Predicting the minimum energy pathway of 1H to 1T phase transition of select 2D transition metal dichalcogenides via density functional theory and machine learning approach

• Published in: Journal of physics. Conference series 2024-07, Vol.2793 (1), p.12017
• Main Authors: Diego, KY M, Putungan, D B, Santos-Putungan, A B
• Format: Article
• Language: English
• Subjects: Algorithms; Chalcogenides; Density functional theory; Error analysis; Gaussian process; Machine learning; Phase transitions; Predictions; Regression; Transition metal compounds
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2793/1/012017

This study predicts the minimum energy pathways (MEPs) and transition barriers of two-dimensional transition metal dichalcogenides (TMDs) during their 1H to 1T structural phase transitions. The investigation utilizes density functional theory (DFT) calculations and machine learning algorithms to predict the MEPs and transition barriers. Six TMDs, namely NbSe 2 , ScS 2 , ScSe 2 , TiTe 2 , VS 2 , and VSe 2 , are selected for analysis. The DFT calculations provide reference values for comparison with the machine learning predictions. The transition barriers obtained through DFT calculations range from 0.13 eV to 0.72 eV. The machine learning models, including Linear Regression, Random Forest Regression, K-Nearest Neighbor Regression, and Gaussian Process Regression, demonstrate varying degrees of accuracy in predicting the transition barriers. Gaussian Process Regression achieved the highest accuracy among all ML models, with the absolute mean error spanning all systems being 0.0093. It has been shown that the features that were identified in the study are sufficient for the prediction of the MEPs and transition barriers of TMDs using machine learning. Overall, this study contributes to the understanding of TMD phase transitions and provides insights into the integration of machine learning in materials science research.