The design of next-generation functional materials and the quest to gain insights into dynamical processes at the atomic scale entail efficient and accurate atomistic simulations. The success of these simulations hinges on the accuracy, robustness, and transferability of the force fields (FFs) employed to describe interatomic interactions. One aspect of developing these FFs is to determine the set of variable parameters in their functional forms that closely reproduce available experimental or quantum mechanical data. In general, accurate FFs possess complex functional forms with numerous parameters, and call for sophisticated fitting strategies. Supervised machine learning methods, e.g., genetic algorithms in combination with local minimization methods (e.g., Simplex) provide an efficient way to scan the parameter space for such an optimization problem. We use representative examples (metal clusters and oxide surfaces) to highlight the effectiveness of ML strategies in bridging the atomistic and electronic length scales and accelerate the design of electrocatalysts. We also demonstrate that these ML models can further be used with AI approaches to design catalyst structures for user defined or targeted properties.<br/>

AI and machine learning guided design of electrocatalysts

2019 Spring National Meeting

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