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

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.

27 pages

References in corpus (14)

• Neural Message Passing for Quantum Chemistry
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• SchNet - a deep learning architecture for molecules and materials
• AFLOW: An automatic framework for high-throughput materials discovery
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Graph Neural Networks: A Review of Methods and Applications
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• The Open Catalyst 2020 (OC20) Dataset and Community Challenges
• By-passing the Kohn-Sham equations with machine learning
• On the Completeness of Atomic Structure Representations
• Orbital Graph Convolutional Neural Network for Material Property Prediction
• Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
• Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
• Molecule Property Prediction and Classification with Graph Hypernetworks

Cited by in corpus (11)

• Recent Advances and Applications of Deep Learning Methods in Materials Science
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• Sustainable AI: Environmental Implications, Challenges and Opportunities
• WILDS: A Benchmark of in-the-Wild Distribution Shifts
• AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
• Rotation Invariant Graph Neural Networks using Spin Convolutions
• ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
• Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
• Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning
• Beyond Independent Error Assumptions in Large GNN Atomistic Models
• Socio-Technological Challenges and Opportunities: Paths Forward