Publisher Correction: Exploiting redundancy in large materials datasets for efficient machine learning with less data

Affiliations

• ¹ Department of Materials Science and Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, Canada.
• ² Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, MD, USA.
• ³ Canmet MATERIALS, Natural Resources Canada, 183 Longwood Road south, Hamilton, ON, Canada.
• ⁴ Department of Materials Science and Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, Canada. jason.hattrick.simpers@utoronto.ca.
• ⁵ Acceleration Consortium, University of Toronto, 27 King's College Cir, Toronto, ON, Canada. jason.hattrick.simpers@utoronto.ca.
• ⁶ Vector Institute for Artificial Intelligence, 661 University Ave, Toronto, ON, Canada. jason.hattrick.simpers@utoronto.ca.
• ⁷ Schwartz Reisman Institute for Technology and Society, 101 College St, Toronto, ON, Canada. jason.hattrick.simpers@utoronto.ca.