  Identifying the atomic structure of organic-inorganic interfaces is
challenging with our current research tools. Interpreting the structure of
complex molecular adsorbates from microscopy images can be difficult, and using
atomistic simulations to find the most stable structures is limited to partial
exploration of the potential energy surface due to the high-dimensional phase
space. In this study, we present the recently developed Bayesian Optimization
Structure Search (BOSS) method as an efficient solution for identifying the
structure of non-planar adsorbates. We apply BOSS with density-functional
theory simulations to detect the stable adsorbate structures of (1S)-camphor on
the Cu(111) surface. We identify the optimal structure among 8 unique types of
stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or
physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates
that new cross-disciplinary tools, like BOSS, facilitate the description of
complex surface structures and their properties, and ultimately allow us to
tune the functionality of advanced materials.
