Jason Hattrick‐Simpers
National Institute of Standards and Technology, University of Toronto
Papers
4
Total Citations
690
H-Index
4
About
Jason Hattrick-Simpers is a leading figure in autonomous experimentation and materials informatics, pioneering the integration of machine learning with closed-loop robotic systems to accelerate materials discovery. His highly cited work, including "Autonomous experimentation systems for materials development: A community perspective" (352 citations) and "On-the-fly closed-loop materials discovery via Bayesian active learning" (325 citations), has established foundational frameworks for self-driving laboratories that optimize experiments in real time. Hattrick-Simpers’ major contributions center on developing automated platforms that combine Bayesian active learning with modular hardware to dramatically speed up materials characterization and synthesis. His AMPERE series—the Automated Modular Platform for Expedited and Reproducible Electrochemical Testing—exemplifies this impact, with AMPERE-2 enabling reproducible electrodeposition and in situ catalyst testing in just 65 minutes per sample. By championing open-hardware designs and community-driven standards, Hattrick-Simpers has made high-throughput experimentation more accessible and reproducible, directly influencing how researchers approach data-driven materials development. His work bridges the gap between machine learning theory and practical laboratory automation, cementing his role as a key architect of next-generation, autonomous scientific discovery.
Research Focus
Key Achievements
Top Papers
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