Predicting phase preferences of two-dimensional transition metal dichalcogenides using machine learning

Abstract

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.

References in corpus (5)

• Two Dimensional Atomic Crystals
• Quantum Spin Hall Effect and Topological Field Effect Transistor in Two-Dimensional Transition Metal Dichalcogenides
• Computational 2D Materials Database: Electronic Structure of Transition-Metal Dichalcogenides and Oxides
• Engineering the structural and electronic phases of MoTe2 through W substitution
• Layer-by-layer epitaxial growth of scalable WSe2 on sapphire by molecular-beam epitaxy

Cited by in corpus (2)

• Design guidelines for two-dimensional transition metal dichalcogenide alloys
• Optimum surface-passivation schemes for near-surface spin defects in silicon carbide