Data for Gaming self-consistent field theory: Generative block polymer phase discovery

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Collection period

2023-01-01
2023-05-01

Date completed

2023-10-01

Date updated

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Journal Title

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Title

Data for Gaming self-consistent field theory: Generative block polymer phase discovery

Published Date

2023-10-18

Author Contact

Dorfman, Kevin D
dorfman@umn.edu

Type

Dataset
Simulation Data

Abstract

This dataset contains the input and output files for self-consistent field theory (SCFT) simulations and the training of generative adversarial networks (GANs) in the associated paper.

Description

Generative adversarial networks (GANs), trained on self-consistent field theory (SCFT) density fields of known block polymer phases, was used to propose new initial guesses for subsequent SCFT calculations. This dataset contains the training data and outputs for GANs, as well as the input and output for SCFT calculations. Codes used can be found on Github, please see the README for further details on how to access.

Referenced by

Chen, P. and Dorfman K. (2023). Gaming self-consistent field theory: Generative block polymer phase discovery. Proceedings of the National Academy of Sciences. 120 (45) e2308698120
https://doi.org/10.1073/pnas.2308698120

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Funding information

National Science Foundation, Grant/Award Numbers: DMR-2011401

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Previously Published Citation

Suggested citation

Chen, Pengyu; Dorfman, Kevin D. (2023). Data for Gaming self-consistent field theory: Generative block polymer phase discovery. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/qv23-pp07.
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File View/OpenDescriptionSize
readme.txtDescription of data1.77 MB
Data_Generative_SCFT.zipSCFT data; GAN training input and output15.97 GB

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