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PhAST: Physics-Aware, Scalable, and Task-specific GNNs for accelerated catalyst design
ALEXANDRE DUVAL · Victor Schmidt · Alex Hernandez-Garcia · Santiago Miret · Yoshua Bengio · David Rolnick

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in AI for Accelerated Materials Design (AI4Mat) »
Event URL: https://openreview.net/forum?id=hHercGKiXvP »
Mitigating the climate crisis requires a rapid transition towards lower carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in this transition, such as electrofuel synthesis, renewable fertiliser production and energy storage. In this context, there is a need to discover more effective catalysts for these 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 it 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 precision. In particular, we aim to improve (1) the graph creation step, (2) atom representations and (3) the energy prediction head. We describe and evaluate these contributions across several architectures, showing up to 5$\times$ reduction in inference time without sacrificing accuracy.
Keywords: Deep Learning · graph neural networks · materials science · Climate Change · Catalyst Discovery

Author Information

ALEXANDRE DUVAL (Mila)

PhD Student at Mila working on GNNs for accelerated catalysis discovery, with Yoshua Bengio and David Rolnick.

Victor Schmidt (Mila - Université de Montréal)
Alex Hernandez-Garcia (Mila - Quebec AI Institute)
Santiago Miret (Intel AI Lab)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

David Rolnick (McGill / Mila)

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