  Here, we present a study combining Bayesian optimisation structural inference
with the machine learning interatomic potential NequIP to accelerate and enable
the study of the adsorption of the conformationally flexible lignocellulosic
molecules $\beta$-D-xylose and 1,4-$\beta$-D-xylotetraose on a copper surface.
The number of structure evaluations needed to map out the relevant potential
energy surfaces are reduced by Bayesian optimisation, while NequIP minimises
the time spent on each evaluation, ultimately resulting in cost-efficient and
reliable sampling of large systems and configurational spaces. Although the
applicability of Bayesian optimisation for the conformational analysis of the
more flexible xylotetraose molecule is restricted by the sample complexity
bottleneck, the latter can be effectively bypassed with external conformer
search tools, such as the Conformer-Rotamer Ensemble Sampling Tool,
facilitating the subsequent lower dimensional global minimum adsorption
structure determination. Finally, we demonstrate the applicability of the
described approach to find adsorption structures practically equivalent to the
density functional theory counterparts at a fraction of the computational cost.

Chemical Physics;Computational Physics