SUSSOL—Using Artificial Intelligence for Greener Solvent Selection and Substitution

Abstract

<jats:p>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.</jats:p>

Solvation Free Energy Prediction from Pairwise Atomistic Interactions by Machine Learning

“…Recent progress in machine learning (ML) techniques [55] and their implementation in computational chemistry [75,7] are currently promoting broad applications of SPR in numerous chemical studies [16,27,14,18,56,64,4,1,46,47,52,58,62,24,6,51,57,73,77,2,3,8,9,26,48,54,60,67,72,71,74]. These studies show that ML guarantees faster calculations than computer simulations and more precise estimations than traditional SPR estimations; a considerable number of models showed accuracies comparable to ab initio solvation models in the aqueous system [75].…”

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

“…Although we may not expect to obtain detailed chemical or physical insights other than the target property because this is a regression analysis in its nature, SPR has demonstrated significant potential in terms of transferability and outstanding computational efficiency [ 11 , 74 , 79 ]. Recent progress in machine learning (ML) techniques [ 59 ] and their implementation in computational chemistry [ 8 , 79 ] are currently promoting broad applications of SPR in numerous chemical studies [ 1 – 4 , 7 , 9 , 10 , 15 , 18 , 20 , 26 , 28 , 29 , 50 – 52 , 55 , 56 , 58 , 60 – 62 , 64 , 66 , 68 , 71 , 75 – 78 , 82 ]. These studies show that ML guarantees faster calculations than computer simulations and more precise estimations than traditional SPR estimations; a considerable number of models showed accuracies comparable to ab initio solvation models in the aqueous system [ 79 ].…”

Collagen/PCL Nanofibers Electrospun in Green Solvent by DOE Assisted Process. An Insight into Collagen Contribution

“…The choice of acetic acid as solvent can promote the exploitation of inexpensive renewable feedstocks, because acetic acid can be obtained from cellulosic biomass such as agricultural residues [ 29 ]. Moreover, the increasing concern, especially in EU regulations, for the impact of chemicals on health and environment encouraged the pharmaceutical industries to substitute toxic or hazardous solvents with green ones [ 30 ]. Furthermore, several works demonstrated the fundamental contribution of the solvent to the collagen structure, showing in particular the strong denaturation effect of fluoroalcohols and TFE solvents on the triple helical structure during solvent casting processes, in contrast with the results reported for acetic acid [ 12 , 31 , 32 ].…”