Catalysis distillation neural network for the few shot open catalyst challenge

• URL: http://arxiv.org/abs/2305.19545v1
• Date: Wed, 31 May 2023 04:23:56 GMT
• Title: Catalysis distillation neural network for the few shot open catalyst challenge
• Authors: Bowen Deng
• Abstract summary: This paper introduces Few-Shot Open Catalyst Challenge 2023, a competition aimed at advancing the application of machine learning for predicting reactions. We propose a machine learning approach based on 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.
• Score: 1.1878820609988694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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