A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials-Reference-Cited by-同舟云学术

A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials

Published:2022 Issue:26 Volume:13 Page:7863-7872
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Lin Min¹^ORCID,Xiong Jingfang¹,Su Mintao¹,Wang Feng¹,Liu Xiangsi¹^ORCID,Hou Yifan¹,Fu Riqiang²^ORCID,Yang Yong¹³^ORCID,Cheng Jun¹⁴^ORCID

Affiliation:

1. Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory for Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China

2. National High Magnetic Field Laboratory, 1800 E. Paul Dirac Drive, Tallahassee, FL 32310, USA

3. College of Energy, Xiamen University, Xiamen 361005, China

4. Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China

Abstract

We developed a widely applicable machine learning (ML) method that can help to correlate dynamic ssNMR spectra with the local structures and transport of ions and thus expands the ssNMR application to fast chemically exchanged material systems.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D2SC01306A

Reference64 articles.

1. Rechargeable Alkali-Ion Battery Materials: Theory and Computation