Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile Article Swipe
Rajni Chahal
,
Michael D. Toomey
,
Logan T. Kearney
,
Ada Sedova
,
Joshua T. Damron
,
Amit K. Naskar
,
Santanu Roy
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1021/acsami.4c04491
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1021/acsami.4c04491
Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogues with sustainable ab initio accuracy across scales.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acsami.4c04491
- OA Status
- green
- Cited By
- 6
- References
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- Related Works
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- OpenAlex ID
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https://doi.org/10.1021/acsami.4c04491Digital Object Identifier
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Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of PolyacrylonitrileWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-07-03Full publication date if available
- Authors
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Rajni Chahal, Michael D. Toomey, Logan T. Kearney, Ada Sedova, Joshua T. Damron, Amit K. Naskar, Santanu RoyList of authors in order
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https://doi.org/10.1021/acsami.4c04491Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/2438910Direct OA link when available
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Polyacrylonitrile, Materials science, Molecular dynamics, Nanotechnology, Interatomic potential, Chemical physics, Polymer, Computational chemistry, Composite material, Physics, ChemistryTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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