  Scalable and cost-effective solutions to renewable energy storage are
essential to addressing the world's rising energy needs while reducing climate
change. As we increase our reliance on renewable energy sources such as wind
and solar, which produce intermittent power, storage is needed to transfer
power from times of peak generation to peak demand. This may require the
storage of power for hours, days, or months. One solution that offers the
potential of scaling to nation-sized grids is the conversion of renewable
energy to other fuels, such as hydrogen or methane. To be widely adopted, this
process requires cost-effective solutions to running electrochemical reactions.
An open challenge is finding low-cost electrocatalysts to drive these reactions
at high rates. Through the use of quantum mechanical simulations (density
functional theory), new catalyst structures can be tested and evaluated.
Unfortunately, the high computational cost of these simulations limits the
number of structures that may be tested. The use of machine learning may
provide a method to efficiently approximate these calculations, leading to new
approaches in finding effective electrocatalysts. In this paper, we provide an
introduction to the challenges in finding suitable electrocatalysts, how
machine learning may be applied to the problem, and the use of the Open
Catalyst Project OC20 dataset for model training.
