Cargando…
Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions
[Image: see text] We present a new open-source, machine learning (ML) enhanced computational method for experimentalists to quickly analyze high-throughput small-angle scattering results from multicomponent nanoparticle mixtures and solutions at varying compositions and concentrations to obtain reco...
Autores principales: | Heil, Christian M., Patil, Anvay, Dhinojwala, Ali, Jayaraman, Arthi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335921/ https://www.ncbi.nlm.nih.gov/pubmed/35912348 http://dx.doi.org/10.1021/acscentsci.2c00382 |
Ejemplares similares
-
Machine Learning Enhanced Computational Reverse Engineering
Analysis for Scattering Experiments (CREASE) to Determine Structures
in Amphiphilic Polymer Solutions
por: Wessels, Michiel G., et al.
Publicado: (2021) -
Computational Reverse-Engineering Analysis for Scattering
Experiments of Assembled Binary Mixture of Nanoparticles
por: Heil, Christian M., et al.
Publicado: (2021) -
Computational Reverse Engineering Analysis for Scattering
Experiments (CREASE) on Vesicles Assembled from Amphiphilic Macromolecular
Solutions
por: Ye, Ziyu, et al.
Publicado: (2021) -
Computational Reverse-Engineering Analysis for Scattering
Experiments for Form Factor and Structure Factor Determination (“P(q) and S(q) CREASE”)
por: Heil, Christian M., et al.
Publicado: (2023) -
Mechanism of structural colors in binary mixtures of nanoparticle-based supraballs
por: Heil, Christian M., et al.
Publicado: (2023)