Theory-guided discovery and synthesis of nanomaterials for energy
Our group works on developing new materials for Li-ion batteries, hydrogen storage, CO2 capture, and photovoltaics, to help mitigate climate change and meet the Paris Agreement goals. We utilize atomistic simulations, machine learning, automated high-throughput materials synthesis and characterization to demonstrate proof-of-principle devices that will speed-up the transition to renewables.
Current research topics
Machine learning for materials discovery
- improvements to crystal graph convolution neural networks (new pooling methods and orbital descriptors) for accurate bandgap predictions, density of states, and wavefunctions
- ML-based forcefields for strained and distorted crystalline materials
- defects and defect tolerance
- continuous latent spaces for organic molecules
Catalysis
- Nitrogen electroreduction
- CO2 capture and reduction
- Acidic oxygen evolution reaction
Batteries
- Li-ion battery high-capacity anodes
- Phase stability of Li-ion cathodes
- Solid-state electrolytes and electrode additives with high ionic conductivity
- Li-S batteries
- Electrolytes for aqueous batteries and supercapacitors
New semiconductor materials
- Inorganic perovskites with improved phase stability
- New non-toxic solution-processed semiconductors
- Materials for blue and UV LEDs and lasers