  Biomass compounds adsorbed on surfaces are challenging to study due to the
large number of possible species and adsorption geometries. In this work,
possible intermediates of erythrose, glyceraldehyde, glycerol and propionic
acid are studied on the Rh(111) surface. The intermediates and elementary
reactions are generated from first 2 recursions of a recursive bond-breaking
algorithm. These structures are used as the input of an unsupervised Mol2Vec
algorithm to generate vector descriptors. A data-driven scheme to classify the
reactions is developed and adsorption energies are predicted. The lowest mean
absolute error (MAE) of our prediction on adsorption energies is 0.39 eV, and
the relative ordering of different surface adsorption geometries is relatively
accurate. We show that combining geometries from density functional
tight-binding (DFTB) calculations with energies from machine-learning
predictions provides a novel workflow for rapidly assessing the stability of
various molecular geometries on the Rh(111) surface.
