Machine learning glass caging order parameters with an artificial nested neural network-Reference-Cited by-同舟云学术

Machine learning glass caging order parameters with an artificial nested neural network

Published:2022 Issue:33 Volume:18 Page:6270-6277
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Zhang Kaihua¹,Li Xinyang²³,Jin Yuliang²³⁴^ORCID,Jiang Ying¹⁵^ORCID

Affiliation:

1. School of Chemistry, Beihang University, Beijing 100191, China

2. CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China

3. School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

4. Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325000, China

5. Center of Soft Matter Physics and Its Applications, Beihang University, Beijing 100191, China

Abstract

A two-level nested neural network is designed to learn caging order parameters for three transitions in glasses. The method can also identify the order of phase transitions, and distinguish dynamical crossovers from phase transitions.

Funder

Chinese Academy of Sciences

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SM/D2SM00310D

Reference46 articles.

1. Formation of Glasses from Liquids and Biopolymers

2. The role of local structure in dynamical arrest

3. Revealing key structural features hidden in liquids and glasses

4. Bond-orientational order in liquids and glasses

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