Glass transition temperature of chalcogenides predicted by large neural network models

BELCIU, M. I.; VELEA, A.; NEMNES, G. A.

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dc.contributor.author	BELCIU, M. I.
dc.contributor.author	VELEA, A.
dc.contributor.author	NEMNES, G. A.
dc.date.accessioned	2026-10-13T17:17:30Z
dc.date.available	2026-10-13T17:17:30Z
dc.date.issued	2026
dc.identifier.citation	BELCIU, M. I.; A. VELEA and G. A. NEMNES. Glass transition temperature of chalcogenides predicted by large neural network models. In: 10th International Symposium on Dielectric Materials and Application, Abstract Book, ISyDMA’10, Chisinau, Republic of Moldova, 30 June -4 July, 2026. Technical University of Moldova. Chişinău: 2026, p. 57.	en_US
dc.identifier.uri	https://repository.utm.md/handle/5014/37372
dc.description	This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI, project number PN-IV-P1-PCE-2023-1785 (60 PCE/2025), within PNCDI IV.	en_US
dc.description.abstract	Chalcogenide glasses are versatile and complex amorphous inorganic materials with exceptional mechanical, thermal and optoelectronic properties. Among their modern technological applications is the integration in storage class memory devices as both phase change memory (PCM) and ovonic threshold selector components (OTS) [1]. The current research is undergoing the transition from the binary 1PCM-1OTS architecture to more performant and scalable selector-only memory structure.	en_US
dc.language.iso	en	en_US
dc.publisher	Technical University of Moldova	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	chalcogenide glasses	en_US
dc.title	Glass transition temperature of chalcogenides predicted by large neural network models	en_US
dc.type	Article	en_US