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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Artificial neural networks in kinetic analysis of glass crystallization: The case of complex nucleation-growth mechanisms
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20500 - Materials engineering

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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