| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Innovations in Energy Materials: Unveiling Future Possibilities of Computational Modelling and Atomically Controlled Experiments
|
| Presentation Title |
Machine Learned Multiphysics Modeling: Enhancing Uniform Distribution of Low-Energy Lithium-Ion Transport Channels in Solid Electrolyte Interphase of Electrodes |
| Author(s) |
Arjun S. Kulathuvayal, Yanqing Su |
| On-Site Speaker (Planned) |
Arjun S. Kulathuvayal |
| Abstract Scope |
Lithium ions accumulation at certain spots on the cathode creates fine Lithium dendrites that may cause thermal runaway in a battery on aging. Dendrites originate from the Solid Electrolyte Interphase (SEI), a passive layer formed from redox reactions on the electrode. The SEI, with its multicomponent structure and numerous grain boundaries, serves as active Li-ion channels with varying activation energies for diffusion. The localized accumulation of low-energy Li-ion channels, accelerating dendrite growth, is not fully understood.
We developed an ML framework to interpret complex associations of components within the SEI. Our model extracts descriptors from the Li-ion diffusion environment, considering component combinations, atomic arrangements, temperature-dependent quasi-particle distributions, surface current density, and potential. Using density functional theory and experimental parameters, the framework predicts dendrite formation probability and suggests optimal SEI configurations to enhance LIB stability and safety, mitigating dendrite risks and aiding in the design of artificial SEIs. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Energy Conversion and Storage, Computational Materials Science & Engineering, |