Small angle scattering of diblock copolymers profiled by machine learning

Chi Huan Tung; Shou Yi Chang; Hsin Lung Chen; Yangyang Wang; Kunlun Hong; Jan Michael Carrillo; Bobby G. Sumpter; Yuya Shinohara; Changwoo Do; Wei Ren Chen

doi:10.1063/5.0086311

Small angle scattering of diblock copolymers profiled by machine learning

Chi Huan Tung, Shou Yi Chang, Hsin Lung Chen, Yangyang Wang, Kunlun Hong, Jan Michael Carrillo, Bobby G. Sumpter, Yuya Shinohara, Changwoo Do, Wei Ren Chen

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We outline a machine learning strategy for quantitively determining the conformation of AB-type diblock copolymers with excluded volume effects using small angle scattering. Complemented by computer simulations, a correlation matrix connecting conformations of different copolymers according to their scattering features is established on the mathematical framework of a Gaussian process, a multivariate extension of the familiar univariate Gaussian distribution. We show that the relevant conformational characteristics of copolymers can be probabilistically inferred from their coherent scattering cross sections without any restriction imposed by model assumptions. This work not only facilitates the quantitative structural analysis of copolymer solutions but also provides the reliable benchmarking for the related theoretical development of scattering functions.

Original language	English
Article number	131101
Journal	Journal of Chemical Physics
Volume	156
Issue number	13
DOIs	https://doi.org/10.1063/5.0086311
State	Published - Apr 7 2022

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/downloads/doe-public-access-plan). A portion of this research used resources at the Spallation Neutron Source and Center for Nanophase Materials Sciences, two DOE Office of Science User Facilities operated by the Oak Ridge National Laboratory. C.-H.T. and S.-Y.C. acknowledge support from the Ministry of Science and Technology of Taiwan under Grant No. MOST 108-2221-E007-054-MY3. Y.W. was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Early Career Research Program Award KC0402010, under Contract No. DE-AC05-00OR22725. Y.S. was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials and Science and Engineering Division. B.G.S. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Data, Artificial Intelligence and Machine Learning at the DOE Scientific User Facilities Program, under Award No. 34532. The authors acknowledge the National Center for High-Performance Computing of Taiwan for providing computational and storage resources.

Funders	Funder number
Center for Nanophase Materials Sciences
Office of Basic Energy Sciences Data, Artificial Intelligence and Machine Learning
U.S. Department of Energy	34532
Office of Science
Basic Energy Sciences	DE-AC05-00OR22725, KC0402010
Oak Ridge National Laboratory
Division of Materials Sciences and Engineering
Ministry of Science and Technology, Taiwan	108-2221-E007-054-MY3

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10.1063/5.0086311

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abstract = "We outline a machine learning strategy for quantitively determining the conformation of AB-type diblock copolymers with excluded volume effects using small angle scattering. Complemented by computer simulations, a correlation matrix connecting conformations of different copolymers according to their scattering features is established on the mathematical framework of a Gaussian process, a multivariate extension of the familiar univariate Gaussian distribution. We show that the relevant conformational characteristics of copolymers can be probabilistically inferred from their coherent scattering cross sections without any restriction imposed by model assumptions. This work not only facilitates the quantitative structural analysis of copolymer solutions but also provides the reliable benchmarking for the related theoretical development of scattering functions.",

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AU - Carrillo, Jan Michael

AU - Sumpter, Bobby G.

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AU - Do, Changwoo

AU - Chen, Wei Ren

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Small angle scattering of diblock copolymers profiled by machine learning

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