Artificial neural networks in kinetic analysis of glass crystallization: The case of complex nucleation-growth mechanisms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25310%2F24%3A39922021" target="_blank" >RIV/00216275:25310/24:39922021 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0022309323006671?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0022309323006671?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jnoncrysol.2023.122802" target="_blank" >10.1016/j.jnoncrysol.2023.122802</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial neural networks in kinetic analysis of glass crystallization: The case of complex nucleation-growth mechanisms

Popis výsledku v původním jazyce

The selected artificial neural networks were trained and tested to determine the kinetics of theoretically simulated signals for two overlapping independent nucleation-growth processes. Whereas the hybrid convolutional neural network did not perform well, the multilayer perceptron (MLP) showed great potential for the kinetic analysis of complex solid-state reactions and transformation mechanisms. In particular, the MLP architecture exhibited remarkable robustness with respect to the scatter in kinetic data as well as the ability to accurately deal with practically fully overlapping kinetic peaks. When trained on a full spectrum of double-process overlaps, the MLP architecture returned very precise estimates of the kinetic parameters during the testing phase despite the limited data sample used for some of the training. This level of accuracy was observed in the case of both overlapping processes being roughly similarly sized, and for the dominant process in the cases of the two processes being largely disproportionate in magnitude.

Název v anglickém jazyce

Artificial neural networks in kinetic analysis of glass crystallization: The case of complex nucleation-growth mechanisms

Popis výsledku anglicky

The selected artificial neural networks were trained and tested to determine the kinetics of theoretically simulated signals for two overlapping independent nucleation-growth processes. Whereas the hybrid convolutional neural network did not perform well, the multilayer perceptron (MLP) showed great potential for the kinetic analysis of complex solid-state reactions and transformation mechanisms. In particular, the MLP architecture exhibited remarkable robustness with respect to the scatter in kinetic data as well as the ability to accurately deal with practically fully overlapping kinetic peaks. When trained on a full spectrum of double-process overlaps, the MLP architecture returned very precise estimates of the kinetic parameters during the testing phase despite the limited data sample used for some of the training. This level of accuracy was observed in the case of both overlapping processes being roughly similarly sized, and for the dominant process in the cases of the two processes being largely disproportionate in magnitude.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20500 - Materials engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Non-Crystalline Solids

ISSN

0022-3093

e-ISSN

1873-4812

Svazek periodika

626

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

122802

Kód UT WoS článku

001166140600001

EID výsledku v databázi Scopus

2-s2.0-85181112957