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  3. Physics-guided Machine Learning For Ionic-liquid Volumetric Properties.
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Physics-Guided Machine Learning for Ionic-Liquid Volumetric Properties.

Kingsley Omeoga1, Tausif Altamash1, Mouad Dahbi1

  • 1College of Chemical Sciences and Engineering (CCSE), Department of Materials Science, Energy and Nano-engineering (MSN), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco.

Journal of Chemical Information and Modeling
|March 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new hybrid model combines classical methods with physics-informed neural networks for accurate ionic liquid (IL) volumetric behavior prediction. This approach enhances electrolyte design for energy storage and chemical systems.

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Area of Science:

  • Physical Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Accurate modeling of ionic liquid (IL) volumetric behavior is essential for designing electrolytes used in energy storage and chemical applications.
  • Classical group contribution methods (GCMs) offer thermodynamic grounding but often lack predictive accuracy and generalizability.
  • Traditional machine learning (ML) models can struggle with physical consistency and broad applicability.

Purpose of the Study:

  • To develop a hybrid modeling strategy that integrates classical group contribution methods with physics-informed neural networks (PINN-GCM) for improved IL volumetric property prediction.
  • To enhance the physical consistency and generalizability of IL modeling by incorporating thermodynamic principles into an ML framework.
  • To leverage the NIST database for training and validating the novel PINN-GCM model across a wide range of ILs and conditions.

Main Methods:

  • A hybrid model (PINN-GCM) was developed by coupling a reoptimized Classical-GCM with a PINN.
  • Thermodynamically optimized parameters from the Tait equation were integrated into the PINN's hybrid loss function.
  • The model was trained and tested on experimental data for 92 ILs, covering temperatures from 217-473 K and pressures from 0.1-207 MPa.

Main Results:

  • The PINN-GCM framework achieved high accuracy, with aggregate RAAD values of 0.067% (training) and 0.065% (test) at the IL level.
  • Ion-level models demonstrated strong combinatorial generalization to new IL pairings, with extrapolation on 2,958 data points from 21 unseen combinations.
  • The model's applicability is validated within the experimental temperature-pressure range and requires ions present in the established library.

Conclusions:

  • The hybrid PINN-GCM strategy effectively models the volumetric behavior of ionic liquids, merging physics-based principles with machine learning.
  • This approach shows significant promise for improving electrolyte design in energy storage and other chemical systems.
  • Future work may involve extending the framework to predict other IL properties like viscosity and conductivity and integrating it into process simulation tools.