Hybrid Approach for Predicting Melting Points in Nonionic Eutectic Solvents Using Thermodynamics and Machine Learning

In this work, a hybrid approach combining solution thermodynamics and machine learning (ML) methods is presented as a means of estimating solid–liquid equilibria (SLE) in nonionic eutectic solvents. The models were developed based on a data set comprising 141 binary mixtures and 1668 experimental me...

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Bibliographic Details
Main Author:	Dmitriy M. Makarov (1600432) (author)
Other Authors:	Vasiliy Golubev (21008304) (author), Arkadiy M. Kolker (1600426) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Medicine Microbiology Ecology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Physical Sciences not elsewhere classified semiempirical associated solution predicting melting points nonionic eutectic solvents calculate melting points ml first predicts direct ml predictions one fitting parameter asl model ’ two fitting parameters work compares models fitting parameters parameter asl ω ′,< ω )), two versions machine learning k </ interchange energy hybrid approach heteroassociation constant developed based 7 k
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Hybrid Approach for Predicting Melting Points in Nonionic Eutectic Solvents Using Thermodynamics and Machine Learning

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