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

LIU Yanbiao; QIAO Jianzhi; YOU Shijie

doi:10.13205/j.hjgc.202206023

Volume 40 Issue 6

Sep. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2022 > 40(6): 182-187

LIU Yanbiao, QIAO Jianzhi, YOU Shijie. RESEARCH PROGRESS ON APPLICATIONS OF MACHINE LEARNING IN CARBON-BASED ENVIRONMENTAL FUNCTIONAL MATERIALS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 182-187. doi: 10.13205/j.hjgc.202206023

Citation:

LIU Yanbiao, QIAO Jianzhi, YOU Shijie. RESEARCH PROGRESS ON APPLICATIONS OF MACHINE LEARNING IN CARBON-BASED ENVIRONMENTAL FUNCTIONAL MATERIALS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 182-187. doi: 10.13205/j.hjgc.202206023

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

doi: 10.13205/j.hjgc.202206023

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

Received Date: 2022-01-17
Available Online: 2022-09-01
Publish Date: 2022-09-01

Abstract

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

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References(34)

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