Application and Prospect of Machine Learning in the Field of Materials and Chemical Engineering
Journal: Architecture Engineering and Science DOI: 10.32629/aes.v5i1.1845
Abstract
With the rapid development of computer science, machine learning has gradually penetrated into various research fields and led to the development of related fields to different degrees. At recent stage, machine learning (ML) algorithms have been able to extensively search for variable relationships from a large amount of observed data, thus helping people to discover more laws that were once unnoticed. As an emerging approach, ML can efficiently utilize and process large-volume data generated from high-throughput experiments in the field of materials and chemicals. In recent years, with the continuous improvement of various algorithmic tools, more and more researchers have applied ML to materials and chemical research, which has greatly broadened the research ideas and methods. This paper briefly introduces the development of machine learning in recent years; summarizes the classification methods of common molecular descriptors and their types; introduces the classification, common algorithms and application scenarios of different types of ML; reviews the relevant research results in the field of materials prediction and inverse synthesis analysis of machine learning; puts forward related suggestions for the application of ML for research; and finally summarizes the prospects for the future development of machine learning applied to the field of materials and chemical engineering.
Keywords
machine learning, molecular descriptors, material prediction, inverse synthesis design
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[4]Liu Yidi, Yang Qi, LI Yao et al. Application of Machine Learning in Organic Chemistry [J]. Chinese Journal of Organic Chemistry, 2019,40(11):3812-3827.
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[9]LI Wei, YANG Jin-cai, HUANG Niu. Deep learning in drug design and discovery[J]. Acta Pharmaceutica Sinica, 2019, 54 (05): 761-767.
[10]Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018 Mar 28;555(7698):604-610.
[11]Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian V. Stevens, Kevin M. Van Geem.Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats.Engineering.Volume 7, Issue 9,2021,Pages 1201-1211,ISSN 2095-8099.
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[26]Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon.Scope of machine learning in materials research—A review.Applied Surface Science Advances.Volume 18,2023,100523,ISSN 2666-5239.
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[35]Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang-Yu Hsieh, Xiaojun Yao.RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions.Chemical Engineering Journal.Volume 420, Part 1,2021,129845,ISSN 1385-8947.
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