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.

翻译：人工智能与科学的融合在新型催化剂设计与发现的计算化学方法上取得了显著进展。然而，电催化反应及催化领域大规模语言模型开发的挑战依然存在。近期基于ChatGPT（生成式预训练Transformer）的小样本方法在性能上超越BERT（双向编码器表征Transformer）的成功，凸显了在研究过程中应对数据有限、计算成本高昂、时间约束以及构效关系问题的重要性。因此，无论从当前还是未来需求来看，开发催化领域的小样本技术都至关重要。本文介绍了2023年小样本开放催化剂挑战赛，该竞赛旨在推动机器学习技术在催化表面反应预测中的应用，特别关注过氧化氢电催化中的双原子催化剂。针对催化领域数据稀缺的问题，我们提出了一种基于类MLP架构的机器学习方法及名为催化蒸馏图神经网络（CDGNN）的框架。结果表明，CDGNN能有效从催化结构中学得嵌入表征，从而捕捉结构-吸附关系。该成果实现了对过氧化氢反应路径的最高效、最先进测定，比现有图神经网络方法提升16.1%。因此，CDGNN为催化领域的小样本学习提供了极具前景的解决方案。