Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Mood Mohan, Micholas Dean Smith, Omar N. Demerdash, Blake A. Simmons, Seema Singh, Michelle K. Kidder, Jeremy C. Smith

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. In the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R2 value of 0.96-0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

Original languageEnglish
Pages (from-to)7809-7821
Number of pages13
JournalACS Sustainable Chemistry and Engineering
Volume11
Issue number20
DOIs
StatePublished - May 22 2023

Funding

This work was supported and provided 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). This work was also part of the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the U. S. Department of Energy. Michelle K. Kidder acknowledged the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences (CSGB) Grant Number 3ERKCG25 for partially supporting this research. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Keywords

  • COSMO-RS
  • gradient boosting tree
  • ionic liquids
  • machine learning
  • sigma profiles
  • speed of sound
  • surface tension

Fingerprint

Dive into the research topics of 'Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids'. Together they form a unique fingerprint.

Cite this