(582d) Computational Design of Metal Oxide Nanoclusters for Selective Partial Oxidation of Hydrocarbons
AIChE Annual Meeting
2022 Annual Meeting
Catalysis and Reaction Engineering Division
Data Science & Machine Learning Approaches to Catalysis III: Applications of Machine Learning to Heterogeneous Catalysis: From Porous Materials to Cluster Catalysis
Thursday, November 17, 2022 - 8:54am to 9:12am
The past few years have witnessed a great improvement in the precise control over size and composition of metal oxide nanocluster catalysts toward important energy and sustainability applications, such as selective partial oxidation of hydrocarbons. Using theoretical modeling approaches, some recent studies reported by our group have revealed the reaction mechanisms of di-copper and di-iron oxide nanoclusters grown using site-selective atomic layer deposition (SS-ALD) for direct conversion of methane to the value-added product methanol under mild conditions.1,2 In this work, using density functional theory (DFT) approaches, we studied the growth, possible geometries, and catalytic performances of a series of tetra-copper oxide nanoclusters in the context of methane partial oxidation. Taking the most active tetra-copper oxide nanocluster as a base-structure, we substituted Cu sites with various metals (Mn, Fe, Co, Ni, Zn) with the goal of rationally tailoring the geometric and electronic structures of the active centers. This significantly expands the design space of metal oxide nanoclusters, making full investigation by DFT time consuming. Thus, we further explored the intrinsic structure-reactivity relationships by training and testing a machine learning model to predict the target properties (computed binding and activation energies of methane) from various geometric and electronic structural features. Moreover, importance analysis provided us with useful information on which features play decisive roles in determining the reactivity of methane conversion, offering theoretical guidance for future design of nanocluster catalysts. These insights highlight the effectiveness of computational modeling and data science approaches for accelerated materials discovery.
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