Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution

Mood Mohan; Nikhitha Gugulothu; Sreelekha Guggilam; T. Rajitha Rajeshwar; Michelle K. Kidder; Jeremy C. Smith

doi:10.1016/j.gce.2024.11.003

Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO₂ and lignin dissolution

Mood Mohan, Nikhitha Gugulothu, Sreelekha Guggilam, T. Rajitha Rajeshwar, Michelle K. Kidder, Jeremy C. Smith

Research output: Contribution to journal › Article › peer-review

Abstract

The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π∗). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R² and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO₂) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO₂ into valuable chemicals.

Original language	English
Journal	Green Chemical Engineering
DOIs	https://doi.org/10.1016/j.gce.2024.11.003
State	Accepted/In press - 2024

Funding

This research was supported by the U.S. Department of Energy (DOE), Office of Science, through the Genomic Science Program, Office of Biological and Environmental Research (Contract No. FWP ERKP752), and the DOE Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences (CSGB) (Award No. DE-SC0022214; FWP 3ERKCG25). This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This material is based upon work supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program under award #ERKP752, and the DOE Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences (CSGB) (Award No. DE-SC0022214; FWP 3ERKCG25). This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funders	Funder number
Basic Energy Sciences
DOE Public Access Plan
U.S. Department of Energy
Office of Science
Biological and Environmental Research program
Biological and Environmental Research	FWP ERKP752
Chemical Sciences, Geosciences, and Biosciences Division	DE-AC05-00OR22725, FWP 3ERKCG25, DE-SC0022214

Keywords

Deep eutectic solvents
Ionic liquids
Kamlet-Ttaft parameter
Machine learning
Organic compounds

Access to Document

10.1016/j.gce.2024.11.003

Cite this

@article{4b4da7658faf4fbf9d68ac89090627c0,

title = "Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution",

abstract = "The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π∗). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R2 and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO2) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO2 into valuable chemicals.",

keywords = "Deep eutectic solvents, Ionic liquids, Kamlet-Ttaft parameter, Machine learning, Organic compounds",

author = "Mood Mohan and Nikhitha Gugulothu and Sreelekha Guggilam and {Rajitha Rajeshwar}, T. and Kidder, {Michelle K.} and Smith, {Jeremy C.}",

note = "Publisher Copyright: {\textcopyright} 2024 Institute of Process Engineering, Chinese Academy of Sciences.",

year = "2024",

doi = "10.1016/j.gce.2024.11.003",

language = "English",

journal = "Green Chemical Engineering",

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AU - Mohan, Mood

AU - Gugulothu, Nikhitha

AU - Guggilam, Sreelekha

AU - Rajitha Rajeshwar, T.

AU - Kidder, Michelle K.

AU - Smith, Jeremy C.

PY - 2024

Y1 - 2024

N2 - The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π∗). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R2 and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO2) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO2 into valuable chemicals.

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