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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Accelerating the search for global minima on potential energy surfaces using machine learning.

S F Carr1, R Garnett2, C S Lo1

  • 1Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA.

The Journal of Chemical Physics
|October 27, 2016
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This study introduces a new framework to speed up finding stable molecule-surface structures. It uses Bayesian inference to predict energies faster, improving catalysis and gas sensing research.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Surface Science

Background:

  • Controlling molecule-surface interactions is crucial for applications like catalysis and gas sensing.
  • Identifying stable adsorbate-surface structures requires finding the global minimum on potential energy surfaces.
  • Current methods can be computationally intensive, necessitating faster approaches.

Purpose of the Study:

  • To present a computational framework for accelerating the search for global minima on potential energy surfaces.
  • To enable faster prediction of converged density functional theory (DFT) potential energies.
  • To optimize the discovery of stable adsorbate-surface structures for chemical applications.

Main Methods:

  • Developed a framework integrating Bayesian inference for predicting DFT potential energies.
  • Utilized Bayesian optimization within the Bayesian Active Site Calculator.
  • Applied global optimization methods to identify adsorption sites on material surfaces.

Main Results:

  • Demonstrated the framework's performance on a hematite (Fe2O3) surface.
  • Identified adsorption sites for hydrocarbons on the rutile TiO2 (110) surface.
  • Showcased accelerated prediction of converged DFT energies with fewer self-consistent field iterations.

Conclusions:

  • The presented framework significantly accelerates the search for stable adsorbate-surface structures.
  • This approach enhances the efficiency of computational studies in catalysis and gas sensing.
  • The method provides a powerful tool for discovering and characterizing molecule-surface interactions.