Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning-Reference-Cited by-同舟云学术

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Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

Published:2023-10-10 Issue:20 Volume:19 Page:7371-7386
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Moriarty Alexander¹^ORCID,Kobayashi Takeshi¹^ORCID,Salvalaglio Matteo¹^ORCID,Angeli Panagiota¹,Striolo Alberto¹²^ORCID,McRobbie Ian³

Affiliation:

1. Department of Chemical Engineering, University College London, London WC1E 7JE, U.K.

2. School of Sustainable Chemical, Biological and Materials Engineering, University of Oklahoma, Norman, Oklahoma 73019-0390, United States

3. Innospec Ltd., Ellesmere Port CH65 4EY, U.K.

Funder

Engineering and Physical Sciences Research Council

ABK Studentship

Asahi Glass Chair of Chemical Engineering

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.3c00868

Reference73 articles.

1. Surfactants and Interfacial Phenomena

2. Structure and aggregation in dilate solution of surface active agents

3. Molecular‐thermodynamic approach to predict micellization, phase behavior and phase separation of micellar solutions. I. Application to nonionic surfactants

4. Gibbs thermodynamics and surface properties at the nanoscale

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2. Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks;Journal of Chemical Theory and Computation;2024-06-26

3. Prediction of critical micelle concentration for per- and polyfluoroalkyl substances;SAR and QSAR in Environmental Research;2024-04-02

4. Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis;Industrial & Engineering Chemistry Research;2024-04-01

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