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材料导报  2026, Vol. 40 Issue (5): 25030145-10    https://doi.org/10.11896/cldb.25030145
  无机非金属及其复合材料 |
人工智能驱动电催化材料全流程开发:从理性设计到产业化新范式
黄河1, 刘桂2,*
1 航天物联网技术有限公司,北京 100094;
2 中南大学冶金与环境学院,长沙 410083
Artificial Intelligence-driven Full-flow Development of Electrocatalytic Materials: a New Paradigm from Rational Design to Industrialization
HUANG He1, LIU Gui2,*
1 Aerospace Internet of Things Technology Co., Ltd., Beijing 100094, China;
2 College of Metallurgy and Environment, Central South University, Changsha 410083, China
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摘要 在全球能源转型的关键阶段,电催化技术作为实现可持续能源转换与存储的核心枢纽,面临着开发高性能催化剂的迫切需求。传统“试错型”研发模式受限于复杂材料体系的多维参数空间、多物理场耦合机制解析困难及高成本转化壁垒,难以满足规模化应用要求。人工智能(AI)与电催化的深度融合为突破上述瓶颈提供了创新路径。本综述系统梳理了AI在电催化领域的最新进展,涵盖催化剂理性设计与高通量筛选、稳定性与活性预测、反应机理解析及实验自动化等关键方向。重点阐述了机器学习算法与密度泛函理论(DFT)在电催化研究中的应用,展示了AI在加速催化剂发现、揭示构效关系及优化反应路径中的显著优势。同时,分析了实验自动化平台与可解释AI如何推动研究范式从经验直觉向数据理性跨越。针对当前面临的领域知识壁垒、算法可解释性及跨尺度建模挑战,提出构建全闭环研究系统、发展跨学科理论框架及强化产学研协同的未来发展方向。AI驱动的电催化创新正加速能源技术革命,为实现全球能源转型提供核心技术支撑。
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黄河
刘桂
关键词:  人工智能  电催化  机器学习  高通量筛选  催化剂设计    
Abstract: At the crucial juncture of global energy transition, electrocatalytic technology, a pivotal cornerstone for sustainable energy conversion and sto-rage, confronts an imperative demand for the development of high-performance catalysts. The conventional "trial-and-error" research and development paradigm, however, is severely hampered by the intricate parameter spaces of material systems, the formidable challenges of analyzing multi-physical field coupling mechanisms, and substantial transformation costs. As a result, it struggles to fulfill the requirements for large-scale applications. The profound integration of artificial intelligence (AI) with electrocatalysis effectively circumvents these bottlenecks, presenting innovative solutions across three key technical dimensions: rational catalyst design and high-throughput screening, prediction of stability and acti-vity, and reaction mechanism analysis in conjunction with experimental automation. This review comprehensively summarizes the latest advancements, particularly the integration of machine learning algorithms in electrochemistry. These examples vividly illustrate the distinct advantages of AI in expediting catalyst discovery, elucidating structure-property relationships, and optimizing reaction pathways. Furthermore, this review delves into how experimental automation platforms and interpretable AI techniques, facilitate a paradigm shift from empirical intuition to data-driven ratio-nality. AI-driven electrocatalytic innovation is accelerating the energy technology revolution at an unprecedented pace, offering crucial support for the global energy transition.
Key words:  artificial intelligence    electrocatalysis    machine learning    high-throughput screening    catalysts designing
出版日期:  2026-03-10      发布日期:  2026-03-10
ZTFLH:  O61  
基金资助: 国家自然科学基金(52422210)
通讯作者:  *刘桂,博士,高级工程师,西部矿业集团科技发展有限公司主任工程师。主要研究方向为电化学催化剂的理论与材料设计。liugui@buaa.edu.cn   
作者简介:  黄河,博士,高级工程师,硕士研究生导师,主要研究方向为智能物联与网络安全。曾任北京航空航天大学信息安全专业主任、副教授,现任航天物联网技术有限公司总工程师。
引用本文:    
黄河, 刘桂. 人工智能驱动电催化材料全流程开发:从理性设计到产业化新范式[J]. 材料导报, 2026, 40(5): 25030145-10.
HUANG He, LIU Gui. Artificial Intelligence-driven Full-flow Development of Electrocatalytic Materials: a New Paradigm from Rational Design to Industrialization. Materials Reports, 2026, 40(5): 25030145-10.
链接本文:  
https://www.mater-rep.com/CN/10.11896/cldb.25030145  或          https://www.mater-rep.com/CN/Y2026/V40/I5/25030145
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