1 research outputs found
Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning
Surface
phase diagrams are necessary for understanding surface
chemistry in electrochemical catalysis, where a range of adsorbates
and coverages exist at varying applied potentials. These diagrams
are typically constructed using intuition, which risks missing complex
coverages and configurations at potentials of interest. More accurate
cluster expansion methods are often difficult to implement quickly
for new surfaces. We adopt a machine learning approach to rectify
both issues. Using a Gaussian process regression model, the free energy
of all possible adsorbate coverages for surfaces is predicted for
a finite number of adsorption sites. Our result demonstrates a rational,
simple, and systematic approach for generating accurate free-energy
diagrams with reduced computational resources. The Pourbaix diagram
for the IrO<sub>2</sub>(110) surface (with nine coverages from fully
hydrogenated to fully oxygenated surfaces) is reconstructed using
just 20 electronic structure relaxations, compared to approximately
90 using typical search methods. Similar efficiency is demonstrated
for the MoS<sub>2</sub> surface