Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

被引:100
|
作者
Jiao, Zeren [1 ]
Hu, Pingfan [1 ]
Xu, Hongfei [1 ]
Wang, Qingsheng [1 ]
机构
[1] Texas A&M Univ, Artie McFerrin Dept Chem Engn, Mary Kay OConnor Proc Safety Ctr, College Stn, TX 77843 USA
关键词
machine learning; deep learning; artificial intelligence; chemical health; process safety; CONVOLUTIONAL NEURAL-NETWORK; PROPERTY RELATIONSHIP MODELS; SKIN SENSITIZATION POTENCY; MINIMUM IGNITION ENERGY; SUPPORT VECTOR MACHINE; IN-SILICO PREDICTION; FAULT-DIAGNOSIS; DISPERSION PREDICTION; FLAMMABILITY LIMITS; RELATIONSHIP QSPR;
D O I
10.1021/acs.chas.0c00075
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions and decision-making. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this Review, commonly used ML/DL tools and concepts as well as popular ML/DL algorithms are introduced and discussed. More than 100 papers have been categorized and summarized to present the current development of ML/DL-based research in the area of chemical health and safety. In addition, the limitation of current studies and prospects of ML/DL-based study are also discussed. This Review can serve as useful guidance for researchers who are interested in implementing ML/DL into chemical health and safety research and for readers who try to learn more information about novel ML/DL techniques and applications.
引用
收藏
页码:316 / 334
页数:19
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