Authors
Ryuki Kayano, Jean-Marc Delaye, Stéphane Gin, Faijan, Takahiro Ohkubo
Published in
ACS applied materials & interfaces. Volume 18. Issue 37. Pages 51783-51797. Sep 23, 2026.
Abstract
Understanding the hydration reactions at borosilicate glass-water interfaces is essential for predicting long-term chemical durability. However, atomistic simulations of multicomponent glass corrosion require both chemical accuracy and nanosecond-scale sampling, which remain difficult to achieve using conventional force fields or ab initio molecular dynamics. This paper describes the development and validation of a machine learning potential (MLP) for multicomponent borosilicate glass-water interfaces containing Si, Al, B, Zr, Na, Ca, Li, Mg, O, and H. This MLP enables nanosecond-scale machine learning molecular dynamics (MLMD) simulations with accuracy close to that of density functional theory. Benchmark simulations of sodium aluminosilicate and calcium aluminosilicate glass-water interfaces demonstrate that the MLP reproduces hydration-induced changes in Al coordination more accurately than available classical potentials. The validated MLP is subsequently applied to multicomponent (ISG-1) glass-water interface models to investigate the initial dissolution behavior of boron. Surface boron atoms primarily react with water through the formation of hydroxylated species. A reaction network analysis of three- and four-coordinated B species, B[3] and B[4], based on oxygen species labeling, reveals that B[3]-OH and B[4]-OH form through distinct pathways: the B[3]-OH species are mainly formed through ion exchange reactions involving modifier cations, whereas B[4]-OH species form through direct water attachment to B[3] followed by deprotonation. The reactivity of B[3] sites is strongly dependent on the type and number of second-nearest-neighbor cations, with boron sites linked to Al or Zr exhibiting markedly lower reactivity than those linked to Si or B[3], indicating site-selective and incongruent initial boron dissolution. These results provide a quantitative atomistic basis for understanding how glass composition controls boron dissolution at multicomponent glass-water interfaces.
PMID:
42902487
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.
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