The focus area of the group is computational studies of materials for energy storage devices and electrocatalysis. From the methodological viewpoint, the group efforts are divided between theoretical approaches involving application of density functional theory (DFT) methods and large-scale classical molecular dynamics (MD) simulations to understand and predict atomistic properties of electrolytes and electrode/electrolyte interfaces. In both the aforementioned thrusts, the methods of high-throughput computational screening using DFT theory based-methods and/or automated classical MD simulations are employed. Over the recent few years, a significant progress has been made in complimenting the existing high-throughput infrastructure with computational models and modules involving machine learning. The materials informatics elements allow for a significant facilitation of materials screening by expanding the parameter space and inclusion of systems and processes spanning different time- and length-scales.
Machine Learning-driven Electrolyte Discovery and Optimization
To facilitate discovery and optimization of novel constituent materials and modules for energy storage, we aim to develop machine learning-based models and concomitant databases. This enterprise encompasses a diverse array of projects involving applications of machine learning methods and big data. Among the priority venues are the following:
Big data analytics for establishing structure-property relationships in materials
for battery applications.
- Applications of machine learning methods for large-scale quantum and classical molecular dynamics simulations of molecular compounds.
- Deep learning methods for design of molecules with desired properties.
Hybrid Energy Storage Devices
The development of powerful mobile devices, electrically driven cars, and renewable intermittent energy production fuels the ever-increasing demand for higher power and energy density in electrochemical energy storage devices. One proposed way to achieve this goal is the creation of a device which uses materials from batteries and supercapacitors, called a hybrid energy storage device. This project focuses on the computational screening of ionic liquid electrolyte for multivalent hybrid ion capacitors with the goal of being able to discover promising candidates for use in such a device. In addition to running bulk calculations to elucidate the solvent structure and calculate properties such as electrochemical window, and diffusivity, we are considering electrolyte confined in different nanoporous carbon electrodes.
Design of Optimal Electrolytes and Electrode-Electrolyte Interfaces for Next Generation Lithium-Sulfur Batteries
Lithium-Sulfur (Li-S) batteries are one of the most promising candidates for next-generation
batteries due to their higher theoretical energy density and lower cost compared to
state-of-the-art Li-ion batteries. However, commercialization of Li-S batteries is
hindered by their poor cycling performance and capacity retention caused by the formation
and dissolution of lithium polysulfides (
LiPSs) during discharging/charging in the electrolyte, the lifeblood of the battery. More
specifically, continuous diffusion of
LiPSs towards Li metal anode and their nucleation and sluggish kinetics on the host carbon
materials at the cathode result in loss of the active material. Hence, tailoring the
atomistic interactions between LiPSs, salt anion, and solvent, in addition to the
functional properties of porous carbon materials is critical in controlling deleterious
side reactions, designing optimal electrolytes, and improving the performance and
longevity of Li-S batteries.
Predicting and understanding the physicochemical behavior of bulk electrolytes and electrode-electrolyte interfaces is achieved via a multi-scale-data-driven approach that combines density functional theory (DFT) and molecular dynamics (MD) simulations. The project is also done in close contact with experimentalists from PNNL, resulting in a better interpretation of experimental results and an enhanced predictive power of the next iteration of simulations. The result is a database consisting of a large number of materials with optimized parameters to be used in electrolytes that achieve all the performance metrics for a given battery application.