Machine learning-assisted screening of SA-FLP dual-active-site catalysts for the production of methanol from methane and water

Abstract

One-step direct production of methanol from methane and water (PMMW) under mild conditions is challenging in heterogeneous catalysis owing to the absence of highly effective catalysts. Herein, we designed a series of “Single-Atom” - “Frustrated Lewis Pair” (SA-FLP) dual active sites for the direct PMMW via density functional theory (DFT) calculations combined with a machine learning (ML) approach. The results indicate that the nine designed SA-FLP catalysts are capable of efficiently activate CH₄ and H₂O and facilitate the coupling of OH* and CH₃* into methanol. The DFT-based microkinetic simulation (MKM) results indicate that CH₃OH production on Co₁-FLP and Pt₁-FLP catalysts can reach the turnover frequencies (TOFs) of 1.01 × 10⁻³ s^–1 and 8.80 × 10⁻⁴ s^–1, respectively, which exceed the experimentally reported values by three orders of magnitude. ML results unveil that the gradient boosted regression model with 13 simple features could give satisfactory predictions for the TOFs of CH₃OH production with RMSE and R² of 0.009 s^–1 and 1.00, respectively. The ML-predicted MKM results indicate that four catalysts including V₁-, Fe₁-, Ti₁-, and Mn₁-FLP exhibit higher TOFs of CH₃OH production than the value that the most relevant experiments reported, indicating that the four catalysts are also promising catalysts for the PMMW. This study not only develops a simple and efficient approach for design and screening SA-FLP catalysts but also provides mechanistic insights into the direct PMMW.