Enhancing Discovery and Understanding of Atomically Dispersed Single Atom Catalysts for CO2 Reduction Reaction with Density Functional Theory and Machine Learning
Authors: Davis Megan Christina , Matanovic Ivana , Kort-Kamp Wilton , Zelenay Piotr , Holby Edward F. 
Published: December 2024

Atomically dispersed M-N-C catalysts are a promising class of materials for efficiently reducing CO2 to valuable feedstock chemicals such as carbon monoxide and ethylene. However, for such catalysts to achieve commercial viability it is necessary to optimize several properties in tandem such as activity towards the CO2 reduction reaction, Faradaic efficiency towards selected products, and in situ stability. This complex multi-objective optimization problem cannot be effectively tackled with costly trial-and-error experimentation alone. Therefore, computational modeling is necessary to accelerate fundamental understanding and to aid in identifying viable active sites in silico before preparation of down-selected candidate materials is attempted in the laboratory. Density functional theory (DFT) can be utilized to model catalytic surfaces and obtain useful electrochemical properties (e.g., free energies of reaction), but an advanced framework is necessary to effectively explore the large chemical space of possible permutations of different transition metals, proximity of metal centers, defects, and other structural considerations. Furthermore, machine learning may be leveraged to accelerate materials understanding and discovery for CO2RR beyond what is feasible for DFT alone.
In this talk, we will discuss our library-based approach for understanding structure-to-function relationships in these materials and identifying novel active sites. This library is built using our high-throughput DFT framework to calculate different properties (e.g., adsorption energies) for a large number of active sites. The structures of the active sites are combinatorically generated based on permutations of transition metals, defects, and other structural considerations. Our automated framework combines DFT and cheminformatics tools to autonomously run DFT calculations as well as to collect, process, and store the results of our simulations and to evaluate different structural descriptors. We will also discuss considerations made for incorporating solvation and electrification effects in a robust manner into our framework.
This library is then used as the basis for the development of machine learning models, which learn to map from descriptor-based structural representations of these surfaces (e.g., metal center, type of defect) to target properties, thereby accelerating understanding of structure to function relationships for M-N-C materials. Game theoretical methods are used to provide model interpretability and insight for how individual structural properties contribute to properties of interest, such as activity or overpotential. We discuss the use of our trained model, capable of screening millions of unique chemistries, in discovering novel M-N-C active sites with optimized properties. By thus combining DFT and machine learning, we aim both to provide fundamental understanding of CO2 reduction activity and durability and to discover new viable active sites. Examples of our modeling strategies applied to direct air capture of CO2 utilizing similar workflows will also be discussed.