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Meeting	MS&T22: Materials Science & Technology
Symposium	AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title	Data-driven Search for Promising Intercalating Ions and Layered Materials for Metal-ion Batteries
Author(s)	Shayani Parida, C. Barry Carter, Avanish Mishra, Arthur Dobley, Avinash M. Dongare
On-Site Speaker (Planned)	Shayani Parida
Abstract Scope	An innovative combination of atomic-scale modeling and machine learning methods is used to find layered materials beyond graphite for anodes and intercalating ions beyond Li in metal-ion batteries with higher power efficiencies. A dataset is created using density functional theory (DFT) calculations to estimate the theoretical capacities and voltages for various metal ions intercalated in layered materials. A gradient boosting decision trees (GBDT) classifier is developed to screen for 2D material and ion combinations based on voltages and structural deformations/transformations. Further, a regression model is developed to predict the binding energies of the feasible layered anodes and intercalating species. The study reveals the importance of elemental features to predict the binding properties of intercalating species for a given layered material. The framework of the approach, the ML algorithm, and the discovery of layered materials as anodes for the next generation metal ion batteries will be discussed.

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