Small angle scattering of diblock copolymers profiled by machine learning.
J Chem Phys
; 156(13): 131101, 2022 Apr 07.
Article
en En
| MEDLINE
| ID: mdl-35395880
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.
Texto completo:
1
Banco de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
J Chem Phys
Año:
2022
Tipo del documento:
Article
País de afiliación:
Taiwán