(370h) BASC: Applying Bayesian Machine Learning Techniques to the Search for Global Minima on Potential Energy Surfaces

Lo, C. S., Washington University in St. Louis
Carr, S. F., Washington University in St. Louis
Controlling molecule-surface interactions is key for chemical applications ranging from catalysis to gas sensing. We present a framework -- the Bayesian Active Site Calculator (BASC) -- for rapidly and accurately searching for the global minimum on potential energy surfaces, corresponding to stable adsorbate-surface structures. We formulate the problem as finding the minimum of a low-dimensional objective function. We present a technique using Bayesian inference that enables us to predict converged density functional theory (DFT) potential energies with fewer self-consistent field (SCF) iterations. We then discuss how we apply Bayesian Optimization to search for the global minimum of the objective function. We benchmark our method against Constrained Minima Hopping, a popular method for solving the problem at hand, and present the adsorption sites found by our global optimization method for various simple hydrocarbons on the rutile TiO2 (110) surface.