A Foundational Model for Reaction Networks on Metal Surfaces

Process optimization in heterogeneous catalysis relies on the control of competing reactions. The reaction mechanisms based on chemical knowledge can be evaluated via density functional theory unveiling experimental catalytic trends. However, this approach finds its limits when applied to complex reaction networks or large molecules, disregarding alternative paths and rare events. Here we present CARE, a foundational model for catalysis on metal surfaces with a rule-based reaction network generator for CxHyOz species built with GAME-Net-UQ, a graph neural network with uncertainty quantification targeting thermodynamic and kinetic parameters, coupled to microkinetic modeling. CARE reproduces experimental activity trends in methanol decomposition, selectivity to C3 products in electrochemical reduction processes, and models the Fischer-Tropsch synthesis to C6 products, including 370k reactions, breaking the current limits of network exploration. This comprehensive model opens the path towards the exploration of thermal and electrocatalytic surface processes previously not amenable to atomistic simulations.