Next-generation materials for energy storage

Our mission

Paris climate agreement has set a target of achieving net-negative global emissions by the year 2050.
Our group works on developing new materials for low-cost and scalable energy conversion, storage, and CO2 capture to meet this target.

Research
Computational design

Density functional theory and machine learning.

Materials synthesis

High-throughput robotic synthesis of solution-processed nanomaterials.

Devices

Li-ion batteries, CO2 capture, solar cells, catalysis.

Group news
Our first publication on machine learning for materials science

Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis In this work, we applied Bayesian optimization methods to facilitate… Read More

Kamal and Alex’s Preview on Trap States in Quantum Dots is online!

Electronic traps are the primary factor stifling the performance of quantum-dot (QD) solar cells to nearly half their theoretical potential.… Read More

Our new toy – Opentrons pipetting robot

https://www.youtube.com/watch?v=gqC--U929p4 Read More

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