Predicting Catalytic Pathways for Thiophenol Decomposition on TM-Doped MoS2: A Comparative Machine Learning Study.

Thiophenol (TP), a high-toxicity compound prevalent in pharmaceuticals and industrial products, necessitates efficient catalytic decomposition methods. While two-dimensional MoS₂ offers a promising large surface area for catalysis, its inert basal plane and weak TP adsorption energy (1.60 eV) limit its efficacy. To address this, we designed a single-atom catalyst via transition metal (TM) doping of MoS₂. Using first-principles calculations, we demonstrate that TM doping drastically alters the local charge density, significantly enhancing adsorption and catalytic activity for TP decomposition into H₂ and H₂S. Our results identify Ni-doped MoS₂ as kinetically favored and Co-doped MoS₂ as thermodynamically favored for the reaction. Furthermore, we evaluated four machine learning models (Linear Regression, K-Nearest Neighbors, Random Forest, and Gradient Boosting Regression Trees) for predicting activation barriers and reaction energies. Random Forest regression emerged as the most accurate predictor. This work provides a theoretical framework for eliminating toxic organic pollutants and establishes a machine-learning-guided strategy for accelerating catalyst screening.