Literature DB >> 32635177

SUSSOL-Using Artificial Intelligence for Greener Solvent Selection and Substitution.

Hannes Sels1, Herwig De Smet1, Jeroen Geuens1.   

Abstract

Solvents come in many shapes and types. Looking for solvents for a specific application can be hard, and looking for green alternatives for currently used nonbenign solvents can be even harder. We describe a new methodology for solvent selection and substitution, by applying Artificial Intelligence (AI) software to cluster a database of solvents based on their physical properties. The solvents are processed by a neural network, the Self-organizing Map of Kohonen, which results in a 2D map of clusters. The resulting clusters are validated both chemically and statistically and are presented in user-friendly visualizations by the SUSSOL (Sustainable Solvents Selection and Substitution Software) software. The software helps the user in exploring the solvent space and in generating and evaluating a list of possible alternatives for a specific solvent. The alternatives are ranked based on their safety, health, and environment scores. Cases are discussed to demonstrate the possibilities of our approach and to show that it can help in the search for more sustainable and greener solvents. The SUSSOL software makes intuitive sense and in most case studies, the software confirms the findings in literature, thus providing a sound platform for selecting the most sustainable solvent candidate.

Entities:  

Keywords:  SH&E; artificial intelligence; self-organizing map; software; solvent selection; solvent substitution; sustainable solvents

Year:  2020        PMID: 32635177     DOI: 10.3390/molecules25133037

Source DB:  PubMed          Journal:  Molecules        ISSN: 1420-3049            Impact factor:   4.411


  3 in total

1.  Editorial for the "Green Chemistry" Section in the Journal Molecules: Focus on Solvents.

Authors:  James Sherwood
Journal:  Molecules       Date:  2020-11-05       Impact factor: 4.411

Review 2.  Application of Biobased Solvents in Asymmetric Catalysis.

Authors:  Margherita Miele; Veronica Pillari; Vittorio Pace; Andrés R Alcántara; Gonzalo de Gonzalo
Journal:  Molecules       Date:  2022-10-08       Impact factor: 4.927

3.  MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning.

Authors:  Hyuntae Lim; YounJoon Jung
Journal:  J Cheminform       Date:  2021-07-31       Impact factor: 5.514

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.