Adsorbate-Dependent
Electronic Structure Descriptors
for Machine Learning-Driven Binding Energy Predictions in Diverse
Single Atom Alloys: A Reductionist Approach
posted on 2024-03-08, 20:13authored byJavad Shirani, Julio J. Valdes, Alain B. Tchagang, Kirk H. Bevan
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 adsorbate electronic structure properties
might address this challenge, in a distinct departure from the traditional
substrate electronic structure feature construction found in the catalysis
literature. Specifically, as a model system, we explore the predictive
capacity of the p-orbital projected density of states (PDOS) pertaining
to the adsorption of CO molecules on a wide range of SAA substrates,
impurity embeddings, and vicinal cuts. This analysis is executed in
two parts. First, we explore the degree to which the entire PDOS distribution,
in the form of an energy-dependent vector, can predict binding energies.
Subsequently, guided by a rigorous intrinsic dimensionality analysis,
uniform manifold approximation and projection visualization, and chemical
intuition, we are able to reduce the predictive feature space to just
three physical quantities based on semicore level properties and charge
filling of the adsorbate–as embedded with the PDOS distribution.
This near-intrinsic feature space and the PDOS distribution are both
shown to provide significant improvements in predictive accuracy when
coupled with regression-based ML methods, even when tackling highly
diverse chemical datasets. The results of this analysis both further
substantiate the transferability characteristics of SAAs and indicate
that adsorbate-based electronic structure features (from either relaxed
or unrelaxed chemical datasets) are powerful tools in the prediction
of catalytic binding energies in such systems. They also underscore
the predictive benefit of finding a feature space with a dimension
equal to the intrinsic dimensionality of the data that can maximally
correlate with the physical property under investigation when employing
ML methods in catalysis studies.