The Robert W. Cahn Prize was established in 2012 to recognize the best paper published in the Journal of Materials Science during each calendar year. The “Cahn Prize” is named in honor of the Journal’s founding editor, the late Professor Robert Wolfgang Cahn FRS.

Each month the editors select a paper published in that month’s issues via a rigorous nomination and voting procedure. From those twelve finalists, a shortlist of three papers is generated that will produce a winner and two runners-up. This year more than 1200 papers presenting original research were considered. The review papers published in 2022 will be considered for the 2022 Bonfield Prize, which was awarded for the first time last year [1].

The shortlist was judged by Professor Sir Robin Grimes FRS FREng, a materials physicist at Imperial College, London, and BCH Steele Chair in Energy Materials. Sir Robin is a long-time member of the Editorial Board of the Journal of Materials Science and has published more than 300 papers. Incidentally, the late Brian Steele was himself the author of a highly cited paper in the Journal of Materials Science [2] and the Ph.D. advisor of the present author.

The winner of the 2022 Robert W. Cahn Best Paper award is “Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys” by Udesh M.H.U. Kankanamge, Johannes Reiner, Xingjun Ma, Santiago Corujeira Gallo, and Wei Xu. [3] This paper is the outcome of an international collaboration between Deakin University in Australia and Fudan University in China.

The runners-up are: “Phase transformation and incompatibility at grain boundaries in zirconia-based shape memory ceramics: a micromechanics-based simulation study” by Zhiyi Wang, Alan Lai, Christopher A. Schuh, and Raúl Radovitzky [4] and “Understanding the impact of texture on the micromechanical anisotropy of laser powder bed fused Inconel 718” by Jakob Schröder, Alexander Evans, Efthymios Polatidis, Jan Capek, Gunther Mohr, Itziar Serrano-Munoz, and Giovanni Bruno [5]. The paper by Zhiyi Wang et a collaboration between several groups at the Massachusetts Institute of Technology. The paper by Jakob Schröder et al. is another example of an international collaboration. In this case partnering Bundesanstalt für Materialforschung und -prüfung in Berlin, the Laboratory for Neutron Scattering and Imaging at the Paul Scherrer Institut, in Villigen, Switzerland, and Universität Potsdam.

Professor Grimes summarizes the winning paper and the two runners-up:

“Machine learning is certainly of great current interest to address the multivariable materials design challenge. While many ML studies demonstrate its capability, the work of Kankanage et al. differentiates ML models on the basis of addressing the multivariable composition-property challenge, helping others to select methodology best suited to similar materials problems. While it was a considerable challenge to select the best paper, I enjoyed how the ML results differentiated composition to multiple possible applications.”

“While it is easy to say strain effects at grain boundaries limit materials performance, the details are notoriously difficult to elucidate and reliable ways to make predictions elude us. The work of Wang et al. makes important contributions to our modelling capabilities and introduces unique features that others will wish to use, making it a worthy finalist.”

“In this beautifully crafted study Schroder et al. unravel the connection in this material between complex microstructure, texture and macroscopic properties linking their experimental observations with model predictions. They also point to how this work should progress and I am sure JMS readers will look forward to following their future endeavors.”

All the monthly finalists for the Cahn Prize are available for download, free of charge, at where you can also access the winning papers from previous years.