| 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 |
Quantum-Assisted Machine Learning Analysis of Silicon-Based Anodes for Lithium Batteries: Thermodynamics, Structural Insights, and Lithium Diffusion. Identifying Challenges and Exploring Novel Candidates |
| Author(s) |
Marco Fronzi, Catherine Stampfl , Amanda Ellis, Eirini Goudeli |
| On-Site Speaker (Planned) |
Marco Fronzi |
| Abstract Scope |
In this study, quantum mechanical and machine learning models analyze the properties of silicon composite phases in lithium-ion battery anodes during lithiation/delithiation, focusing on volume expansion challenges. Insights into Gibbs free energy, chemical potentials, and the stability of Li0 and Li+ species are provided. The study examines 211 thermodynamically stable crystal structures formed with inexpensive, recyclable elements, assessing their potential as anode materials. Advanced machine learning explores relationships between atomic orbital overlap, energy density, and ion mobility, crucial for rapid charging. The combined effects of elemental composition and crystallographic space groups are also analyzed. Several crystal structures exhibit exceptional stability and efficient lithium ion mobility, showing promise for high-capacity, durable battery anodes. These findings highlight the importance of a multidimensional approach in battery material development, offering guidance for designing high-performance lithium-ion batteries for a sustainable economy. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Energy Conversion and Storage |