Prediction of zwitterion hydration and ion association properties using machine learning-Reference-Cited by-同舟云学术

Prediction of zwitterion hydration and ion association properties using machine learning

Published:2023 Issue:17 Volume:19 Page:3179-3189
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Christiansen Daniel¹,Cheng Gang¹^ORCID,Mehraeen Shafigh¹^ORCID

Affiliation:

1. Department of Chemical Engineering, University of Illinois at Chicago, 929 West Taylor Street, Chicago, Illinois 60607, USA

Abstract

Hydration and ion association data for a library of zwitterions was produced by molecular dynamics simulations. Machine learning was applied to reveal how chemical design influences target properties.

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/SM/D3SM00062A

Reference43 articles.

1. Zwitterionic materials for antifouling membrane surface construction

2. Ultralow-Fouling Zwitterionic Polyurethane-Modified Membranes for Rapid Separation of Plasma from Whole Blood

3. The zwitterion effect in high-conductivity polyelectrolyte materials

4. The Zwitterion Effect in Ionic Liquids: Towards Practical Rechargeable Lithium-Metal Batteries

5. Enhancing the ionic conductivity and mechanical properties of zwitterionic polymer electrolytes by betaine-functionalized graphene oxide for high-performance and flexible supercapacitors

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