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

TL;DR

This paper introduces the Open Catalyst Project OC20 as a pivotal resource for applying machine learning to electrocatalysis in renewable energy storage. It frames the challenge of expensive DFT relaxations and outlines three practical ML tasks (S2EF, IS2RS, IS2RE) to approximate energies and forces, relaxations, and energies from initial structures. By detailing OC20’s scale, inputs, and evaluation protocols, the authors lay out how graph neural networks and related ML models can accelerate catalyst discovery across vast chemical spaces. The study emphasizes the potential for ML-DFT surrogates to enable rapid, large-scale screening of electrocatalysts for hydrogen evolution, oxygen evolution, and methane synthesis, ultimately driving down costs in renewable energy storage technologies. It also discusses future directions, such as handling larger molecules, diverse reaction pathways, and more realistic reaction environments with electrolytes.

Abstract

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.

Figures (18)

• Figure 1: Hourly electricity demand for California on a typical summer day (August 6th, 2020) as reported by California ISO CAISO. The demand (black line) peeks around 19:00, the same time that output from solar and wind power (green line) declines. The remaining demand after the use of wind and solar power is shown by the orange line. Note that energy storage is needed if solar and wind are to meet demand without excess energy loss.
• Figure 2: Diagram of the hydrogen (H2) and methane (CH4) energy storage process using renewable energy. The stored hydrogen and methane is used to generate electricity when renewable energy generation is not able to meet demand. Renewable energy (bottom left) may either be fed directly into the grid or used to generate hydrogen by electrolysis. The hydrogen is stored for future electricity generation by fuel cells or used in the methanation process to generate methane. The generation of electricity from stored methane can potentially be carbon neutral by recycling the CO2 for later use in methanation.
• Figure 3: Illustration of a PEM fuel cell. The anode (red) splits hydrogen into protons H+ and electrons e-. The cathode (blue) combines the protons and electrons with oxygen to produce water. The membrane (yellow) only allows protons to pass through it, which forces the electrons to travel through the electric circuit, creating an electric potential.
• Figure 4: Illustration of the potential energy between two atoms as the distance $d$ between them varies. When the atoms are far apart (right), the weaker van der Waals forces attract atoms towards each other. As the atoms move closer together (left), the stronger Coulombic forces repel the atoms. The minimum is the stable state where these two forces cancel out. The depth of the minimum is dependent on whether a bond is formed (chemisorption) or not (physisorption) by the atoms.
• Figure 5: Illustration of a reaction's free and activation energy. The primary role of a cataylst is to minimize the activation energy of a reaction. The sign of the free energy indicates whether the reaction is spontaneous (negative) or not (positive). Plot values obtained from CatApp hummelshoj2012catapp for Pt(111) surface.