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

By C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Rivière and 7 more. 2020

83 citations recorded by the source corpus.

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.

Subfields: Energy Engineering and Power Technology, Materials Chemistry, Renewable Energy, Sustainability and the Environment, Computational Chemistry & Drug Discovery, AI for Physical Sciences

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Citations in the corpus

Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “AI is used to discover new electrocatalysts for efficient and scalable ways to store and utilize renewable energy [4], predicting renewable energy availability in advance to improve energy utilization [5], operating hyperscale data centers efficiently [6], growing plants using less natural resources”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “The OC20 dataset has been recently introduced, containing 200+ million samples from quantum chemistry simulations relevant to the discovery of new catalysts for renewable energy storage and other energy applications (Becke, 2014; Chanussot et al., 2020; Zitnick et al., 2020).”
Recent advances and applications of deep learning methods in materials science (2021, methodology): “DL is also heavily being used for predicting catalytic behavior of materials such as the Open Catalyst Project 109 which is driven by the DL methods materials design.”
Socio-Technological Challenges and Opportunities: Paths Forward (2021, methodology): “AI is used to discover new electrocatalysts for more efficient and scalable ways to store and use renewable energy [Zitnick et al., 2020] while also being used to predict renewable energy availability ahead of actual generation to better utilize the energy [Elkin and Witherspoon].”
Geometric and Physical Quantities improve E(3) Equivariant Message Passing (2021, background): “The Open Catalyst Project OC20 dataset (Zitnick et al., 2020; Chanussot et al., 2021), consists of molecular adsorptions onto surfaces.”