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The integration of artificial intelligence and science has resulted insubstantial progress in computational chemistry methods for the design anddiscovery of novel catalysts. Nonetheless, the challenges of electrocatalyticreactions and developing a large-scale language model in catalysis persist, andthe recent success of ChatGPT's (Chat Generative Pre-trained Transformer)few-shot methods surpassing BERT (Bidirectional Encoder Representation fromTransformers) underscores the importance of addressing limited data, expensivecomputations, time constraints and structure-activity relationship in research.Hence, the development of few-shot techniques for catalysis is critical andessential, regardless of present and future requirements. This paper introducesthe Few-Shot Open Catalyst Challenge 2023, a competition aimed at advancing theapplication of machine learning technology for predicting catalytic reactionson catalytic surfaces, with a specific focus on dual-atom catalysts in hydrogenperoxide electrocatalysis. To address the challenge of limited data incatalysis, we propose a machine learning approach based on MLP-Like and aframework called Catalysis Distillation Graph Neural Network (CDGNN). Ourresults demonstrate that CDGNN effectively learns embeddings from catalyticstructures, enabling the capture of structure-adsorption relationships. Thisaccomplishment has resulted in the utmost advanced and efficient determinationof the reaction pathway for hydrogen peroxide, surpassing the current graphneural network approach by 16.1%.. Consequently, CDGNN presents a promisingapproach for few-shot learning in catalysis.

Catalysis distillation neural network for the few shot open catalyst challenge