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.
- Small angle scattering of diblock copolymers profiled by machine learning
- Small angle scattering of diblock copolymers profiled by machine learning
- Chi-Huan Tung (Author) - Oak Ridge National LaboratoryShou-Yi Chang (Author) - National Tsing Hua UniversityHsin-Lung Chen (Author) - Department of Materials Science and Engineering , National Tsing Hua UniversityYangyang Wang (Author) - National Tsing Hua UniversityKunlun Hong (Author) - National Tsing Hua UniversityJan Michael Carrillo (Author) - Center for Nanophase Materials SciencesBobby G. Sumpter (Author) - National Tsing Hua UniversityYuya Shinohara (Author) - Oak Ridge National LaboratoryChangwoo Do (Author) - National Tsing Hua UniversityWei-Ren Chen (Author) - Center for Nanophase Materials Sciences
- © 2022 Author(s).
- Journal article
- 04/2022
- Journal of Chemical Physics, Vol.156(13), 131101
- English