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Scalable and cost-effective solutions to renewable energy storage areessential to addressing the world's rising energy needs while reducing climatechange. As we increase our reliance on renewable energy sources such as windand solar, which produce intermittent power, storage is needed to transferpower from times of peak generation to peak demand. This may require thestorage of power for hours, days, or months. One solution that offers thepotential of scaling to nation-sized grids is the conversion of renewableenergy to other fuels, such as hydrogen or methane. To be widely adopted, thisprocess requires cost-effective solutions to running electrochemical reactions.An open challenge is finding low-cost electrocatalysts to drive these reactionsat high rates. Through the use of quantum mechanical simulations (densityfunctional theory), new catalyst structures can be tested and evaluated.Unfortunately, the high computational cost of these simulations limits thenumber of structures that may be tested. The use of machine learning mayprovide a method to efficiently approximate these calculations, leading to newapproaches in finding effective electrocatalysts. In this paper, we provide anintroduction to the challenges in finding suitable electrocatalysts, howmachine learning may be applied to the problem, and the use of the OpenCatalyst Project OC20 dataset for model training.

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage