Abstract
The integration of artificial intelligence and science has resulted in
substantial progress in computational chemistry methods for the design
and discovery of novel catalysts. Nonetheless, the challenges of
electrocatalytic reactions and developing a “large-scale language
model” in catalysis persist, and the recent success of ChatGPT’s(Chat
Generative Pre-trained Transformer) few-shot methods surpassing BERT
(Bidirectional Encoder Representation from Transformers) underscores the
importance of addressing limited data, expensive computations, time
constraints and structure-activity relationship in research. Hence, the
development of few-shot techniques for catalysis is critical and
essential, regardless of present and future requirements. This paper
introduces the Few-Shot Open Catalyst Challenge 2023, a competition
aimed at advancing the application of machine learning technology for
predicting catalytic reactions on catalytic surfaces, with a specific
focus on dual-atom catalysts in hydrogen peroxide electrocatalysis. To
address the challenge of limited data in catalysis, we propose a machine
learning approach based on MLP-Like and a framework called Catalysis
Distillation Graph Neural Network (CDGNN).Our results demonstrate that
CDGNN effectively learns embeddings from catalytic structures, enabling
the capture of structure-adsorption relationships. This accomplishment
has resulted in the utmost advanced and efficient determination of the
reaction pathway for hydrogen peroxide, surpassing the current graph
neural network approach by 16.1%. Consequently, CDGNN presents a
promising approach for few-shot learning in catalysis.