收稿日期: 2023-03-06
修回日期: 2023-05-12
录用日期: 2023-05-16
网络出版日期: 2023-05-24
基金资助
国家自然科学基金面上项目(22071147)
Application of Machine Learning in Chemical Synthesis and Characterization
Received date: 2023-03-06
Revised date: 2023-05-12
Accepted date: 2023-05-16
Online published: 2023-05-24
自动化化学合成是化学领域长期追求的目标之一.近年来,机器学习的出现使得实现这一目标有了可能.以数据驱动为核心的机器学习借助计算机学习海量化学数据中的信息,寻找信息之间的客观联系和规律,根据已有规律和信息训练生成模型,借助模型预测分析需解决的实际问题.机器学习因其出色的计算预测能力,帮助化学工作者快速高效解决化学合成问题,加快研究进程.机器学习的出现和发展对化学合成及表征领域展示出强大的研究助力作用,但目前并不存在通用性极强的机器学习模型,化学工作者仍需根据实际情况选择不同模型进行训练学习.从监督学习、无监督学习、半监督学习、强化学习等机器学习的角度,向化学工作者展示常见学习方法在化学合成及表征中应用的最佳案例,帮助其利用机器学习知识进一步拓宽研究思路.
孙婕 , 李子昊 , 张书宇 . 机器学习在化学合成及表征中的应用[J]. 上海交通大学学报, 2023 , 57(10) : 1231 -1244 . DOI: 10.16183/j.cnki.jsjtu.2023.078
Automated chemical synthesis is one of the long-term goals pursued in the field of chemistry. In recent years, the advent of machine learning (ML) has made it possible to achieve this goal. Data-driven ML uses computers to learn relative information in massive chemical data, find objective connections between information, train models by using objective connections, and analyze the actual problems which can be solved according to these models. With its excellent computational prediction capabilities, ML helps chemists solve chemical synthesis problems quickly and efficiently and accelerate the research process. The emergence and development of ML has shown a strong research assistance in the field of chemical synthesis and characterization. However, there is no highly versatile ML model at present, and chemists still need to choose different models for training and learning according to actual situations. This paper aims to show chemists the best cases of common learning methods in chemical synthesis and characterization from the perspective of ML, such as supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, etc., and help them use ML knowledge to further broaden their research ideas.
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