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Predicting catalytic pathways for Thiophenol decomposition on TM-doped MoS2: a comparative machine learning study.

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Title: Predicting catalytic pathways for Thiophenol decomposition on TM-doped MoS2: a comparative machine learning study.
Authors: Zhang, Meng1 (AUTHOR), Zhai, Yingjiao1 (AUTHOR) yjzhai@cust.edu.cn, Chu, Xueying1 (AUTHOR), Li, Jinhua1 (AUTHOR), Liu, Fujun1 (AUTHOR) fjliu@cust.edu.cn
Source: Nanotechnology. 2026, Vol. 37 Issue 18, p1-15. 15p.
Subjects: Thiophenol, Machine learning, Activation energy, Transition metal catalysts, Random forest algorithms, Transition metals, Catalytic activity
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:09574484
DOI:10.1088/1361-6528/ae61b9