1 research outputs found
The Synergistic Effect between Metal and Sulfur Vacancy to Boost CO<sub>2</sub> Reduction Efficiency: A Study on Descriptor Transferability and Activity Prediction
Both metal center
active sites and vacancies can influence the
catalytic activity of a catalyst. A quantitative model to describe
the synergistic effect between the metal centers and vacancies is
highly desired. Herein, we proposed a machine learning model to evaluate
the synergistic index, PSyn, which is
learned from the possible pathways for CH4 production from
CO2 reduction reaction (CO2RR) on 26 metal-anchored
MoS2 with and without sulfur vacancy. The data set consists
of 1556 intermediate structures on metal-anchored MoS2,
which are used for training. The 2028 structures from the literature,
comprising both single active site and dual active sites, are used
for external test. The XGBoost model with 3 features, including electronegativity,
d-shell valence electrons of metal, and the distance between metal
and vacancy, exhibited satisfactory prediction accuracy on limiting
potential. Fe@Sv-MoS2 and Os@MoS2 are predicted
to be promising CO2RR catalysts with high stability, low
limiting potential, and high selectivity against hydrogen evolution
reactions (HER). Based on some easily accessible descriptors, transferability
can be achieved for both porous materials and 2D materials in predicting
the energy change in the CO2RR and nitrogen reduction reaction
(NRR). Such a predictive model can also be applied to predict the
synergistic effect of the CO2RR in other oxygen and tungsten
vacancy systems