  Tailoring the functional properties of advanced organic/inorganic
heterogeonous devices to their intended technological applications requires
knowledge and control of the microscopic structure inside the device. Atomistic
quantum mechanical simulation methods deliver accurate energies and properties
for individual configurations, however, finding the most favourable
configurations remains computationally prohibitive. We propose a 'building
block'-based Bayesian Optimisation Structure Search (BOSS) approach for
addressing extended organic/inorganic interface problems and demonstrate its
feasibility in a molecular surface adsorption study. In BOSS, a likelihood-free
Bayesian scheme accelerates the identification of material energy landscapes
with the number of sampled configurations during active learning, enabling
structural inference with high chemical accuracy and featuring large simulation
cells. This allowed us to identify several most favourable molecular adsorption
configurations for $\mathrm{C}_{60}$ on the (101) surface of $\mathrm{TiO}_2$
anatase and clarify the key molecule-surface interactions governing structural
assembly. Inferred structures were in good agreement with detailed experimental
images of this surface adsorbate, demonstrating good predictive power of BOSS
and opening the route towards large-scale surface adsorption studies of
molecular aggregates and films.
