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Aaron Gilad Kusne

Research Scientist, Materials Measurement Science Division, National Institute of Standards & Technology  
Adjunct Associate Professor of Materials Science & Engineering, University of Maryland College Park

Autonomous Materials Research Systems

Accelerating Discovery & Democratizing Science

Main Projects
Key focus - Machine learning for science: active learning to guide and optimize experiments, incorporating prior scientific knowledge into ML, uncertainty quantification and propagation, trust and interpretability.
Real world successes: discovery of new materials in rare-earth free permanent magnetics, spin-driven thermoelectrics, and phase change materials.


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

 

                       
ML for Accelerating Materials Research
We use ML to learn about important materials (e.g. superconductors) and guide research in the lab. https://www.nature.com/articles/s41524-018-0085-8 
ML for Superconductivity

 

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.

 

 

 


In the News
Scientific American: Our ML-driven search for room-temperature superconductors.
 


Group Members
Peter Tonner
Machine Learning + Genetics  
NIST NRC Postdoc

Austin McDannald
Machine Learning + Materials Science  
NIST Postdoc

Amit Verma
Machine Learning + Materials Science  
CMU Postdoc


Past Group Members
Brian DeCost
NRC Postdoc, Machine Learning + Materials Science  
Current: Research Scientist, NIST

Graham Antoszewski
UMD Masters in Applied Math  
Current: BlackSky
 


Past Mentees
Yuma Iwasaki
Research Scientist, NEC  
Current: Research Scientist, NEC

Varshini Salvedurai
Summer High School Student (SHIP)  
Current: CMU CS Undergrad
 


Open Positions
For openings, please contact me at: aaron(.)kusne(@)nist(.)gov  

NRC Postdoc Postings:



Publication List

See my google scholar page

 

Awards

Bronze Award

Publications

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics

Author(s)
Kamal Choudhary, Aaron G. Kusne, Francesca M. Tavazza, Jason R. Hattrick-Simpers, Rama K. Vasudevan, Apurva Mehta, Ryan Smith, Lukas Vlcek, Sergei V. Kalinin, Maxim Ziatdinov
The use of advanced data analytics, statistical and machine learning approaches (‘AI’) to materials science has experienced a renaissance, driven by advances in
Created April 7, 2019, Updated August 10, 2020