Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation

TL;DR

This paper introduces a machine learning approach using a physics-inspired graph representation to efficiently predict binding motifs and adsorption enthalpies of complex adsorbates on transition metals and alloys, aiding catalytic material discovery.

Contribution

The study develops a data-efficient graph kernel and Gaussian Process Regression model that accurately predicts binding motifs and enthalpies, including for alloys and out-of-domain transition metals.

Findings

Model predicts binding motifs with high accuracy.

Effective for alloys and out-of-domain metals.

Enables active learning with uncertainty estimation.

Abstract

Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces, e.g. alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals (TMs) and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian Process Regression. The model shows good predictive performance, not only for the elemental TMs on which it was trained, but also for an alloy based on these TMs. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain TM. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.