PeopleofSuperintelligence

Researcher

Jason Hattrick-Simpers

Jason Hattrick-Simpers is a materials scientist who uses AI and automated experiments to discover alloys and oxides that hold up in extreme environments. After a PhD at the University of Maryland in 2007, he went to NIST as a postdoctoral fellow and became an assistant professor of chemical engineering at the University of South Carolina in 2011. The Maryland Science Center gave him its Outstanding Young Engineer award in 2019, and after a stint as a NIST staff scientist he joined the University of Toronto in 2021. A 2023 paper he coauthored with Kangming Li, Brian DeCost, Kamal Choudhary and Michael Greenwood showed that machine-learning models trained on Materials Project 2018 lose accuracy on newer compounds, and in 2025 he brought a self-driving electrochemistry lab that two undergraduates built under his supervision for under $500 into his course on AI for self-driving labs. He is a professor of materials science and engineering at Toronto, where his group AUTODIAL works on autonomous discovery of alloys, and he is a faculty affiliate of the Vector Institute.

Now at
CanmetMATERIALS, Acceleration Consortium and Vector Institute
Badges

Timeline

  1. 2007

    A Maryland PhD and NIST fellowship

    A Rowan University student, he spends two summers at Maryland as an undergraduate research fellow, then completes a PhD in materials science and engineering there in 2007 under Ichiro Takeuchi. A NIST/NRC Postdoctoral Research Associateships fellowship takes him to NIST's Metallurgy Group, where he develops a rapid screening technique for measuring hydrogen storage in thin-film samples.

  2. 2011–2015

    Assistant professor at South Carolina

    He joins the University of South Carolina's Department of Chemical Engineering as an assistant professor in 2011, recruited to launch a research program in combinatorial materials synthesis for hydrogen storage and other energy applications. In 2015, the university's Office of the Vice President for Research names him one of six Breakthrough Stars.

  3. 2019

    Outstanding Young Engineer award

    In 2019, the Maryland Science Center honors him with its Outstanding Young Engineer award. His research integrates machine learning, artificial intelligence and high-throughput experiments to optimize the properties and processing of novel materials.

  4. 2021

    Joining Toronto and CanmetMATERIALS

    After working as a staff scientist at NIST in Gaithersburg, he joins the University of Toronto's Department of Materials Science & Engineering in 2021 and becomes a research scientist at CanmetMATERIALS. He is also the newest member of U of T's Acceleration Consortium, which uses AI to accelerate the design and discovery of advanced materials.

  5. 2023

    Testing machine-learning generalization

    In 2023, he coauthors an npj Computational Materials paper with Kangming Li, Brian DeCost, Kamal Choudhary and Michael Greenwood. It shows that machine-learning models trained on Materials Project 2018 can degrade severely on new compounds in Materials Project 2021, and that UMAP-guided and query-by-committee strategies improve accuracy by adding only 1% of the test data.

  6. 2023

    Alloys from the Sputtertron

    In 2023, he is using AI at the Acceleration Consortium to develop and test wear-resistant alloys that resist rust and corrosion. A sputtering tool called the Sputtertron, whose AI system analyzes each batch of alloys and plans the next round of tests, produces about 24 new samples a day, compared with the two or three his team made before.

  7. 2025

    A self-driving lab under $500

    Two U of T Engineering undergraduates, Kyrylo Kalashnikov and Robert Hou, build a self-driving lab for electrochemistry under his supervision, using off-the-shelf parts and open-source software for under $500. In 2025, he integrates the system into his course MSE 403/1003, Advanced AI for Self-Driving Labs.

  8. As of October 2026

    Professor and Vector affiliate

    He is a Professor in the Department of Materials Science and Engineering at the University of Toronto, where his research group is AUTODIAL (AUTOnomous DIscovery of ALloys). His research focuses on using AI and experimental automation to discover alloys and oxides that survive extreme environments and materials for energy conversion and storage. He is also a Research Scientist at CanmetMATERIALS, a researcher at the Acceleration Consortium and a faculty affiliate of the Vector Institute.

Career

  1. Since 2021University of TorontoProfessor
  2. Since 2021CanmetMATERIALSResearch scientist
  3. Since 2021Acceleration ConsortiumResearcher
  4. NowVector InstituteFaculty affiliate
  5. 2011–2015University of South CarolinaAssistant professor
  6. 2007–2021NISTPostdoctoral researcher, Staff scientist
  7. University of MarylandUndergraduate research fellow

Education

  1. 2007University of MarylandPhD
  2. Rowan UniversityUndergraduate student

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Toronto. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Vector Institute. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  3. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Toronto. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  4. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorateIncludes University of Toronto. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorate. Across 2,116 people with both an undergraduate degree and a doctorate recorded, we trace which undergraduate schools feed which doctoral programs, how often students stay at the same school, and how paths differ by birth-country region.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Alumnus Named “Breakthrough Star” by University of South Carolina | Department of Materials Science and Engineering mse.umd.edu. Checked 21 October 2026.
  2. MSE@UMD Grads: The Next Generation of Faculty, Part 2 | Department of Materials Science and Engineering mse.umd.edu. Checked 21 October 2026.
  3. Jason Hattrick-Simpers Honored by Maryland Science Center | NIST nist.gov. Checked 21 October 2026.
  4. MSE welcomes new faculty member Jason Hattrick-Simpers - U of T Engineering News news.engineering.utoronto.ca. Checked 21 October 2026.
  5. A critical examination of robustness and generalizability of machine learning prediction of materials properties | npj Computational Materials nature.com. Checked 21 October 2026.
  6. Automating discovery: The rise of robot labs - MaRS Discovery District marsdd.com. Checked 21 October 2026.
  7. Low-cost, open-source robotic system could make self-driving labs more accessible to students and researchers - Department of Mechanical & Industrial Engineering mie.utoronto.ca. Checked 21 October 2026.
  8. Jason Hattrick-Simpers - Department of Materials Science & Engineering mse.utoronto.ca. Checked 21 October 2026.
  9. Jason Hattrick-Simpers - Vector Institute for Artificial Intelligence vectorinstitute.ai. Checked 21 October 2026.