Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis-Reference-Cited by-同舟云学术

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Published:2024-04-01 Issue:14 Volume:63 Page:6313-6324
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Nnadili Miriam¹^ORCID,Okafor Andrew N.¹,Olayiwola Teslim¹,Akinpelu David²,Kumar Revati³^ORCID,Romagnoli Jose A.¹^ORCID

Affiliation:

1. Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, Louisiana 70803, United States

2. Department of Mechanical & Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana 70803, United States

3. Department of Chemistry, Louisiana State University, Baton Rouge, Louisiana 70803, United States

Funder

U.S. Department of Energy, Office of Science, under the Office of Basic Energy Sciences Separation Science program

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.4c00401

Reference63 articles.

1. Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

2. Deep learning

3. Deep Generative Models for Molecular Science

4. Deep generative models for 3D molecular structure

5. The Synthesizability of Molecules Proposed by Generative Models

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