Multiscale Modelling of Materials Chemomechanics: Brittle Fracture of Oxides and Semiconductors

Kermode, James R.; Peralta, Giovanni; Li, Zhenwei; Vita, Alessandro De

doi:10.1016/j.mspro.2014.06.271

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…These methods are more complex since continuum fields must be constructed from atomic data but come with the added benefit of the insight derived from the fields. Innovative hybrid methods have also begun to appear using both: (a) higher scale methods like finite elements with cohesive zones enhanced with Cauchy-Born models [35][36][37] that bring continuum methods closer to first principles, and (b) ab initio methods linked with classical molecular dynamics to accurately capture the extreme configurational environments near the crack tip [21,38].…”

Section: Introductionmentioning

confidence: 99%

An atomic-scale evaluation of the fracture toughness of silica glass

Jones

Rimsza

2018

J. Phys.: Condens. Matter

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Section: Introductionmentioning

confidence: 99%

An atomic-scale evaluation of the fracture toughness of silica glass

Jones

Rimsza

2018

J. Phys.: Condens. Matter

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“…Many processes in nature involve the concerted action of phenomena taking place across different length scales, so that simulating them requires large model systems and is best achieved by adopting a nonuniform precision atomistic approach. An archetype of such problems is the chemomechanical behavior of a brittle material, in which the macroscopic mechanical stress field in a material is bidirectionally coupled with atomic‐scale chemical reactions . Consider, for example, the stress‐corrosion cracking of silica in the presence of water: molecules in the proximity of the crack tip as well as those in pores within the material are known to modify the stress field in the solid, which in turn determines the local atomic strain that influences the bond breaking reactions and diffusivity of water into the material .…”

Section: Introductionmentioning

confidence: 99%

A framework for machine‐learning‐augmented multiscale atomistic simulations on parallel supercomputers

Caccin

Kermode

et al. 2015

Int J of Quantum Chemistry

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. (2015) A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers. International Journal of Quantum Chemistry. Permanent WRAP url: http://wrap.warwick.ac.uk/68012 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work of researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher statement:"This is the peer reviewed version of the following article: Caccin, Marco, Li, Zhenwei, Kermode, James R and De Vita, Alessandro. (2015) A framework for machine-learningaugmented multiscale atomistic simulations on parallel supercomputers. International Journal of Quantum Chemistry, which has been published in final form at http://dx.doi.org/10.1002/qua.24952 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. AbstractRecent advances in quantum mechanical(QM)-based molecular dynamics simulations have used machine-learning (ML) to predict, rather than re-calculate, QMaccurate forces in atomic configurations sufficiently similar to previously encountered ones. Here, we discuss how ML approaches can be deployed within large-scale QM/MM materials simulations on massively parallel supercomputers, making QM zones of 1000 atoms routinely attainable. We argue that the ML approach allows computational effort to be concentrated on the most chemically active subregions of the QM zone, significantly improving the overall efficiency of the simulation. We thus propose a novel method to partition large QM regions into multiple subregions which can be computed in parallel to achieve optimal scaling. Then we review a recently proposed QM/ML MD scheme [Z. Li et al., Phys. Rev. Lett. 114(9), 096405 (2015)], discussing how this could be efficiently combined with QM-zone partitioning.

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Machine learning prediction on intermetallic compounds with implemented virtual-center-atom structural descriptor

Wang

2022

Science and Technology of Advanced Materials: Methods

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Multiscale Modelling of Materials Chemomechanics: Brittle Fracture of Oxides and Semiconductors

Cited by 3 publications

References 27 publications

An atomic-scale evaluation of the fracture toughness of silica glass

An atomic-scale evaluation of the fracture toughness of silica glass

A framework for machine‐learning‐augmented multiscale atomistic simulations on parallel supercomputers

Machine learning prediction on intermetallic compounds with implemented virtual-center-atom structural descriptor

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