Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

A long-standing challenge in the design of single atom alloys (SAAs), for catalytic applications, is the determination of a feature space that maximally correlates to molecular binding energies per the Sabatier principle. The more representative a feature space is of the underlying binding properties, the greater the predictive capability of a given machine learning (ML) algorithm. Moreover, the greater the diversity and range of SAA impurities/sites examined, the greater the difficulty in arriving at such a predictive feature. In this work, we undertake to examine the degree to which adsorbat... Mehr ...