A Machine Learning-Based Computational Architecture for Unlocking Water Dynamics in Saturated Calcium Silicate Hydrate.
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| Title: | A Machine Learning-Based Computational Architecture for Unlocking Water Dynamics in Saturated Calcium Silicate Hydrate. |
|---|---|
| Authors: | Liu, Chunlong1,2 (AUTHOR), Kang, Juntao1,2 (AUTHOR) liuqm@whut.edu.cn, Liu, Qimin1,2 (AUTHOR), Yu, Zechuan1,2 (AUTHOR) zecyui@whut.edu.cn |
| Source: | Materials (1996-1944). Jun2026, Vol. 19 Issue 12, p2631. 24p. |
| Subjects: | Calcium silicate hydrate, Diffusion coefficients, Graph neural networks, Multiscale modeling, Concrete durability, Molecular dynamics, Machine learning, Transport theory |
| Abstract: | The durability of reinforced concrete is closely related to the transport behavior of water and aggressive ions within the complex nanoporous network of calcium silicate hydrate. While molecular dynamics simulations provide critical atomistic insights into these confined transport behaviors, their immense computational cost limits their scalability to complex structural and temporal domains. To overcome this bottleneck, we propose a novel, modular computational framework that synergizes high-throughput molecular dynamics with advanced graph neural networks. By rigorously learning the mapping between the local atomic environment and kinetic behaviors, our model achieves high-fidelity predictions of pore water diffusion coefficients in saturated calcium silicate hydrate while improving computational efficiency by three orders of magnitude compared to conventional force field methods. Furthermore, the model demonstrates strong transferability and can accurately capture localized nonlinear diffusion characteristics in multiparticle pore structures with rough surfaces. Building on the interchangeability of this framework's core modules, we envision a visionary multiscale computational strategy that dynamically couples nanoscale atomistic predictions with mesoscale simulations. This work not only provides an ultrafast, highly accurate tool for screening transport properties across diverse structural configurations but also lays the groundwork for next-generation multiscale modeling of chloride ingress, ultimately advancing the design of resilient and sustainable reinforced concrete. [ABSTRACT FROM AUTHOR] |
| Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194907705 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Machine Learning-Based Computational Architecture for Unlocking Water Dynamics in Saturated Calcium Silicate Hydrate. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Chunlong%22">Liu, Chunlong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kang%2C+Juntao%22">Kang, Juntao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liuqm@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Qimin%22">Liu, Qimin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Zechuan%22">Yu, Zechuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zecyui@whut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Jun2026, Vol. 19 Issue 12, p2631. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Calcium+silicate+hydrate%22">Calcium silicate hydrate</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion+coefficients%22">Diffusion coefficients</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Multiscale+modeling%22">Multiscale modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Concrete+durability%22">Concrete durability</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+dynamics%22">Molecular dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Transport+theory%22">Transport theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The durability of reinforced concrete is closely related to the transport behavior of water and aggressive ions within the complex nanoporous network of calcium silicate hydrate. While molecular dynamics simulations provide critical atomistic insights into these confined transport behaviors, their immense computational cost limits their scalability to complex structural and temporal domains. To overcome this bottleneck, we propose a novel, modular computational framework that synergizes high-throughput molecular dynamics with advanced graph neural networks. By rigorously learning the mapping between the local atomic environment and kinetic behaviors, our model achieves high-fidelity predictions of pore water diffusion coefficients in saturated calcium silicate hydrate while improving computational efficiency by three orders of magnitude compared to conventional force field methods. Furthermore, the model demonstrates strong transferability and can accurately capture localized nonlinear diffusion characteristics in multiparticle pore structures with rough surfaces. Building on the interchangeability of this framework's core modules, we envision a visionary multiscale computational strategy that dynamically couples nanoscale atomistic predictions with mesoscale simulations. This work not only provides an ultrafast, highly accurate tool for screening transport properties across diverse structural configurations but also lays the groundwork for next-generation multiscale modeling of chloride ingress, ultimately advancing the design of resilient and sustainable reinforced concrete. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/ma19122631 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 2631 Subjects: – SubjectFull: Calcium silicate hydrate Type: general – SubjectFull: Diffusion coefficients Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Multiscale modeling Type: general – SubjectFull: Concrete durability Type: general – SubjectFull: Molecular dynamics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Transport theory Type: general Titles: – TitleFull: A Machine Learning-Based Computational Architecture for Unlocking Water Dynamics in Saturated Calcium Silicate Hydrate. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Chunlong – PersonEntity: Name: NameFull: Kang, Juntao – PersonEntity: Name: NameFull: Liu, Qimin – PersonEntity: Name: NameFull: Yu, Zechuan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961944 Numbering: – Type: volume Value: 19 – Type: issue Value: 12 Titles: – TitleFull: Materials (1996-1944) Type: main |
| ResultId | 1 |
