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Biomass compounds adsorbed on surfaces are challenging to study due to thelarge number of possible species and adsorption geometries. In this work,possible intermediates of erythrose, glyceraldehyde, glycerol and propionicacid are studied on the Rh(111) surface. The intermediates and elementaryreactions are generated from first 2 recursions of a recursive bond-breakingalgorithm. These structures are used as the input of an unsupervised Mol2Vecalgorithm to generate vector descriptors. A data-driven scheme to classify thereactions is developed and adsorption energies are predicted. The lowest meanabsolute error (MAE) of our prediction on adsorption energies is 0.39 eV, andthe relative ordering of different surface adsorption geometries is relativelyaccurate. We show that combining geometries from density functionaltight-binding (DFTB) calculations with energies from machine-learningpredictions provides a novel workflow for rapidly assessing the stability ofvarious molecular geometries on the Rh(111) surface.

Application of DFTB and machine learning to evaluate the stability of biomass intermediates on the Rh(111) surface