Accelerated Search of Catalysts Using Density Functional Theory and Machine Learning

Abstract

The need for clean and renewable energy resources has propelled the interest in designing new catalysts producing energy from renewable resources and alternate cleaner fuels such as hydrogen, methane, ammonia, ethylene, etc. Despite an extensive search, finding an efficient catalyst in terms of activity, selectivity, stability, and cost is still far from reality. We attempt to address some of these challenges by combining the density functional theory (DFT) and machine learning (ML). We report a carbon-nitride and transition metal (TM) based single atom catalyst (TM-SAC) for electrocatalytic nitrogen reduction reaction (eNRR). Among all the TM-based SACs, Mo- and W-SACs are found to be highly active and selective for eNRR over competing hydrogen evolution reaction (HER). The higher activity is attributed to the optimum stability of nitrogen and other eNRR intermediates over the SAC. Further, we addressed one of the major issues of CO2 reduction reaction (CO2RR) catalysts i.e., their selectivity. Our work presented a simple solution where the selectivity can be tuned by varying alloy surface composition. This arises due to the change in electronic structure of the bimetallic catalyst, simultaneously changing the d-band center of the metals. Modifying the surface composition is further employed to enhance the activity of Pt-Pd based alloys for methanol oxidation reaction (MOR) and oxygen reduction reaction (ORR) for fuel-cell applications. Surface Pd helped altering the thermodynamics of the reaction, which is also confirmed by experimental validation. Importantly, the d-band center emerged as the descriptor for the activity of the proposed catalysts. Owing to the complexity and resource extensive calculations involved in determining the catalytic activity of the alloy catalyst, we employed machine learning approach to estimate the d-band center of core-shell nanoparticles, a measure of catalytic performance. The machine learning model based on recommender-system uses data calculated from DFT for d-band center and a collection of accessible elemental information. This model recommends bimetallic nanoparticles with an optimum range of d-band center and capture the pair-wise properties of the metals present in core and shell of the nanoparticles. Further, we developed an efficient framework to search for a water-splitting photocatalyst by using metal phosphorus trichalcogenides (MPX3) class of compounds. The high-throughput study corroborates the role of accurate band gap, band-edge alignment, optical transitions, and charge carrier mobilities of the materials. The thermodynamics of the redox reactions confirms the photocatalytic efficiency of the screened catalysts. The results of our study pave way to overcome some of the critical challenges related to catalysts by effectively addressing the selectivity and activity problems of both existing and newly designed catalysts.