Machine learning predicted inelasticity in defective two-dimensional transition metal dichalcogenides using SHAP analysis

Abstract

The manipulation of crystallographic defects in 2H-transition metal dichalcogenides (2H-TMDCs), whether pre- or post-synthesis, has garnered significant interest recently, as it holds the promise of tuning the thermal, chemical, and electronic properties of these materials. However, such desirable improvements often come at the cost of deteriorated elastic and inelastic properties, which may lead to serious concerns considering mechanical reliability issues. Therefore, persistent efforts are needed to explore the effects of energetically favorable vacancies on the mechanical properties of 2D TMDCs for an effective tuning of material properties for versatile applications. In this context, machine learning models trained on data based on molecular models can not only provide fast, efficient material models but also unearth crucial structure–property relations. However, such efforts are at an early stage of development. In this study, machine learning and deep learning techniques are used to analyze the mechanical properties of 2D transition metal dichalcogenides (TMDCs) in both pristine and defect forms. The goal is to predict failure stress, strain to failure, and strength based on chirality and strain. Various crystallographic defects were considered, and extensive molecular dynamics simulations were performed. XGBoost and densely connected neural network (DenseNet) algorithms were used to make accurate state-of-the-art predictions, and comparative evaluation and Shapley value analysis of both models are presented to improve interpretability.