人工智能助力当代化学研究

doi:10.6023/A20070306

化学学报 ›› 2020, Vol. 78 ›› Issue (12): 1366-1382.DOI: 10.6023/A20070306 上一篇下一篇

综述

人工智能助力当代化学研究

朱博阳^a, 吴睿龙^a, 于曦^a,b

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 E-mail:luciszhu@outlook.com;xi.yu@tju.edu.cn
作者简介:朱博阳.主要研究方向:人工智能.程序后端开发.
吴睿龙.2017年考入天津大学,现为理学院化学系大三学生.目前在于曦老师指导下从事机器学习用于化学体系的大学生创新项目.
于曦.天津大学理学院化学系、物理系研究员、教授.主要研究方向为微观体系电荷的量子输运、分子电子学和微纳光电器件,以及人工智能辅助的有机光电材料开发.
基金资助:
项目受国家自然科学基金（Nos.21973069，21773169，21872103）、国家重点研究开发计划（Nos.2017YFA0204503，2016YFB0401100）、天津大学北洋青年学者计划（No.2018XRX-0007）和天津大学大学生创新创业训练计划（No.201910056451）资助.

Artificial Intelligence for Contemporary Chemistry Research

Zhu Boyang^a, Wu Ruilong^a, Yu Xi^a,b

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).

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摘要/Abstract

以机器学习为代表的人工智能在当代的科学研究中正在发挥越来越重要的作用.不同于传统的计算机程序，机器学习人工智能可以通过对大量数据的反复分析和自身模型的优化，即“学习”过程，从而在大量的数据中寻找客观事物的相互联系，形成具有更好预测和决策能力的新模型，做出合理的判断.化学研究的特点恰恰是机器学习人工智能的强项.化学研究经常要面对十分复杂的物质体系和实验过程，从而很难通过化学物理原理进行精准的分析和判断.人工智能可以挖掘化学实验中产生的海量实验数据的相关性，帮助化学家做出合理分析预测，大大加速化学研发过程.本文介绍了当代人工智能方法及用其解决化学问题基本原理，并通过具体案例展示了人工智能辅助解决不同化学研发问题的方法以及对应的机器学习算法.将人工智能运用在化学科学的尝试正处于蓬勃上升期，人工智能已经初步展示出对化学研究的强大助力，希望本文能帮助更多的国内的化学工作者了解和运用这一有力的工具.

关键词: 人工智能, 机器学习, 分子描述符, 深度学习

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

引用此文

朱博阳, 吴睿龙, 于曦. 人工智能助力当代化学研究[J]. 化学学报, 2020, 78(12): 1366-1382.

Zhu Boyang, Wu Ruilong, Yu Xi. Artificial Intelligence for Contemporary Chemistry Research[J]. Acta Chimica Sinica, 2020, 78(12): 1366-1382.

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人工智能助力当代化学研究

Artificial Intelligence for Contemporary Chemistry Research

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