Autor(es)

Cassar, Daniel R. ^[1] ; Santos, Gisele G. ^[1] ; Zanotto, Edgar D. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Dept Mat Engn, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: CERAMICS INTERNATIONAL; v. 47, n. 8, p. 10555-10564, APR 15 2021.
• Citações Web of Science: 2

Resumo

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data-and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T-g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n(d)) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high nd (1.7 or more) and low T-g (500 degrees C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses. (AU)

• Processo FAPESP: 13/07793-6 - CeRTEV - Centro de Pesquisa, Tecnologia e Educação em Materiais Vítreos
• Beneficiário: Edgar Dutra Zanotto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 17/12491-0 - Processos cinéticos em vidros e novas formulações vítreas via aprendizagem de máquina
• Beneficiário: Daniel Roberto Cassar
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado