Accelerating the search for global minima on potential energy surfaces using machine learning

Controlling molecule-surface interactions is key for chemical applications ranging from catalysis to gas sensing. We present a framework for accelerating the search for the global minimum on potential surfaces, corresponding to stable adsorbate-surface structures. We present a technique using Bayesian inference that enables us to predict converged density functional theory potential energies with fewer self-consistent field iterations. We then discuss how this technique fits in with the Bayesian Active Site Calculator, which applies Bayesian optimization to the problem. We demonstrate the performance of our framework using a hematite (Fe₂O₃) surface and present the adsorption sites found by our global optimization method for various simple hydrocarbons on the rutile TiO₂ (110) surface.