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
  • ML-based forcefields for strained and distorted crystalline materials
  • defects and defect tolerance
  • continuous latent spaces for organic molecules


  • Nitrogen electroreduction
  • Acidic oxygen evolution reaction


  • 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