AI-Accelerated Discovery of Electrocatalyst Materials
Yifan Zeng, Jun Wang, Fengwang Li, Tongliang Liu, Aoni Xu
Abstract
The rational exploration and design of high-performance, stable electrocatalysts are crucial for efficient renewable energy storage, conversion, and utilization. Artificial intelligence (AI) is revolutionizing this field by significantly reducing the time and cost associated with conventional trial-and-error experimentation and density functional theory (DFT) calculations. Advancements in data quality, computing power, and algorithms have positioned AI as a key enabler in understanding electrocatalytic mechanisms, designing advanced materials, analyzing structures, and predicting performance. This review highlights the pivotal role of AI in electrocatalyst discovery, focusing on the critical aspects of data, descriptors, and machine learning models. We discuss various AI approaches, including their applications in accelerating DFT calculations, exploring reaction mechanisms, designing electrocatalysts, and predicting performance, providing a comprehensive overview of the current state-of-the-art. We also address the challenges and opportunities in leveraging AI for electrocatalyst development, emphasizing the importance of data quality, model selection, and collaborative research. This review aims to guide researchers in effectively utilizing AI to accelerate the discovery and optimization of electrocatalysts for a renewable energy future.