Artificial Intelligence and QM/MM with a Polarizable Reactive Force Field for Next-Generation Electrocatalysts

We developed a computational framework that enables quantum mechanics accuracy for simulation of practical-sized nanoparticles and catalysts. This enables design and discovery of new-generation electrocatalysts with dramatically higher performance. We also developed strategies to combine this new method with machine learning techniques to accelerate the design of disordered and dealloyed catalyst surfaces. We applied this technique to select the top 300 active sites from the 10,000 surface sites of solvated gold (Au) nanoparticles.