化工进展 ›› 2023, Vol. 42 ›› Issue (7): 3340-3348.DOI: 10.16085/j.issn.1000-6613.2023-0655

• 专栏:智能化工装备与安全 • 上一篇    下一篇

功能固体材料智能合成研究进展

陈森1,2(), 殷鹏远1,2, 杨证禄1,2, 莫一鸣1,2, 崔希利1,2, 锁显1,2(), 邢华斌1,2   

  1. 1.浙江大学化学工程与生物工程学院,浙江 杭州 310027
    2.浙江大学杭州国际科创中心,浙江 杭州 311215
  • 收稿日期:2023-04-23 修回日期:2023-06-21 出版日期:2023-07-15 发布日期:2023-08-14
  • 通讯作者: 锁显
  • 作者简介:陈森(1997—),男,博士研究生,研究方向为分离工程与高纯化学品制备。E-mail:senchen_@zju.edu.cn
  • 基金资助:
    国家重大科研仪器研制项目(22227812)

Advances in the intelligent synthesis of functional solid materials

CHEN Sen1,2(), YIN Pengyuan1,2, YANG Zhenglu1,2, MO Yiming1,2, CUI Xili1,2, SUO Xian1,2(), XING Huabin1,2   

  1. 1.College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
    2.ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311215, Zhejiang, China
  • Received:2023-04-23 Revised:2023-06-21 Online:2023-07-15 Published:2023-08-14
  • Contact: SUO Xian

摘要:

先进功能材料是现代工业发展的基础与先导,加快材料研发与筛选速度的关键在于研究范式的创新。以过程自动化、合成高通量、信息数字化为核心要素的智能合成模式作为新的研究范式逐渐成为现代材料研发的新趋势。本文介绍了近年来功能固体材料在智能合成领域的研究现状,着重梳理了以沸石、金属有机框架材料、多孔有机聚合物为代表的多孔材料以及其他功能固体材料在自动化、高通量合成方面的研究进展。文章指出了目前功能固体材料智能合成中仍存在的全流程自动化实现难、与人工智能结合程度有限等不足,对比了几种高效合成方法在反应时效性、产物影响等方面的特性及优缺点,分析了以“人工智能+大数据”为代表的数据驱动模式对功能材料性能预测与辅助合成带来的影响,最后总结出功能更全、精度更高、微量化自动合成平台的开发,准确性更高、泛用性更广的人工智能算法的构建以及两者的高度集成将成为未来的发展方向。

关键词: 功能材料, 多孔材料, 高通量, 自动化, 合成, 数据驱动, 预测

Abstract:

Advanced functional materials are the foundation of modern industries and the innovation of research paradigms is the key to accelerating materials screening and development. As a new research paradigm, the intelligent synthesis model with process automation, high-throughput synthesis and digitalization of information as core elements has gradually become a new trend in modern materials research and development. This article summarized the research status of functional solid materials in the field of intelligent synthesis in recent years and focused on the research progress in the automatic and high-throughput synthesis of porous materials represented by zeolites, metal-organic frameworks, and porous organic polymers, as well as other functional solid materials. This review pointed out the existing shortcomings in the intelligent synthesis of advanced functional materials, such as the difficulty of full process automation and the limited integration with artificial intelligence, further compared the characteristics, advantages, and disadvantages of several efficient synthetic methods in the reaction rates and product effects, and analyzed the impact of the data-driven model represented by “artificial intelligence and big data” on the performance prediction and assisted synthesis of functional materials. Finally, it was concluded that the development of a more fully functional, accurate, and micro-scale automated synthesis platform, the construction of more accurate and generalizable artificial intelligence algorithms, and their high-degree integration would be the future directions.

Key words: functional materials, porous materials, high-throughput, automation, synthesis, data-driven, prediction

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