Density Functional Theory (DFT) Based Machine & Deep Learning Strategies for Rational Screening and Directed Design of Catalytic Promoter Materials and Supported Nanoparticle Interfaces for Renewable Fuel Applications
Previous experimental work observed the presence of molybdenum carbide or an oxy-carbide phase along with the oxide with potential implications to the HDO mechanistic chemistry and overall activity. To understand the individual role of the oxide and the carbide phase for HDO chemistry, molybdenum carbide supported on the oxide was modeled based on earlier HRTEM characterization. A novel two-step approach was implemented using Deep Learning techniques such as artificial neutral networks (ANN) to account for the absence of detailed structural and phase composition information, and also to reduce the computational tediousness of sifting through all possible combinations of nano-particle support interfaces using ab-initio DFT calculations. Preliminary results suggest a drastic reduction in the total computational time required to predict the structure and thermodynamic feasibility of the interface with this approach. Oxygen vacancy formation, and H2 spillover at the interface were also investigated.
The ability to rapidly screen promoters for MoO3 and rationally design mixed oxy-carbide interfaces will ultimately lead the way to rationally designing novel HDO catalysts for the commercial upgrade of biomass to chemicals and fuels.
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