Skip to the content.

A. 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
aaron(.)kusne(@)nist(.)gov

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. e.g. https://www.nature.com/articles/s41524-018-0085-8





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. (linked)


Organized Workshops