Predicting initial dissolution rates using structural features from molecular dynamics simulations

Predicting the chemical durability of glass materials is important for various applications from daily life such as cell phone screens and kitchenware to advanced technologies such as nuclear waste disposal and biomedicine. In this work, we explored the prediction of the initial glass dissolution rates using structural features from molecular dynamics (MD) simulations for a series of glass compositions (total 28), including ZrO2- and V2O5-containing boroaluminosilicate, borosilicate, and aluminosilicate glasses. The initial dissolution rates (r(0)) measured experimentally at 90 degrees C with varying solution conditions were correlated with structural features (e.g., polyhedral linkages and non-bridging oxygen species) obtained from MD simulations, either from this study or from literature. As hydrolysis of the glass network through breaking of the network former linkages (e.g., Si-O-Si, Si-O-Al, etc.) is a critical step of network glass dissolution, the statistics of these linkages obtained from MD were also correlated to r(0) through linear regression, where the coefficients of determination (R-2) and root mean square error are found to be 0.949 and 0.681, respectively. This model was compared and discussed with existing models developed by various approaches, including machine learning, the kinetic rate equation, topological constraint theory, and other descriptors from MD simulations. The discussion provides insights on future model improvements to predict glass dissolution. In addition, as the effect of V2O5 on glass dissolution was not well studied comparing to ZrO2, the impact of V2O5 was emphasized in this paper, suggesting that the impact is not the same across all glass compositions and test conditions.

Automated summary

Predicting the chemical durability of glass materials is crucial for various applications. This study investigates the prediction of glass dissolution rates through molecular dynamics simulations and provides insights on improving existing models.