.png){kind=icon}
.png){kind=icon}
Return
Physics-Guided Machine Learning for Ionic-Liquid Volumetric Properties
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jcim.5c02962.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Accurate modeling of the volumetric behavior of ionic liquids (ILs) is crucial for guiding the design of electrolytes for energy storage and other chemical systems. While classical group contribution methods (GCMs) are grounded in thermodynamic theory, traditional machine learning (ML) models often lack physically consistent predictions and generalizability. To improve this, a hybrid modeling strategy is introduced that couples a reoptimized Classical-GCM with a physics-informed neural network (PINN-GCM), where thermodynamically optimized parameters from the Tait equation are directly incorporated into the hybrid loss function of the network. Building on the previous efforts of Jacquemin et al. (Ind. Eng. Chem. Res., 2017, 56, 6827-6840), the data set was extracted from the National Institute of Standards and Technology (NIST) database. The PINN-GCM framework was evaluated across 92 ILs, comprising 8,467 experimental data points spanning 217-473 K and 0.1-207 MPa. The aggregate performance yielded average RAAD values of 0.067 and 0.065% for the training and test sets, respectively, at the IL level. The ion-level models were trained on 6,049 points from 59 ILs (32 cations and 28 anions), with extrapolation evaluated on 2,958 points from 21 unseen IL combinations, demonstrating strong combinatorial generalization to new pairings of known ions, although structural generalization to entirely novel ion chemistries remains beyond the scope of the current model. The framework shows promise for integration into process simulation tools and extension to related IL properties (viscosity and conductivity), although its applicability is validated within the experimental temperature-pressure range and requires ions present in the established library. This strategy highlights the potential of merging physics-based modeling and ML to develop foundational models for multiproperty prediction, thereby promoting the improved design of safer electrolytes and other chemical systems in the future.
Keywords:
WIDE-RANGE
APPLICABILITY DOMAIN
DENSITY-ESTIMATION
TEMPERATURE
VISCOSITY
PREDICTION
PRESSURES
MODEL
FRAMEWORK
NETWORKS
Journal
IF:
5.3
Papers:
9.1K
Citations:
4.0W