Machine learning models accelerate deep eutectic solvent discovery for the recycling of lithium-ion battery cathodes-Reference-Cited by-同舟云学术

Machine learning models accelerate deep eutectic solvent discovery for the recycling of lithium-ion battery cathodes

Published:2024 Issue:13 Volume:26 Page:7857-7868
ISSN:1463-9262
Container-title:Green Chemistry
language:en
Short-container-title:Green Chem.

Author:

Zhou Fengyi¹,Shi Dingyi²,Mu Wenbo³,Wang Shao¹,Wang Zeyu¹,Wei Chenyang¹,Li Ruiqi²,Mu Tiancheng¹^ORCID

Affiliation:

1. Department of Chemistry, Renmin University of China, Beijing 100872, China

2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

3. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093-0404, USA

Abstract

Deep learning model Conditional Generative Adversarial Network (CGAN) was used to design deep eutectic solvent (DES) based green process for lithium-ion cathode recycling, and the importance of acidity, coordination, and reducibility were quantified.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/GC/D4GC01418A

Reference57 articles.

1. The predicted persistence of cobalt in lithium-ion batteries

2. Next‐Generation Cobalt‐Free Cathodes – A Prospective Solution to the Battery Industry's Cobalt Problem

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