On-the-fly Machine Learning-Accelerated Geometry Optimization: Theoretical Screening of a Single Atom Alloy for CO₂ Electroreduction Reaction

Force-fields based upon machine learning (ML) methods are being adopted for the design of catalysts due to their high efficiency and first-principles level accuracy. Such efficiency and accuracy are important particularly for high-throughput computations, which are key to the computational design of new catalysts. Among various types of catalysts, single-atom alloy (SAA) catalysts, which substitute a single atom from the surface for a different type of atom, have exhibited outstanding selectivity and activity due to their unique geometry. There have been a number of theoretical studies to find optimal combinations of SAA for catalytic reactions but screening all possible combinations remains intractable due to the computational expense. Here, we show a mathematical modeling package of training potential energy surfaces using artificial neural networks and applying the machine-learned models to accelerate geometry optimization process. On-the-fly generated machine learning force-fields are used as the basis for a separate optimization to reach a local minima or saddle point on the surrogate PES, which requires less computational effort than the same steps using DFT. Iteration between ML-optimization and DFT calculations will reduce the overall number of DFT force calls required for optimizations, while retaining DFT accuracy for high-throughput screening.