Predicting initial dissolution rates using structural features from molecular dynamics simulations
Abstract
Predicting chemical durability of glass materials is important for various applications from daily life such as drink glass and kitchen ware to advanced technologies such as nuclear waste disposal and biomedicine. In this work, we explored prediction of initial dissolution rate through structural features from molecular dynamics (MD) simulations for a wide range of glass compositions (total 28) including borosilicate and aluminosilicate glasses, ZrO2-containing and V2O5-containing boroaluminosilicate glasses. The initial dissolution rates (r0) measured experimentally at 90 °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. Since 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 correlated to r0 through linear regression, where the coefficient of determination (R2) 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 modelmore »
- Authors:
-
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Univ. of North Texas, Denton, TX (United States)
- Univ. of Montpellier (France)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Energy Frontier Research Center (EFRC) (United States). Center for Performance and Design of Nuclear Waste Forms and Containers
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1923312
- Report Number(s):
- PNNL-SA-176987
Journal ID: ISSN 0002-7820
- Grant/Contract Number:
- AC05-76RL01830; SC0016584
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of the American Ceramic Society
- Additional Journal Information:
- Journal Volume: 106; Journal Issue: 2; Journal ID: ISSN 0002-7820
- Publisher:
- American Ceramic Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; atomistic simulation; glass dissolution; polyhedral linkage; vanadium oxide
Citation Formats
Lu, Xiaonan, Deng, Lu, Gin, Stéphane, Parruzot, Benjamin, Reiser, Joelle T., Ryan, Joseph V., Vienna, John D., and Du, Jincheng. Predicting initial dissolution rates using structural features from molecular dynamics simulations. United States: N. p., 2022.
Web. doi:10.1111/jace.18850.
Lu, Xiaonan, Deng, Lu, Gin, Stéphane, Parruzot, Benjamin, Reiser, Joelle T., Ryan, Joseph V., Vienna, John D., & Du, Jincheng. Predicting initial dissolution rates using structural features from molecular dynamics simulations. United States. https://doi.org/10.1111/jace.18850
Lu, Xiaonan, Deng, Lu, Gin, Stéphane, Parruzot, Benjamin, Reiser, Joelle T., Ryan, Joseph V., Vienna, John D., and Du, Jincheng. Mon .
"Predicting initial dissolution rates using structural features from molecular dynamics simulations". United States. https://doi.org/10.1111/jace.18850. https://www.osti.gov/servlets/purl/1923312.
@article{osti_1923312,
title = {Predicting initial dissolution rates using structural features from molecular dynamics simulations},
author = {Lu, Xiaonan and Deng, Lu and Gin, Stéphane and Parruzot, Benjamin and Reiser, Joelle T. and Ryan, Joseph V. and Vienna, John D. and Du, Jincheng},
abstractNote = {Predicting chemical durability of glass materials is important for various applications from daily life such as drink glass and kitchen ware to advanced technologies such as nuclear waste disposal and biomedicine. In this work, we explored prediction of initial dissolution rate through structural features from molecular dynamics (MD) simulations for a wide range of glass compositions (total 28) including borosilicate and aluminosilicate glasses, ZrO2-containing and V2O5-containing boroaluminosilicate glasses. The initial dissolution rates (r0) measured experimentally at 90 °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. Since 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 correlated to r0 through linear regression, where the coefficient of determination (R2) 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, the impact of V2O5 on the glass dissolution was examined in detail, implicating that the impact is not the same across all glass compositions and test conditions.},
doi = {10.1111/jace.18850},
journal = {Journal of the American Ceramic Society},
number = 2,
volume = 106,
place = {United States},
year = {Mon Oct 17 00:00:00 EDT 2022},
month = {Mon Oct 17 00:00:00 EDT 2022}
}
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