Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery

11 May 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split surface into three types of site, first nearest neighbor of adsorbates and second nearest neighbor in same layer and sublayer. From this representation and machine learning regression model, we achieve reasonable accuracy (0.120 eV for CO* and 0.105 eV for H*) with quick training (~200 sec using CPU). Because our representation does not require density functional calculation and atomic structure modelling, it can predict binding energies of possible active motifs without time-consuming steps.

Keywords

CO2 reduction
H2 evolution
binding energy prediction machine-learning models

Supplementary materials

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