Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics-Reference-Cited by-同舟云学术

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

Published:2021 Issue: Volume: Page:
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Ma Zhan¹²³⁴^ORCID,Wang Shu¹²³⁴^ORCID,Kim Minhee⁵²³⁴,Liu Kaibo⁵²³⁴,Chen Chun-Long⁶⁷⁸⁴^ORCID,Pan Wenxiao¹²³⁴^ORCID

Affiliation:

1. Department of Mechanical Engineering

2. University of Wisconsin-Madison

3. Madison

4. USA

5. Department of Industrial and Systems Engineering

6. Physical Sciences Division

7. Pacific Northwest National Laboratory

8. Richland

Abstract

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters.

Funder

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SM/D1SM00364J

Reference75 articles.

1. Coarse-Graining Methods for Computational Biology

2. Coarse-Grained Protein Models and Their Applications

3. Coarse-graining simulation approaches for polymer melts: the effect of potential range on computational efficiency

4. Resolving Dynamic Properties of Polymers through Coarse-Grained Computational Studies

5. A Review of Multiscale Computational Methods in Polymeric Materials

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