  Controlling the properties of organic/inorganic materials requires detailed
knowledge of their molecular adsorption geometries. This is often unattainable,
even with current state-of-the-art tools. Visualizing the structure of complex
non-planar adsorbates with atomic force microscopy (AFM) is challenging, and
identifying it computationally is intractable with conventional structure
search. In this fresh approach, cross-disciplinary tools are integrated for a
robust and automated identification of 3D adsorbate configurations. Bayesian
optimization is employed with first-principles simulations for accurate and
unbiased structure inference of multiple adsorbates. The corresponding AFM
simulations then allow fingerprinting adsorbate structures that appear in AFM
experimental images. In the instance of bulky (1S)-camphor adsorbed on the
Cu(111) surface, three matching AFM image contrasts are found, which allow
correlating experimental image features to distinct cases of molecular
adsorption.
