Machine learning and atomistic materials simulations


We are a group at the interface between machine learning, materials informatics and quantum chemistry.

Designing for function.

Harder, better, faster, stronger. We use computational tools to tackle design molecular materials in a number of areas, such as organic light-emitting diodes, energy storage and catalysis. 

Basic questions.

The combination of large experimental datasets and accurate theoretical simulations with statistical inference allow computer to answer fundamental scientific questions like never before.

We are recruiting! 

We are actively looking for new group member at all levels.

  • Undergraduate students interested in joining the group for their PhD are encouraged to apply for admission to MIT graduate program.
  • Graduate students admitted to DMSE and also to other departments at MIT who are interested in working with us should contact Rafa. 
  • We are also on the lookout for outstading postdoctoral researchers with a background in any combination of the following: machine learning, high-throughput simulation, materials informatics, electronic structure. Python chops and experience using databases are a big plus.
    • Specifically, we are seeking to fill one or two postdoctoral positions in design of oxide catalysts for the oxygen evolution and oxygen reduction reactions using density functional theory, high throughput simulations and machine learning, as part of larger collaboration to achieve accelerated materials design in the lab. The expected start date is May 1 2020.