| Full text | |
| Author(s): |
Alcobaca, Edesio
[1]
;
Mastelini, Saulo Martiello
[1]
;
Botari, Tiago
[1]
;
Pimentel, Bruno Almeida
[1]
;
Cassar, Daniel Roberto
[2]
;
de Leon Ferreira de Carvalho, Andre Carlos Ponce
[1]
;
Zanotto, Edgar Dutra
[2]
Total Authors: 7
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos - Brazil
[2] Univ Fed Sao Carlos, Ctr Res Technol & Educ Vitreous Mat, Dept Mat Engn, Sao Carlos - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | ACTA MATERIALIA; v. 188, p. 92-100, APR 15 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Modern technologies demand the development of new glasses with unusual properties. Most of the previous developments occurred by slow, expensive trial-and-error approaches, which have produced a considerable amount of data over the past 100 years. By finding patterns in such types of data, Machine Learning (ML) algorithms can extract useful knowledge, providing important insights into composition-property maps. A key step in glass composition design is to identify their physical-chemical properties, such as the glass transition temperature, T-g. In this paper, we investigate how different ML algorithms can be used to predict the T-g of glasses based on their chemical composition. For such, we used a dataset of 43,240 oxide glass compositions, each one with its assigned T-g. Besides, to assess the predictive performance obtained by ML algorithms, we investigated the possible gains by tuning the hyperparameters of these algorithms. The results show that the best ML algorithm for predicting T-g is the Random Forest (RF). One of the main challenges in this task is the prediction of extreme T-g values. To do this, we assessed the predictive performance of the investigated ML algorithms in three T-g intervals. For extreme T-g values (<= 450 K and >= 1150 K), the top-performing algorithm was the k-Nearest Neighbours, closely followed by RF. The induced RF model predicted extreme values of T-g with a Relative Deviation (RD) of 3.5% for glasses with high T-g (>= 1150 K), and RD of 7.5% for glasses with very low T-g (<= 450 K). Finally, we propose a new visual approach to explain what our RF model learned, highlighting the importance of each chemical element to obtain glasses with extreme T-g. This study can be easily expanded to predict other composition-property combinations and can advantageously replace empirical approaches for developing novel glasses with relevant properties and applications. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 17/12491-0 - Kinetic processes in glass and formulation of new glasses using machine learning |
| Grantee: | Daniel Roberto Cassar |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry. |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 18/07319-6 - Multi-target data stream mining |
| Grantee: | Saulo Martiello Mastelini |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 17/06161-7 - Interpretability of Deep Networks |
| Grantee: | Tiago Botari |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 17/20265-0 - Uso de Meta-Aprendizado para Seleção de Algoritmos em Problemas de Agrupamento |
| Grantee: | Bruno Almeida Pimentel |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 13/07793-6 - CeRTEV - Center for Research, Technology and Education in Vitreous Materials |
| Grantee: | Edgar Dutra Zanotto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 18/14819-5 - Automated Machine Learning: Learning to Learn |
| Grantee: | Edesio Pinto de Souza Alcobaça Neto |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |