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
- Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization
- Machine Learning for Autonomous Genetic Engineering of Microbial Systems
- Machine Learning for High Throughput Materials Discovery and Optimization Applications
Publication List
See my google scholar page. (linked)