机器学习在碳基环境功能材料领域的应用研究进展

刘艳彪; 乔建质; 尤世界

doi:10.13205/j.hjgc.202206023

机器学习在碳基环境功能材料领域的应用研究进展

doi: 10.13205/j.hjgc.202206023

1. 东华大学环境科学与工程学院国家环境保护纺织工业污染防治工程技术中心, 上海 201620;
2. 上海污染控制与生态安全研究院, 上海 201620;
3. 哈尔滨工业大学环境学院城市水资源与水环境国家重点实验室, 哈尔滨 150090

基金项目:

国家自然科学基金面上项目(52170068)

详细信息

作者简介:
刘艳彪(1982-),男,博士,教授,主要从事环境纳米技术方向的研究。yanbiaoliu@dhu.edu.cn

通讯作者:
刘艳彪(1982-),男,博士,教授,主要从事环境纳米技术方向的研究。yanbiaoliu@dhu.edu.cn

计量
- 文章访问数: 113
- HTML全文浏览量: 13
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-17
- 网络出版日期: 2022-09-01
- 刊出日期: 2022-09-01

RESEARCH PROGRESS ON APPLICATIONS OF MACHINE LEARNING IN CARBON-BASED ENVIRONMENTAL FUNCTIONAL MATERIALS

1. Textile Pollution Controlling Engineering Center of the Ministry of Ecology and Environment, College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China;
2. Shanghai Institute of Pollution Control and Ecological Security, Shanghai 201620, China;
3. State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China

摘要

摘要: 随着环境功能材料领域产生的数据量及其数据复杂性急剧增加,高成本、长周期的传统实验手段已无法迎合目前功能材料的发展趋势。近年来迅速发展的机器学习能对数据进行深入挖掘和解析,有望为此类问题提供有效的解决方案。机器学习具备效率高、精度高等优势,有效弥补了传统"试错"方式的不足。介绍了机器学习的基本工作原理和算法,从预测理化性质、辅助微观表征和指导新型材料合成3个方面简述了机器学习在环境碳基功能材料领域中的应用研究进展,分析了机器学习在该领域的问题与挑战,展望了机器学习方法在环境碳基功能材料领域的前景与发展趋势。
- 机器学习 /
- 环境功能材料 /
- 性质预测 /
- 结构表征 /
- 设计合成
Abstract: With the rapid increase in the capacity and complexity of data generated in the field of environmental functional materials,the high cost and long cycle time of traditional experimental methods can no longer meet the current trend of functional materials.The rapid development of machine learning in recent years can dig deeper and analyze the data,which provides an effective solution.Machine learning has the advantages of high efficiency and accuracy,which effectively compensates for the shortcomings of the traditional "trial and error" strategy.This paper outlines the basic working principles and algorithms of machine learning,summarizes the recent advances in machine learning in the field of carbon-based environmental functional materials (e.g.,predicting physicochemical properties,assisting structural characterization as well as guiding the synthesis of advanced functional materials),and presents the existing problems and challenges of machine learning in this field.Future perspectives of machine learning in environmental functional materials is analyzed as well.
- machine learning /
- environmental functional materials /
- characteristics prediction /
- structural characterization /
- design and preparation

HTML全文

参考文献(34)

[1]	JORDAN M I, MITCHELL T M. Machine learning:trends, perspectives, and prospects[J]. Science, 2015, 349(6245):255-260.
[2]	YAN X L, SEDYKH A, WANG W Y, et al. Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations[J]. Nature Communications, 2020, 11(1):2519.
[3]	BUTLER K T, DAVIES D W, CARTWRWRIGHT H, et al. Machine learning for molecular and materials science[J]. Nature, 2018, 559(7715):547-555.
[4]	MITCHELL T M. Machine Learning[M]. London:McGraw-Hill Education-Eroupe, 1997.
[5]	MASSIMO B, DAVIDE M, MATTIA N, et al. Machine learning in manufacturing and industry 4.0 applications[J]. International Journal of Production Research, 2021, 59(16):4773-4778.
[6]	ZHANG S W. Application of machine learning in animal disease analysis and prediction[J]. Current Bioinformatics, 2021, 16(7):972-982.
[7]	ROBERT J M, STEPHEN R G, CRAIG G N, et al. Applying machine learning to agricultural data[J]. Computers and Electronics in Agriculture, 1995, 4(12):275-293.
[8]	FANG J, SWAIN A, UNNI R, et al. Decoding optical data with machine learning[J]. Laser&Photonics Reviews, 2020, 15(2):2000422.
[9]	ZHONG S F, ZHANG K, BAGHERI M, et al. Machine learning:new ideas and tools in environmental science and engineering[J]. Environmental Science&Technology, 2021, 55(19):12741-12754.
[10]	LONG F, WANG L G, CAI W F, et al. Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data[J]. Water Research, 2021, 199:117182.
[11]	HUANG R X, MA C X, MA J, et al. Machine learning in natural and engineered water systems[J]. Water Research, 2021, 205:117666.
[12]	OBENG-GYAS E, ROOSTAEI J, GIBSON J M. Lead distribution in urban soil in a medium-sized city:household-scale analysis[J]. Environmental Science&Technology, 2021, 55(6):3696-3705.
[13]	TAPAVICZA E, RUDORFF G F, DAVID O D H, et al. Elucidating an atmospheric brown carbon species-Toward supplanting chemical intuition with exhaustive enumeration and machine learning[J]. Environmental Science&Technology, 2021, 55(12):8447-8457.
[14]	ZHU J J, CHEN Y C, SHIE R H, et al. Predicting carbonaceous aerosols and identifying their source contribution with advanced approaches[J]. Chemosphere, 2021, 266:128966.
[15]	KUSDHANY M I M, STEPHEN M L. New insights into hydrogen uptake on porous carbon materials via explainable machine learning[J]. Carbon, 2021, 179:190-201.
[16]	ZHANG K, ZHONG S F, ZHANG H C. Predicting aqueous adsorption of organic compounds onto biochars, carbon nanotubes, granular activated carbons, and resins with machine learning[J]. Environmental Science&Technology, 2020, 54(11):7008-7018.
[17]	MORTAZAVI B, RAJABPOUR A, ZHUANG X Y, et al. Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials[J]. Carbon, 2022, 186:501-508.
[18]	CHENG Q, BEN M, DANIEL H, et al. A comprehensive assessment of empirical potentials for carbon materials[J]. APL Materials, 2021, 9(6):061102.
[19]	LUO Q X, HOLM E A, WANG C. A transfer learning approach for improved classification of carbon nanomaterials from TEM images[J]. Nanoscale Advances, 2021,3:206-213.
[20]	MASTON T, FARFEL M, LEVIN N, et al. Machine learning and computer vision for the classification of carbon nanotube and nanofiber structures from transmission electron microscopy data[J]. Microscopy and Microanalysis, 2019, 25(S2):198-199.
[21]	DERINGER V L, MERLET C, HU Y C, et al. Towards an atomistic understanding of disordered carbon electrode materials†[J]. Chemical Communications, 2018,54:5988-5991.
[22]	ANJA A, VOLKER L D, SAMI S, et al. Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part Ⅰ:Fingerprint Spectra[J]. Chemistry of Materials, 2019, 31(22):9243-9255.
[23]	ANJA A, VOLKER L D, SAMI S, et al. Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part Ⅱ:Quantitative Fitting of Spectra[J]. Chemistry of Materials, 2019, 31(22):9256-9267.
[24]	TESSONNIER J P, SU D S. Recent progress on the growth mechanism of carbon nanotubes:a review[J]. ChemSusChem, 2011, 4(7):824-847.
[25]	BECKHAM J L, WYSS K M, XIE Y C, et al. Machine learning guided synthesis of flash graphene[J]. Advanced Materials, 2022, 34(12):2106506.
[26]	YU Z C, HUANG W M. Accelerating optimizing the design of carbon-based electrocatalyst via machine learning[J]. Electroanalysis, 2021, 33:1-10.
[27]	何峰,张静静,陈奕君,等.电化学氧还原反应合成H₂O₂碳基催化剂研究进展[J].储能科学与技术, 2021, 10(6):1963-1976.
[28]	HAN Y, TANG B J, WANG L, et al. Machine-learning-driven synthesis of carbon dots with enhanced quantum yields[J]. ACS Nano, 2020, 14(11):14761-14768.
[29]	KUHN M, JOHNSON K. Applied Predictive Modeling[M]. Springer, 2013.
[30]	SHORTEN C, KHOSHGOFTAAR T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 2019, 6(1):60.
[31]	VARMA S, SIMON R. Bias in error estimation when using cross-validation for model selection[J]. BMC Bioinformatics, 2006, 7(1):91.
[32]	BVHLMANN P, GEER S V. Statistics for high-dimensional data:methods, theory and applications[M]. Springer Science&Business Media, 2011.
[33]	REXSTAD E, INNIS G S. Model simplification-three applications[J]. Ecological Modelling, 1985, 27(1/2):1-13.
[34]	YAO Y, ROSASCO L, CAPONNETTO A. On early stopping in gradient descent learning[J]. Constructive Approximation, 2007, 26(2):289-315.