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Accelerating Langevin Field-Theoretic Simulation of Polymers with Deep Learning

Cite this: Macromolecules 2022, 55, 15, 6505–6515
Publication Date (Web):July 21, 2022
https://doi.org/10.1021/acs.macromol.2c00705
Copyright © 2022 American Chemical Society

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    Abstract

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    Langevin field-theoretic simulation (L-FTS) is a promising tool in polymer field theory that can account for the compositional fluctuation effect, which is neglected in the self-consistent field theory (SCFT). However, L-FTS is a computationally expensive tool, and it may take more than a week to accurately calculate ensemble averages of thermodynamic quantities. In our previous study, we introduced a deep neural network (DNN) that estimates the saddle point of the pressure field to reduce the subsequent Anderson mixing (AM) iterations. Herein, we propose a novel DNN that can be successively applied to determine the saddle point without using conventional field-update algorithms. Major deep learning (DL) models for semantic segmentation in computer vision are adopted to construct the optimal DNN architecture. Our model utilizing atrous convolutions in parallel is accurate and computationally efficient, and it is robust to the simulation parameter changes and can consequently be reused after single training. We demonstrate that our DNN can achieve speedup of factor 6 or more compared to the AM method without affecting accuracy. Open-source code for our deep Langevin FTS (DL-FTS) enables an easy and rapid Python scripting of SCFT and L-FTS incorporated with CPU or GPU parallelization and DL.

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.macromol.2c00705.

    • Open-source code for L-FTS (ZIP)

    • Open-source code for DL-FTS (ZIP)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    This article is cited by 6 publications.

    1. Wonjun Kang, Daeseong Yong, Jaeup U. Kim. Characteristics of the Complex Saddle Point of Polymer Field Theory. Macromolecules 2024, 57 (8) , 3850-3861. https://doi.org/10.1021/acs.macromol.3c02622
    2. Haoke Qiu, Jingying Wang, Xuepeng Qiu, Xuemin Dai, Zhao-Yan Sun. Heat-Resistant Polymer Discovery by Utilizing Interpretable Graph Neural Network with Small Data. Macromolecules 2024, 57 (8) , 3515-3528. https://doi.org/10.1021/acs.macromol.4c00508
    3. MagruderBenjamin R.Ph.D. StudentDorfmanKevin D.Distinguished McKnight University ProfessorGayashani Ginige, Recent Ph.D. graduate, University of Alberta. Theory of Block Polymer Self-Assembly. 2024https://doi.org/10.1021/acsinfocus.7e8001
    4. Sanghoon Kim, Wonjun Kang, Chunghwan Jung, Mooseong Kim, Keon-Woo Kim, Myeongcheol Go, Nara Jeon, Junsuk Rho, Jaeup U. Kim, Jin Kon Kim. WiFi-like Nanostructures from Confinement of Block Copolymer Microdomains in Asymmetric Hemisphere Nanocavity. Macromolecules 2023, 56 (5) , 1837-1844. https://doi.org/10.1021/acs.macromol.2c02454
    5. Bart Vorselaars. Efficient Langevin and Monte Carlo sampling algorithms: The case of field-theoretic simulations. The Journal of Chemical Physics 2023, 158 (11) , 114117. https://doi.org/10.1063/5.0131183
    6. Thomas M. Beardsley, Mark W. Matsen. Well-tempered metadynamics applied to field-theoretic simulations of diblock copolymer melts. The Journal of Chemical Physics 2022, 157 (11) , 114902. https://doi.org/10.1063/5.0112703