State Key Laboratory of Clean Energy Utilization, Zhejiang University
Institute of Carbon Neutrality, Zhejiang University
Baima Lake Laboratory

“双碳”背景下,加快研发高效的能源与环境催化材料有助于推进能源清洁利用和环境污染治理。 传统催化材料研发模式主要依赖实验试错方法,难以满足能源与环境领域对高效催化材料的研发需求。 快速发展的机器学习等数据科学技术为催化材料研发带来范式变革的契机。 基于机器学习、实验数据和计算数据的有机结合,可对催化材料进行快速筛选,突破传统试错法的局限性,有利于解决催化剂研发效率低、成本高等难题。 本文从催化材料的位点预测、配方筛选、构型设计以及反应路径优化等角度讨论了机器学习方法加快能源与环境催化材料创新的研究进展,分析了不同训练数据获取途径对应的机器学习方法构建及其在催化材料开发中的应用,展望了机器学习加快催化材料研究方法创新的发展趋势,以期为促进其在能源与环境领域的应用提供启示。

Under the "dual carbon" background, the development of high-performance energy and envi⁃ronmental catalysis materials is of great significance for promoting energy clean transformation and envi⁃ronmental pollution control. The traditional research and development mode of catalysts mainly relies onexperimental and trial-and-error methods, which to a large extent cannot meet the research and develop⁃ment needs of efficient catalysts in emerging energy and environmental fields. The rapid development ofdata science technologies such as machine learning is expected to bring about a paradigm shift in catalystresearch and development. By using machine learning methods to quickly screen high - performanceenergy and environmental catalysis materials using experimental or computational data, the limitations oftraditional trial-and-error methods could be overcome, and the problem of low efficiency and high costin catalyst research and development could be solved. This article reviewed the main processes and re⁃search progress of machine learning methods in the development of energy and environmental catalysismaterials from the perspective of active-sites prediction,catalysts screening, morphology design and reac⁃tion mechanism revelation, and the ML methods construction corresponding to various training data ac⁃quisition and their applications in the catalytic research. We also discussed the future direction of thismethod in the catalysis field, in order to provide perspective and promote its application in the energyand environmental fields.

国家自然科学基金资助项目(51836006);浙江省自然科学基金资助项目(LDT23E06012E06)

张霄, 董毅, 林赛赛, 等. 机器学习加速能源环境催化材料的创新研究[J]. 能源环境保护, 2023, 37(3):1-12.

ZHANG Xiao, DONG Yi, LIN Saisai, et al. Machine learning accelerating innovative researches on energy andenvironmental catalysts[J]. Energy Environmental Protection, 2023, 37(3): 1-12.