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
