Multi-objective optimization strategy for green solvent design <i>via</i> a deep generative model learned from pre-set molecule pairs-Reference-Cited by-同舟云学术

Multi-objective optimization strategy for green solvent design via a deep generative model learned from pre-set molecule pairs

Published:2024 Issue:1 Volume:26 Page:412-427
ISSN:1463-9262
Container-title:Green Chemistry
language:en
Short-container-title:Green Chem.

Author:

Zhang Jun¹,Wang Qin²,Wen Huaqiang¹,Gerbaud Vincent³^ORCID,Jin Saimeng¹^ORCID,Shen Weifeng¹^ORCID

Affiliation:

1. School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China

2. School of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

3. Laboratoire de Génie Chimique, Université de Toulouse, CNRS, INP, UPS, Toulouse, France

Abstract

Green solvent design is usually a multi-objective optimization problem that requires identification of a set of solvent molecules to balance multiple, often trade-off, properties.

Funder

National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

National Natural Science Foundation of China

Chongqing University

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Environmental Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2024/GC/D3GC04354A

Reference70 articles.

1. Computer-aided multi-objective optimization in small molecule discovery

2. Computer-aided naphtha liquid–liquid extraction: Molecular reconstruction, sustainable solvent design and multiscale process optimization

3. Computer-aided molecular design of solvents for chemical separation processes

4. Sustainable solvent selection for the manufacture of methylammonium lead triiodide (MAPbI3) perovskite solar cells

5. Green chemistry: challenges and opportunities

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