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Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures
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DOI：10.1016/j.actamat.2020.01.047.png)
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Abstract
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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.
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NEURAL-NETWORK PREDICTION
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