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Aaron Gilad Kusne (Fed)

Research Scientist, Materials Measurement Science Division, National Institute of Standards and Technology  
Adjunct Associate Professor of Materials Science & Engineering, University of Maryland College Park
Fellow of the American Physical Society

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.

Project: Materials Exploration, Discovery, and 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, resulting in the first autonomous system to discover a best-in-class material (phase-change memory material) CAMEO:

Icons from CAMEO paper available below. Please reference: Kusne, A.G., et al. "On-the-fly closed-loop materials discovery via Bayesian active learning." Nature communications 11.1 (2020): 1-11.
For other versions, please contact me.

CAMEO Icons. Please cite: AG Kusne,





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

ANDiE (Autonomous Neutron Diffraction Explorer) demonstrates autonomous control over neuron scattering at the NIST Center for Neutron Research and the Oak Ridge National Laboratory High Flux Isotope Reactor. ANDiE achieves a 5x acceleration in determining magnetic structure and transition behavior through novel machine learning with built-in magnetic structure and neutron scattering physics.

Project: Low-Cost Autonomous Scientist Kit
We've developed a low cost autonomous scientist kit for Teaching and Developing machine learning.

We have developed the next generation in science education - a kit for building a low-cost autonomous scientist capable of experiment design, execution, and analysis in a closed loop. This kit can be used to teach the next generation workforce in areas such as ML, control systems, measurement science, materials synthesis, decision theory, among others. Industry can also use the kit to develop and evaluate autonomous methodologies. The kit was used during two courses at the University of Maryland to teach undergraduate and graduate students autonomous physical science. The kit was demonstrated for hypothesis design, discovery, and validation.


Project: Autonomous Protein Engineering
The complexity of biological systems is incredible. We are combining ML and robotics to build a greater understanding of protein engineering.,,


Project: ML for Accelerating Materials Research
We use ML to learn about important materials (e.g. superconductors) and guide research in the lab. 

ML for Superconductivity








Project: Bootcamp: Machine Learning for Materials Research
Educating the next generation of physicists and materials scientists.  
MLMR 2020 180 attendees joined us from 13 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, TMS, MLSE, NSF meetings, among others.


Project: 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
ML for Superconductivity: Scientific American: Our ML-driven search for room-temperature superconductors.
CAMEO + Materials Discovery: AAAS Eureka Alert!; Science Daily; Semiconductor Engineering; Phys.orgOptics.orgScience Bulletin; COSMOS MagazineChemical Engineering; UMD News; NIST News

Current Mentees
Austin McDannald
Machine Learning + Materials Science  
NIST Postdoc

Jong Ho Kim
Machine Learning + Materials Science
Research Scientist, Research Institute of Industrial Science & Technology, Korea

Haotong Liang
Machine Learning + Materials Science
UMD PhD Student

Chih-Yu Lee
Machine Learning + Materials Science
UMD PhD Student

Felix Adams
Machine Learning + Materials Science
UMD PhD Student

Logan Saar
Machine Learning + Materials Science
UMD Undergraduate Student

Alex Wang
Machine Learning + Materials Science
UMD Undergraduate Student


Past Mentees
Peter Tonner
NIST NRC Postdoc, Machine Learning + Genetics
Current: Research Scientist, NIST

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

Graham Antoszewski
UMD Masters in Applied Math  
Current: BlackSky

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

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

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

NRC Postdoc Postings:

Publication List

See my google scholar page


Recently Organized Workshops

2016+ Annual 5-Day Machine Learning for Materials Research Bootcamp
2022 Advances in Autonomous Materials Research
2022 MRS Biannual Tutorial Day on Data Science
2021 MRS Fall Symposium: Accelerating Experimental Materials Research with Machine Learning
2021 MRS Tutorial Day: Machine Learning for Materials Science and Engineering
2021 Workshop on Autonomous Materials Research
2021 Materials Research Data Alliance Workshop: Education and Workforce Development
2021 LBL Workshop: Workshop on Autonomous Discovery in Science and Engineering
2020 MRS Kavli Workshop: Building a Community for Autonomous Research, Online.
2020 Workshop on Machine Learning Microscopy Data, College Park, MD
2019 Autonomous Systems for Materials Development Workshop, Philadelphia, PA
2019 MLMR Workshop on Autonomous Research, College Park, MD


Bronze Award - The Bronze Medal Award is the highest recognition awarded by NIST. 


An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Jason Hattrick-Simpers, Brian DeCost, Aaron Gilad Kusne, Howard Joress, Winnie Wong-Ng, Debra Kaiser, Andriy Zakutayev, Caleb Phillips, Tonio Buonassisi, Shijing Sun, Janak Thapa
Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models

On-the-fly closed-loop materials discovery via Bayesian active learning

Aaron Gilad Kusne, Heshan Yu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Albert Davydov, Leonid A. Bendersky, Apurva Mehta, Ichiro Takeuchi
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Kamal Choudhary, Kevin Garrity, Andrew C. Reid, Brian DeCost, Adam Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using
Created April 7, 2019, Updated July 11, 2022