Exploring multi-scale convolutional neural network for modeling amorphous materials behavior: a comparative approach
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Exploring multi-scale convolutional neural network for modeling amorphous materials behavior: a comparative approach
Popis výsledku v původním jazyce
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'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's findings have significant implications for the utilization of amorphous materials in high-tech and classical engineering applications.
Název v anglickém jazyce
Exploring multi-scale convolutional neural network for modeling amorphous materials behavior: a comparative approach
Popis výsledku anglicky
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'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's findings have significant implications for the utilization of amorphous materials in high-tech and classical engineering applications.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10403 - Physical chemistry
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Logic Journal of the IGPL
ISSN
1367-0751
e-ISSN
1368-9894
Svazek periodika
34
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
16
Strana od-do
12-2025
Kód UT WoS článku
001630295500001
EID výsledku v databázi Scopus
2-s2.0-105023826383