Abstract
We conducted a comprehensive study to investigate the performance of various machine-learning models in predicting the chemical durability of oxide glasses under different chemical conditions with glass composition as input features, by taking advantage of the large dataset (~1400 datapoints) we have collected. Two typical machine-learning tasks, weight loss regression, and surface appearance change rating classification, were conducted in the study. We successfully made Neural Networks delivered an excellent performance in predicting the weight loss, while Random Forest in classifying the surface appearance change rating. Additionally, feature importance analysis showed that SiO2, Na2O, P2O5 were the most dominate features for predicting the weight loss, while SiO2, ZrO2, CaO were the topmost features for classifying the surface appearance change rating, under acid, HF, and base testing conditions, respectively. We also realized that the trained models fall short of extrapolating data far from the training dataset space even though they exhibit outstanding interpolation performance in some cases. Topology constrained theory fed by structural information from molecular dynamics simulations seems to be a promising approach to address the challenge.
Highlights
The chemical durability of oxide glasses has been commonly perceived to be superior to most other materials, which promotes its wide application over the history of human civilization[1]
To meet the ever-increasing demands for more advanced glasses exposed to various extreme chemical environments, it is important and urgent to fundamentally understand the glass dissolution kinetics, mechanisms of glasses being attacked by different chemicals, and guidelines to control the chemical durability of glasses
We collected data on the chemical durability of ~1400 oxide glass compositions under various chemical testing conditions that have been conducted over the past decade at Corning Incorporated
Summary
The chemical durability of oxide glasses has been commonly perceived to be superior to most other materials, which promotes its wide application over the history of human civilization[1]. Du et al.[7] investigate the atomic origin of the passivation effect in hydrated silicate glass using reactive molecular dynamics simulations. They propose that the passivation propensity is intrinsically governed by the reorganization of the medium-range order structure of gel upon aging and the formation of small silicate rings that hinder water mobility. The insights from these fundamental studies largely boost our understanding of the glass chemical durability, it is still extremely challenging to effectively leverage them to innovate new glass products with advanced properties. Ren et al.[11] successfully leveraged ML modeling and high-throughput experiments to discover three new material glass systems with large glass-forming ability. Different from other conventional physics-based modeling approaches with the aim of understanding specific aspect of material that are otherwise difficult to probe by experiments[12,13,14,15,16,17], ML models have the advantage of handling and comprehending high-dimensional feature space, and have great potentials in advancing our understanding of various materials[18,19,20]
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