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      • 論文
      主辦單位:煤炭科學研究總院有限公司、中國煤炭學會學術期刊工作委員會
      機器學習加速能源環境催化材料的創新研究
      • Title

        Machine learning accelerating innovative researches on energy andenvironmental catalysts

      • 作者

        張霄董毅林賽賽傅雨杰徐麗趙海濤楊洋劉鵬劉少俊張涌新鄭成航高翔

      • Author

        ZHANG Xiao;DONG Yi;LIN Saisai;FU Yujie;XU Li;ZHAO Haitao;YANG Yang;LIU Peng;LIU Shaojun;ZHANG Yongxin;ZHENG Chenghang;GAO Xiang

      • 單位

        浙江大學能源高效清潔利用全國重點實驗室浙江大學碳中和研究院白馬湖實驗室

      • Organization
        State Key Laboratory of Clean Energy Utilization, Zhejiang University
        Institute of Carbon Neutrality, Zhejiang University
        Baima Lake Laboratory
      • 摘要
        “雙碳”背景下,加快研發高效的能源與環境催化材料有助于推進能源清潔利用和環境污染治理。 傳統催化材料研發模式主要依賴實驗試錯方法,難以滿足能源與環境領域對高效催化材料的研發需求。 快速發展的機器學習等數據科學技術為催化材料研發帶來范式變革的契機。 基于機器學習、實驗數據和計算數據的有機結合,可對催化材料進行快速篩選,突破傳統試錯法的局限性,有利于解決催化劑研發效率低、成本高等難題。 本文從催化材料的位點預測、配方篩選、構型設計以及反應路徑優化等角度討論了機器學習方法加快能源與環境催化材料創新的研究進展,分析了不同訓練數據獲取途徑對應的機器學習方法構建及其在催化材料開發中的應用,展望了機器學習加快催化材料研究方法創新的發展趨勢,以期為促進其在能源與環境領域的應用提供啟示。
      • Abstract
        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.
      • 關鍵詞

        催化劑能源與環境機器學習高通量技術數據驅動

      • KeyWords

        Catalyst; Energy and environment; Machine learning; High-throughput technique; Data-driven

      • 基金項目(Foundation)
        國家自然科學基金資助項目(51836006);浙江省自然科學基金資助項目(LDT23E06012E06)
      • 文章目錄

        0 引 言
        1 位點預測
        2 配方篩選
        3 構型設計
        4 路徑優化
        5 結論與展望

      • DOI
      • 引用格式
        張霄, 董毅, 林賽賽, 等. 機器學習加速能源環境催化材料的創新研究[J]. 能源環境保護, 2023, 37(3):1-12.
      • Citation
        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.
      相關問題

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