Identifying efficient and inexpensive hydrodesulfurization catalysts through Machine Learning-Assisted analysis of Metal-Sulfur bonds in transition metal sulfides

This paper investigates the potential of transition metal sulfides (TMSs) as hydrodesulfurization (HDS) catalysts through electronic structure and bonding analysis. The HDS mechanism focuses on breaking and regenerating metal-sulfur (M-S) bonds at sulfur vacancies, known as active sites adhering to the Sabatier principle. Machine learning was employed to identify TMSs with crystal orbital Hamilton populations (COHP) similar to known catalysts like OsS2 or Co-Mo-S, facilitating novel catalyst development. However, the weak thiophene adsorption on the top sites of the first-row TMS surfaces limits their catalytic potential. To address this limitation, adsorption on bridge sites was explored, leading to the discovery of V5S4 and Ti8Cu3S16 as highly promising HDS catalysts. These findings suggest that first-row TMSs can serve as cost-effective and efficient HDS catalysts, offering valuable insights for the development of other catalysts.