DeepAI AI Chat
Log In Sign Up

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

by   Alexandre Duval, et al.

Mitigating the climate crisis requires a rapid transition towards lower carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in a great number of industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the amount of energy spent on such processes, we must quickly discover more efficient catalysts to drive the electrochemical reactions. Machine learning (ML) holds the potential to efficiently model the properties of materials from large amounts of data, and thus to accelerate electrocatalyst design. The Open Catalyst Project OC20 data set was constructed to that end. However, most existing ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. Here, we propose several task-specific innovations, applicable to most architectures, which increase both computational efficiency and accuracy. In particular, we propose improvements in (1) the graph creation step, (2) atom representations and (3) the energy prediction head. We describe these contributions and evaluate them on several architectures, showing up to 5× reduction in inference time without sacrificing accuracy.


page 1

page 2

page 3

page 4


Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Accelerated discovery with machine learning (ML) has begun to provide th...

Machine Learning for a Sustainable Energy Future

Transitioning from fossil fuels to renewable energy sources is a critica...

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery

Strategies for machine-learning(ML)-accelerated discovery that are gener...

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

Scalable and cost-effective solutions to renewable energy storage are es...