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Free keywords:
Physics, Chemical Physics, physics.chem-ph
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.
