CIESC Journal ›› 2023, Vol. 74 ›› Issue (1): 14-28.DOI: 10.11949/0438-1157.20221077

• Reviews and monographs • Previous Articles     Next Articles

Artificial intelligence for accelerating polymer design: recent advances and future perspectives

Tianhang ZHOU1(), Xingying LAN1,2(), Chunming XU1,2   

  1. 1.College of Carbon Neutrality Future Technology, China University of Petroleum (Beijing), Beijing 102249, China
    2.State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing), Beijing 102249, China
  • Received:2022-07-30 Revised:2022-09-05 Online:2023-03-20 Published:2023-01-05
  • Contact: Xingying LAN

人工智能加速聚合物设计的最新进展和未来前景

周天航1(), 蓝兴英1,2(), 徐春明1,2   

  1. 1.中国石油大学(北京)碳中和未来技术学院,北京 102249
    2.中国石油大学(北京)重质油国家重点实验室,北京 102249
  • 通讯作者: 蓝兴英
  • 作者简介:周天航(1994—),男,博士,讲师,zhouth@cup.edu.cn
  • 基金资助:
    国家自然科学基金创新群体项目(22021004)

Abstract:

Due to the enormous chemical and configurational space, the optimal design of candidates for the next-generation soft materials is still a challenging task. It is cumbersome to conduct trial-and-error research using high-throughput computations or experiments to evaluate the properties of a large number of materials and select the best candidates for future investigations. Using artificial intelligence approaches in combination with computer simulations and experiments, researchers are able to reliably predict properties of materials over a vast structural and property space, breaking the traditional model of “empirically guided experiments” and gradually overcoming various bottlenecks in the process of the polymer design. This review begins with a historical look at the difficulties in polymer engineering over the preceding decades. The concept of data-driven techniques is then given and examined in detail, along with how they are used in polymer design. The following section highlights some noteworthy developments in identifying novel polymers with specific characteristics using data-driven approaches. In conclusion, this review provides a synopsis of recent tendencies and outlines the opportunities for intelligent design in polymer engineering. Artificial intelligence, rapid computational simulation, and the availability of enormous amounts of open-source homogeneous data combined with experiments will revolutionize polymer research and accelerate the industrial application of designed polymeric materials. Finally, the current industry development trend is summarized, and the large-scale application prospects of intelligent design in the research of new polymers are prospected.

Key words: artificial intelligence, polymer design, data-driven, structure-property relationship, inverse design

摘要:

广阔的化学空间蕴藏着近乎无限的可能,高性能聚合物材料的设计至今仍是一项充满挑战的工作。利用实验或高通量计算广泛探索大量样本,选择其中性能较好的候选材料进行深入研究的传统试错方式,越发难以在庞大的组合序列中筛选出满足实际多项性能需求的新材料。由先进计算方法、自动化实验和大数据技术耦合形成的新型智能研发模式,突破了“经验指导实验”的传统思路,有望实现在广阔的结构与性质空间中进行聚合物材料的特性预测,逐渐成为克服各类瓶颈问题的得力助手,大大提高了高性能聚合物设计的效率。本文回顾了以往聚合物设计的困境,讨论了人工智能方法的一般思路及其在聚合物设计中的工作原理,列举了采用智能方法在高性能聚合物研发工作中取得突破性进展的典型案例,最后对当前行业发展趋势进行总结,展望了智能设计在新型聚合物研究中的规模化应用前景。

关键词: 人工智能, 聚合物设计, 数据驱动, 结构-性能关系, 反求设计

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