Exploring multi-scale convolutional neural network for modeling amorphous materials behavior: a comparative approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F25%3A39923523" target="_blank" >RIV/00216275:25530/25:39923523 - isvavai.cz</a>

Result on the web

<a href="https://academic.oup.com/jigpal/article-abstract/34/1/jzaf031/8363935" target="_blank" >https://academic.oup.com/jigpal/article-abstract/34/1/jzaf031/8363935</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/jigpal/jzaf031" target="_blank" >10.1093/jigpal/jzaf031</a>

Result language

angličtina

Original language name

Exploring multi-scale convolutional neural network for modeling amorphous materials behavior: a comparative approach

Original language description

Glass transitions are a crucial phenomena in amorphous materials with potential for various applications. The Tool-Narayanaswamy-Moynihan model is a widely used empirical model that describes the enthalpy relaxation behavior of these materials. However, determining the appropriate values for its parameters can be challenging. To address the issue, this research explores the use of deep learning to describe the kinetics of enthalpy relaxation and glass transition. More specifically, a multi-scale convolutional neural network (MCNN) model is proposed, which outperforms traditional methods in extracting kinetic information from differential scanning calorimetry data. The MCNN model&apos;s robust architecture captures both low-frequency and high-frequency signal components, making it highly effective for complex pattern recognition in thermal analysis. The study&apos;s findings have significant implications for the utilization of amorphous materials in high-tech and classical engineering applications.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10403 - Physical chemistry

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Logic Journal of the IGPL

ISSN

1367-0751

e-ISSN

1368-9894

Volume of the periodical

34

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

16

Pages from-to

12-2025

UT code for WoS article

001630295500001

EID of the result in the Scopus database

2-s2.0-105023826383