A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties-Reference-Cited by-同舟云学术

A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties

Published:2020 Issue:12 Volume:22 Page:3867-3876
ISSN:1463-9262
Container-title:Green Chemistry
language:en
Short-container-title:Green Chem.

Author:

Wang Zihao¹²³⁴^ORCID,Su Yang¹²³⁴^ORCID,Jin Saimeng¹²³⁴^ORCID,Shen Weifeng¹²³⁴^ORCID,Ren Jingzheng⁵⁶⁴,Zhang Xiangping⁷⁸⁹¹⁰¹¹^ORCID,Clark James H.¹²¹³¹⁴¹⁵^ORCID

Affiliation:

1. School of Chemistry and Chemical Engineering

2. Chongqing University

3. Chongqing 400044

4. People's Republic of China

5. Department of Industrial and Systems Engineering

6. The Hong Kong Polytechnic University

7. Beijing Key Laboratory of Ionic Liquids Clean Process

8. CAS Key Laboratory of Green Process and Engineering

9. Institute of Process Engineering

10. Chinese Academy of Sciences

11. Beijing

12. Green Chemistry Centre of Excellence

13. University of York

14. York YO105DD

15. UK

Abstract

A novel strategy of molecular feature extraction coupling with the machine learning algorithm for developing predictive models of environmental properties.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Environmental Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/GC/D0GC01122C

Reference50 articles.

1. Green chemistry: challenges and opportunities

2. Green chemistry: today (and tomorrow)