  Catalyst discovery is paramount to support access to energy and key chemical
feedstocks in a post fossil fuel era. Exhaustive computational searches of
large material design spaces using ab-initio methods like density functional
theory (DFT) are infeasible. We seek to explore large design spaces at
relatively low computational cost by leveraging large, generalized, graph-based
machine learning (ML) models, which are pretrained and therefore require no
upfront data collection or training. We present catlas, a framework that
distributes and automates the generation of adsorbate-surface configurations
and ML inference of DFT energies to achieve this goal. Catlas is open source,
making ML assisted catalyst screenings easy and available to all. To
demonstrate its efficacy, we use catlas to explore catalyst candidates for the
direct conversion of syngas to multi-carbon oxygenates. For this case study, we
explore 947 stable/ metastable binary, transition metal intermetallics as
possible catalyst candidates. On this subset of materials, we are able to
predict the adsorption energy of key descriptors, *CO and *OH, with near-DFT
accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity
towards C2+ oxygenates from an existing microkinetic model, we identified 144
candidate materials. For 10 promising candidates, DFT calculations reveal a
good correlation with our assessment using ML. Among the top elemental
combinations were Pt-Ti, Pd-V, Ni-Nb, and Ti-Zn, all of which appear unexplored
experimentally.

Materials Science