Aaron Gilad Kusne

Autonomous Phase Mapping and Materials Optimization
The structure of a material greatly influences its properties. Thus the search for better materials must often include knowledge of the relationship between how a material is made and the resulting structure, i.e. "phase mapping".  
Autonomous phase mapping at the Stanford Linear Accelerator has allowed us to reduce the number of measurement experiments necessary for phase mapping by an order of magnitude. This in turn accelerates materials optimization and discovery.  E.g. arXiv:2006.06141

Autonomous Metrology
We are investigating the use of ML to guide microscopy and other measurement systems to accelerate knowledge capture.  

Autonomous Protein Engineering
The complexity of biological systems is incredible. We are combining ML and robotics to build a greater understanding of protein engineering.  
https://arxiv.org/abs/1911.02106, https://doi.org/10.1101/2020.03.05.979385, https://doi.org/10.1101/2020.07.10.197574

Bootcamp: Machine Learning for Materials Research
Educating the next generation of physicists and materials scientists.  
MLMR 2020 180 attendees joined us from 12 countries, 30% from industry.  Over the 5 years of the bootcamp, we have had attendees from  a total of 19 countries. We have also run tutorials at MRS, APS, MLSE, NSF meetings, among others.

REMI: REsource for Materials Informatics
A repository for tutorials and code examples covering materials data import/export, pre-processing, and analysis. Search by material system, synthesis / simulation method, measurement method, data type, data analysis type, and more.