ArticleACS polymers Au2021
Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions.
Article in ACS polymers Au, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films.Macromolecules · 2026Article
- Article
- Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.Chemical science · 2023Review
- Synergistic Role of Temperature and Salinity in Aggregation of Nonionic Surfactant-Coated Silica Nanoparticles.Langmuir : the ACS journal of surfaces and colloids · 2023Article
- Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination ("JACS Au · 2023Article
- Thermodynamic origins of two-component multiphase condensates of proteins.Chemical science · 2023Article
- Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions.ACS central science · 2022Article
- Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles.ACS materials Au · 2021Article
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Authors and funding
2 authors.
Funding
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Abstract
In this article, we present a machine learning enhancement for our recently developed "Computational Reverse Engineering Analysis for Scattering Experiments" (CREASE) method to accelerate analysis of results from small angle scattering (SAS) experiments on polymer materials. We demonstrate this novel artificial neural network (NN) enhanced CREASE approach for analyzing scattering results from amphiphilic polymer solutions that can be easily extended and applied for scattering experiments on other polymer and soft matter systems. We had originally developed CREASE to analyze SAS results [i.e., intensity profiles,
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Registered trials
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