(538e) Developing a Fingerprinting and Machine Learning Framework Linking Structural Disorder to Oxidation Behavior in Metal Grain Boundaries for CO2 Electrocatalysis
The structural disorder distinguishing different GBs is affected by metal composition and structure, as well as metal-oxygen interfacial interactions resulting from deriving GBs from metal oxides. Thus, this work aspires to understand how GB metal properties and oxidation impact GB structural disorder, as well as linking structural disorder to relative GB energetic favorability. This aspiration is pursued by developing a fingerprinting method categorizing GBs by their radial distribution functions (RDFs), then applying those RDFs to a Markov chain Monte Carlo (MCMC) machine learning (ML) analysis generalizing the link between GB structural disorder and energetics. Firstly, an initial screening of metal GB candidates will be performed via molecular mechanics, indicating which structural properties best distinguish GB systems and which best correlate with energetics. Subsequently, density functional theory (DFT) calculations will be completed on screened structures and their oxidized analogues. A MCMC ML analysis will then discern the impact of oxidation on GB structural disorder and energetic favorability. By generalizing these structure-energy relationships, a fundamental understanding of how oxidation can tune GB structure to modify electrochemical product selectivity can be achieved.