Faculty Directory
Bryce Meredig

Adjunct Professor of Materials Science and Engineering


2145 Sheridan Road
Evanston, IL 60208-3109

Email Bryce Meredig


Materials Science and Engineering


PhD, Materials Science and Engineering, Northwestern University, Evanston, IL

MBA, Stanford University, Stanford, CA

BAS Materials Science and Engineering & German Studies, Stanford University, Stanford, CA

Research Interests

Dr. Bryce Meredig is broadly interested in AI, data, simulations, and automation in materials science. His publications cover topics including machine learning methods tailored to materials design; self-driving simulations and labs; and materials databases and data infrastructure.

Dr. Meredig also has extensive experience as an entrepreneur. He co-founded Citrine Informatics in 2013, serving as CEO and later Chief Science Officer, helping to grow the company over nearly a decade into a leading provider of enterprise materials informatics software. Today, he enjoys serving as a mentor for the next generation of hard tech founders.

Significant Recognition

  • AIME Robert Lansing Hardy Award
  • Early Career Achievement Award, Department of Materials Science and Engineering, Northwestern University

Selected Publications

L Kavalsky, VI Hegde, ES Muckley, MS Johnson, B Meredig, V Viswanathan. By how much can closed-loop frameworks accelerate computational materials discovery? Dig Discov 10.1039/D2DD00133K (2023).

CKH Borg, ES Muckley, C Nyby, JE Saal, L Ward, A Mehta, B Meredig. Quantifying the performance of machine learning models in materials discovery, Dig Discov 2, 327-338 (2023).

AY Fong, L Pellouchoud, M Davidson, RC Walroth, C Church, E Tcareva, L Wu, K Peterson, B Meredig, CJ Tassone. Utilization of machine learning to accelerate colloidal synthesis and discovery, J Chem Phys 154 (22), 224201 (2021).

Y Kim, EMT Antono, ES Kim, BW Meredig, JB Ling. Predictive design space metrics for materials development, U.S. Patent 10,657,300, issued May 19, 2020.

B Meredig, E Antono, C Church, M Hutchinson, J Ling, S Paradiso, B Blaiszik, I Foster, B Gibbons, J Hattrick-Simpers, A Mehta, L Ward. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery, Mol Syst Des Eng 10.1039/C8ME00012C (2018).