(342e) Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures | AIChE

(342e) Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures


Ravichandran, A., NASA Ames - KBR, Inc
Lawson, J. W., NASA Ames Research Center
Hennig, R. G., Cornell University
Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. Molten salt electrolyte batteries require conditions far above room temperature to operate, but have desirable chemical stability, motivating their potential use in aerospace energy storage applications. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials.

The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored how machine learning methods could learn the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approaches use existing databases for sigma profiles of organic solvents. Our models attempt to learn using the data of different fidelity to learn activity coefficients of mixtures. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using support vector machines. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.