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Title: Basc: Applying Bayesian optimization to the search for global minima on potential energy surfaces
We present a novel application of Bayesian optimization to the field of surface science: rapidly and accurately searching for the global minimum on potential energy surfaces. Controlling molecule-surface interactions is key for applications ranging from environmental catalysis to gas sensing. We present pragmatic techniques, including exploration/exploitation scheduling and a custom covariance kernel that encodes the properties of our objective function. Our method, the Bayesian Active Site Calculator (BASC), outperforms differential evolution and constrained minima hopping - two state-of-the-art approaches - in trial examples of carbon monoxide adsorption on a hematite substrate, both with and without a defect.
Authors:
; ;
Award ID(s):
1355406
Publication Date:
NSF-PAR ID:
10023538
Journal Name:
33rd International Conference on Machine Learning, ICML 2016
Sponsoring Org:
National Science Foundation
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