|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Data Driven Approach to Design/Discover Intercalating Ions and Layered Materials for Metal-ion Batteries
||Shayani Parida, Avanish Mishra, Arthur Dobley, C Barry Carter, Avinash Dongare
|On-Site Speaker (Planned)
This study utilizes data-driven machine learning methods to find alternative 2D materials and intercalating ions beyond Li for metal-ion batteries with high-power efficiencies. A dataset is constructed by performing first-principles density functional theory (DFT) simulations to estimate theoretical capacities and voltages by calculating the binding energies of metal ions on 2D materials. A tree-based regression model is developed to predict the binding energies with unprecedented accuracy. The model suggests that atomic electronegativities and number of valence electrons bear a strong correlation with binding energy. The generated dataset also provides insight into structural accommodations that can be expected upon ion intercalation on various 2D materials. Additionally, a binding energy and structural deformation-based classification model is developed to screen anode materials for the next-generation batteries. The model selects intercalating ion and 2D material pairs that are suitable for batteries, based on calculated voltage and volumetric changes in the 2D material upon intercalation.
||Energy Conversion and Storage, Machine Learning, Modeling and Simulation