Thrust 1: Understanding Solid-Electrolyte Interphase with Advanced Transmission Electron Microscopy (TEM)

Nature Nanotechnology, 17, 768–776 (2022), Nature Comm 12, 2350 (2021), Nature Comm, 10, 1650 (2019) 

Thrust 2: Developing Beyond-Li Battery Chemistries

Nano Letters, 22, 7535–7544 (2022), Nano Letters 24, 5429-5435 (2024)

Thrust 3: Design & Fabrication of Polymer-Based Solid Electrolytes

Nature Nanotechnology, 17, 768–776 (2022)

Thrust 4: Data-Efficient Learning for Optimizing Battery Component Processing and Manufacturing

1. Limited Data Challenge: Battery component manufacturing often involves complex processes with limited available data, making data-efficient AI approaches crucial.

2. Promising Techniques: Transfer learning, Bayesian optimization, and other data-efficient techniques are being explored for optimizing battery manufacturing processes.

3. Potential Benefits: Data-efficient Learning can lead to improved product quality, reduced waste, increased efficiency, and lower costs in battery component manufacturing.

e.g. arXiv:1506.01349