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

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

doi:10.1016/j.gce.2024.11.003

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

Journal Article · 16 November 2024 · Green Chemical Engineering

DOI: https://doi.org/10.1016/j.gce.2024.11.003 · OSTI ID:2477702

^[1]; Gugulothu, Nikhitha ^[1];

^[2]; Rajitha Rajeshwar, Tatikonda ^[3];

^[1];

^[3]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Texas A & M Univ., Corpus Christi, TX (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)

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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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-00OR22725; SC0022214

OSTI ID:: 2477702

Journal Information:: Green Chemical Engineering, Journal Name: Green Chemical Engineering Journal Issue: 2 Vol. 6; ISSN 2666-9528

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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

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