The Materials Intelligence Research  group develops and uses computational methods combining quantum physics with data science to invent new materials for energy and information technologies.



Performance of devices in energy and information technologies is controlled by functional materials that operate due to coupling and transport of electrons, phonons and ions. Complex energy conversion and carrier conduction mechanisms are difficult to probe experimentally, and we strive to accelerate their understanding and rational design using accurate physics and machine learning models,  aided by atomistic computations.  We make use of rapidly developing computer resources and big data technologies to make these methods efficient, automatic and transferable across diverse materials classes to enable fast computational screening. By working closely with experimental collaborators we emphasize technological relevance of our research.


Main interests

  • Physics of transport phenomena in complex functional materials and interfaces
  • Efficient transferable atomistic and electronic structure computation methods
  • Machine learning of new descriptors and structure-property relationships
  • Automated intelligent computational materials screening infrastructure