5 papers
Ionic Interdiffusion at Cathode-Solid-Electrolyte Interface: A Machine Learning-Assisted Multiscale Investigation and Mitigation Strategies
Musawenkosi K. Ncube, Pallab Barai, Selva Chandrasekaran Selvaraj +3
Future lithium-based batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. The majority of solid electrolytes are thermody…
Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations
Hanzeng Guo, Volodymyr Koverga, Selva Chandrasekaran Selvaraj +1
Lithium zirconium chlorides (LZCs) present a promising class of cost-effective solid electrolyte for next-generation all-solid-state batteries. The unique crystal structure of LZCs…
Interactive Multiscale Modeling to Bridge Atomic Properties and Electrochemical Performance in Li-CO Battery Design
Mohammed Lemaalem, Selva Chandrasekaran Selvaraj, Ilias Papailias +5
Li-CO batteries are promising energy storage systems due to their high theoretical energy density and CO fixation capability, relying on reversible LiCO/C formation…
Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study
Selva Chandrasekaran Selvaraj, Daiwei Wang, Donghai Wang +1
Mixed ionic-electronic conductors (MIECs) exhibit both high ionic and electronic conductivity to improve the battery performance. In this work, we investigate the mechanism and sta…
Graph Neural Networks Based Deep Learning for Predicting Structural and Electronic Properties
Selva Chandrasekaran Selvaraj
This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials…