Extrapolation Techniques in Database Construction for Machine-Learning Potentials: Achieving Subchemical Accuracy in Sampling Conformal Funnels in Catalytic Processes

We present a computational workflow, the conformal sampling of catalytic processes enhanced with extrapolation techniques (CSCP-X), for constructing machine-learning interatomic potentials (MLIPs) to accelerate the exploration of first-principles potential energy surfaces for complex catalytic reactions. The MLIPs developed within the enhanced CSCP framework achieve subchemical accuracy error (∼0.03 eV) with respect to the energetics of the generating first-principles DFT approach, that is the...