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Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning
Zachary W Ulissi1,2, Aayush R Singh1,2, Charlie Tsai1,2
1SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University , Stanford, California 94305, USA.
The Journal of Physical Chemistry Letters
|August 26, 2016
Summary
Machine learning predicts surface free energies, simplifying the creation of accurate surface phase diagrams. This approach reduces computational costs and improves the understanding of surface chemistry in catalysis.
Area of Science:
- Surface Science
- Computational Chemistry
- Electrocatalysis
Background:
- Surface phase diagrams are crucial for understanding surface chemistry in electrochemical catalysis.
- Traditional methods for constructing these diagrams rely on intuition or complex computational approaches, risking inaccuracies or implementation difficulties.
Purpose of the Study:
- To develop a machine learning approach for accurate and efficient generation of surface phase diagrams.
- To overcome limitations of traditional methods in predicting complex adsorbate coverages and configurations.
Main Methods:
- Utilized a Gaussian process regression model to predict the free energy of various adsorbate coverages on surfaces.
- Applied the model to reconstruct the Pourbaix diagram for IrO2(110) and analyze the MoS2 surface.
Main Results:
- The machine learning approach accurately predicted free energies for all possible adsorbate coverages.
- Reconstructed the Pourbaix diagram for IrO2(110) using significantly fewer electronic structure calculations (20 vs. ~90).
- Demonstrated similar computational efficiency for the MoS2 surface.
Conclusions:
- The developed machine learning method offers a rational, simple, and systematic way to generate accurate surface free-energy diagrams.
- This approach reduces computational resources required for surface phase diagram construction.
- Enables a more comprehensive understanding of surface chemistry in electrochemical catalysis.
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