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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title Modeling the Morphological Dependent Performance of an All Solid-state Battery
Author(s) Kalyan Sundar Krishna Chivukula, Fiyanshu Kaka
On-Site Speaker (Planned) Fiyanshu Kaka
Abstract Scope Solid-state batteries (SSBs) are promising alternatives to conventional metal-ion batteries due to their potential to address safety concerns related to liquid electrolytes. This study focuses on improving SSBs' electrochemical performance by optimizing the multi-component solid electrolyte blends. Computational models were utilized to systematically obtain the optimized blend ratio by exploring binary and ternary blends. This systematic evaluation enhances Li-ion mobility and overall electrochemical performance, leading to improved battery characteristics. A novel diffuse-interface approach has been employed to analyze the impact of phase-separating blends on SSBs upon considering factors like electrode-electrolyte interfacial area and energy level differences among phases. The optimized blend ratio resulted in a significant improvement of 38.15% and 8.11% in the overall potential difference across the SSB for binary and ternary solid electrolyte blends, respectively. This research advances SSB technology by optimizing the solid electrolyte morphology, and facilitating the development of safer and more efficient energy storage systems.
Proceedings Inclusion? Planned:
Keywords Modeling and Simulation, Other, Other

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High-Throughput Artificial Neural Network - Kinetic Monte Carlo (ANN-KMC) Framework for Diffusion Studies in FeNiCrCoCu High-entropy Alloys of Versatile Compositions
Homogeneous Solute Segregation Suppressing Strain Localization in Nanocrystalline Ni-Nb Alloys
Impacts of Oxygen Doping on Sodium-ion Diffusion in Solid-state Batteries with Glassy Electrolyte: A Molecular Dynamics Perspective
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Large-scale Ab-Initio Computation of Core Energetics of Pyramidal Dislocations in Mg and Mg-Y Alloy Using DFT-FE: Implications Towards Ductility Enhancement
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Unraveling the Mechanisms of Stability in CoMoFeNiCu High Entropy Alloys via Physically Interpretable Graph Neural Networks

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