Date of Award

Winter 12-2015

Author's School

School of Engineering & Applied Science

Author's Department

Computer Science & Engineering

Degree Name

Master of Science (MS)

Degree Type



Machine learning is a rapidly evolving field in computer science with increasingly many applications to other domains. In this thesis, I present a Bayesian machine learning approach to solving a problem in theoretical surface science: calculating the preferred active site on a catalyst surface for a given adsorbate molecule. I formulate the problem as a low-dimensional objective function. I show how the objective function can be approximated into a certain confidence interval using just one iteration of the self-consistent field (SCF) loop in density functional theory (DFT). I then use Bayesian optimization to perform a global search for the solution. My approach outperforms the current state-of-the-art method, constrained minima hopping, for CO on ferric oxide by a factor of 75 to 1. This thesis is the first documented application of Bayesian optimization to surface science.


English (en)


Roman Garnett

Committee Members

Cynthia Lo Roman Garnett Michael Brent


Permanent URL: https://doi.org/10.7936/K78C9TJ1