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Kinetic processes in glass and formulation of new glasses using machine learning

Grant number: 17/12491-0
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): October 01, 2017
Effective date (End): September 30, 2020
Field of knowledge:Engineering - Materials and Metallurgical Engineering
Principal Investigator:Edgar Dutra Zanotto
Grantee:Daniel Roberto Cassar
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated research grant:13/07793-6 - CEPIV - Center for Teaching, Research and Innovation in Glass, AP.CEPID

Abstract

Heated glasses undergo a variety of kinetic processes that are very important both scientifically and technologically, such as crystallization. When controlled, crystallization is the key to make glass-ceramics but it must be avoided in order to make a glass. In this research we will study some microscopic aspects of crystallization. For example, we want to investigate the eluding "diffusional entity" that controls this process. We are also interested in the initial stages of formation of a critical crystal nucleus, as well as in the average time to form the first critical nucleus. In addition, we seek to develop an artificial neural network (ANN) that is able to predict the glass forming ability (GPA) of complex, multi-component compositions. GPA is inversely related to the easiness to crystallize. With this ANN and forty years of our group's accumulated knowledge, we hope to accelerate the development of new and interesting glass composition which may display unusual properties.

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CASSAR, DANIEL R. Solving the classical nucleation theory with respect to the surface energy. Journal of Non-Crystalline Solids, v. 511, p. 183-185, MAY 1 2019. Web of Science Citations: 0.
CASSAR, DANIEL R.; DE CARVALHO, ANDRE C. P. L. F.; ZANOTTO, EDGAR D. Predicting glass transition temperatures using neural networks. ACTA MATERIALIA, v. 159, p. 249-256, OCT 15 2018. Web of Science Citations: 4.
ZANOTTO, EDGAR D.; CASSAR, DANIEL R. The race within supercooled liquids-Relaxation versus crystallization. Journal of Chemical Physics, v. 149, n. 2 JUL 14 2018. Web of Science Citations: 2.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.