Borates and borosilicates are potential candidates for the design and development of glass formulations with important industrial and technological applications. A major challenge that retards the pace of development of borate/borosilicate based glasses using predictive modeling is the lack of reliable computational models to predict the structure‐property relationships in these glasses over a wide compositional space. A major hindrance in this pursuit has been the complexity of boron‐oxygen bonding due to which it has been difficult to develop adequate B–O interatomic potentials. In this article, we have evaluated the performance of three B–O interatomic potential models recently developed by Bauchy et al [
Machine learning (ML) is emerging as a powerful tool to predict the properties of materials, including glasses. Informing ML models with knowledge of how glass composition affects short-range atomic structure has the potential to enhance the ability of composition-property models to extrapolate accurately outside of their training sets. Here, we introduce an approach wherein statistical mechanics informs a ML model that can predict the non-linear composition-structure relations in oxide glasses. This combined model offers an improved prediction compared to models relying solely on statistical physics or machine learning individually. Specifically, we show that the combined model accurately both interpolates and extrapolates the structure of Na2O–SiO2glasses. Importantly, the model is able to extrapolate predictions outside its training set, which is evidenced by the fact that it is able to predict the structure of a glass series that was kept fully hidden from the model during its training.
more » « less- NSF-PAR ID:
- 10370667
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract J .Non‐Cryst. Solids , 2018, 498, 294–304], Du et al [J. Am. Ceram. Soc. https://doi.org/10.1111/jace.16082 ] and Edèn et al [Phys. Chem. Chem. Phys. , 2018, 20, 8192–8209] aiming to reproduce the short‐to‐medium range structures of sodium borosilicate glasses in the system 25 Na2Ox B2O3(75 −x ) SiO2(x = 0‐75 mol%). To evaluate the different force fields, we have computed at the density functional theory level the NMR parameters of11B,23Na, and29Si of the models generated with the three potentials and the simulated MAS NMR spectra compared with the experimental counterparts. It was observed that the rigid ionic models proposed by Bauchy and Du can both reliably reproduce the partitioning between BO3and BO4species of the investigated glasses, along with the local environment around sodium in the glass structure. However, they do not accurately reproduce the second coordination sphere of silicon ions and the Si–O–T (T = Si, B) and B‐O‐T distribution angles in the investigated compositional space which strongly affect the NMR parameters and final spectral shape. On the other hand, the core‐shell parameterization model proposed by Edén underestimates the fraction of BO4species of the glass with composition 25Na2O 18.4B2O356.6SiO2but can accurately reproduce the shape of the11B and29Si MAS‐NMR spectra of the glasses investigations due to the narrower B–O–T and Si‐O‐T bond angle distributions. Finally, the effect of the number of boron atoms (also distinguishing the BO3and BO4units) in the second coordination sphere of the network former cations on the NMR parameters have been evaluated. -
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