Revolutionizing inverse design of ionic liquids through the multi-property prediction of over 300,000 novel variants using ensemble deep learning-Reference-Cited by-同舟云学术

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Revolutionizing inverse design of ionic liquids through the multi-property prediction of over 300,000 novel variants using ensemble deep learning

Published:2024-06 Issue: Volume:159 Page:100798
ISSN:0927-796X
Container-title:Materials Science and Engineering: R: Reports
language:en
Short-container-title:Materials Science and Engineering: R: Reports

Author:

Lemaoui Tarek,Eid Tarek,Darwish Ahmad S.,Arafat Hassan A.,Banat Fawzi,AlNashef Inas

Funder

Center for Membranes and Advanced Water Technology, Khalifa University

Khalifa University

Publisher

Elsevier BV

Reference81 articles.

1. Green Chemistry: Principles and Practice

2. Metrics to ‘green’ chemistry—which are the best?

3. Ionic liquids: a pathway to environmental acceptability

4. Applications of ionic liquids in the chemical industry

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1. High-throughput screening of 2,500 ionic liquids for sustainable furfural recovery: Bridging quantum simulations, machine learning, and experimental validation;Chemical Engineering Journal;2024-09

2. Rapid and highly efficient removal of aqueous perfluorooctanoic acid using deep eutectic solvents for sustainable water remediation: An integrated experimental-modeling approach;Chemical Engineering Journal;2024-09

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