About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
|
Presentation Title |
High-dimensional Neural Network Potential for Liquid Electrolyte Simulations: Applications to Li-ion Battery Materials |
Author(s) |
Garvit Agarwal, Steven Dajnowicz, James M. Stevenson, Leif D. Jacobson, Farhad Ramezanghorbani, Karl Leswing, Mathew D. Halls, Robert Abel |
On-Site Speaker (Planned) |
James M. Stevenson |
Abstract Scope |
To move towards accurate and reliable modeling of Li-ion battery (LIB) chemistries, we developed a machine-learned potential for liquid electrolyte simulations. The potential was constructed using the charge recursive neural network architecture, which includes both long-range interactions and global charge redistribution. The potential uses non-periodic (cluster) DFT training data, allowing the use of more accurate functionals, like the range-separated hybrid ωB97X-D3BJ, which would be prohibitively expensive for generating datasets with periodic DFT. Here, we focus on seven carbonate solvents and LiPF6 salt in the LIB technology. Despite only training to cluster data, the predicted bulk thermodynamic properties and transport properties are in excellent agreement with experiments. The potential reproduces the concentration and temperature dependence for viscosity and diffusivity of ions and solvent. Furthermore, we demonstrate the capability of the model to accurately predict the solvation structure of ions using a comparison of the radial distribution functions with experimental data. |