relationships. We note that we performed a comparative analysis of different types of machine learning models, and found that the Extra Trees model performed the best. Low mean squared error (MSE <0.0006) and high correlation values (Pearson correlation coefficient≫0.99) with respect to DFT simulated band energies for all the TMD alloys of the dataset validate the excellent predictive power of the model. Additionally, the model made excellent predictions for the compositions beyond the training dataset, showcasing the robustness of the model as a substitute for computationally-heavy DFT simulation. We emphasize that the proposed whole band model is more informative than the existing band gap-only models, since it provides the band gaps and enables the derivation of secondary electronic properties, such as effective carrier mass by curvature fitting. As such, we argue that our work in this study will benefit the materials research in nanoscience by providing a faster and insightful alternative framework to computer-based simulations.