Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

Chahal, Rajni; Toomey, Michael D.; Kearney, Logan T.; Sedova, Ada; Damron, Joshua T.; Naskar, Amit K.; Roy, Santanu

doi:10.1021/acsami.4c04491

Title: Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

Journal Article · Wed Jul 03 00:00:00 UTC 2024 · ACS Applied Materials and Interfaces

DOI: https://doi.org/10.1021/acsami.4c04491 · OSTI ID:2438910

^[1]; Toomey, Michael D. ^[1]; Kearney, Logan T. ^[1]; Sedova, Ada ^[1];

^[1];

^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

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. In conclusion, 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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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Hydrogen Fuel Cell Technologies Office (HFTO); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-05CH11231; AC05-00OR22725

OSTI ID:: 2438910

Journal Information:: ACS Applied Materials and Interfaces, Journal Name: ACS Applied Materials and Interfaces Journal Issue: 28 Vol. 16; ISSN 1944-8244

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
ReaxFF
machine-learning interatomic potential
mechanical properties
molecular dynamics
polyacrylonitrile
polymers

Title: Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

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