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.
Density functional theory and machine learning.
High-throughput robotic synthesis of solution-processed nanomaterials.
Li-ion batteries, CO2 capture, solar cells, catalysis.
Kamal’s Mn-MOFs for water splitting in acidic media is online!
Congratulations, Kamal! ACS Materials Letters, 2020, just accepted manuscript. The oxygen evolution capabilities of a manganese metal-organic framework (MOF) are… Read More
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