Acta Chimica Sinica ›› 2020, Vol. 78 ›› Issue (12): 1366-1382.DOI: 10.6023/A20070306 Previous Articles     Next Articles



朱博阳a, 吴睿龙a, 于曦a,b   

  1. a 天津大学化学系 天津 300072;
    b 天津市分子光电科学重点实验室 天津 300072
  • 投稿日期:2020-07-12 发布日期:2020-08-21
  • 通讯作者: 朱博阳, 于曦;
  • 作者简介:朱博阳.主要研究方向:人工智能.程序后端开发.
  • 基金资助:

Artificial Intelligence for Contemporary Chemistry Research

Zhu Boyanga, Wu Ruilonga, Yu Xia,b   

  1. a Department of Chemistry, Tianjin University, Tianjin 300072, China;
    b Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Tianjin 300072, China
  • Received:2020-07-12 Published:2020-08-21
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Nos. 21973069, 21773169, 21872103), National Key R&D Program (Nos. 2017YFA0204503, 2016YFB0401100), the PEIYANG Young Scholars Program of Tianjin University (No. 2018XRX-0007) and the College Student Innovation and Entrepreneurship Training Program of Tianjin University (No. 201910056451).

Artificial intelligence (AI), especially the machine learning, is playing an increasingly important role in contemporary scientific research. Unlike the traditional computer program, machine learning can analyze a large number of data repeatedly and optimize its own model, a process which is called a "learning process". So that the AI can find the relationship underling the experiments from a large number of data, form a new model with better prediction and decisionmaking ability, and make an optimized strategy. The characteristics of chemical research just hit the strengths of machine learning. Chemical research often faces very complex material system and experimental process, so it is difficult to accurately analyze and making judgment through physical chemistry principles. Artificial intelligence can mine the correlation of massive experimental data generated in chemical experiments, help chemists make reasonable analysis and prediction, and therefore greatly accelerate the process of chemical research. This review presents the modern artificial intelligence method and its basic principles on solving chemical problems, by representative examples with specific machine learning algorithm. The application of artificial intelligence in chemical science is in a period of vigorous rise. Artificial intelligence has initially shown a powerful assist to chemical research. We hope this review can help more domestic chemical workers understand and use this powerful tool.

Key words: artificial intelligence, machine learning, molecular descriptor, deep learning