Jason R. Hattrick-Simpers

TL;DR: A clear relationship between the hysteresis and the middle eigenvalue of the transformation stretch tensor as predicted by the theory was observed for the first time and a new composition region of titanium-rich SMAs is identified with potential for improved control of SMA properties.

TL;DR: This work trains a machine learning model on previously reported observations, parameters from physiochemical theories, and makes it synthesis method–dependent to guide high-throughput experiments to find a new system of metallic glasses in the Co-V-Zr ternary, and provides a quantitatively accurate, synthesis method-sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses.

TL;DR: An effort to deploy a federated network of high-throughput experimental tools, which are integrated with a modern materials data infrastructure, is needed.

TL;DR: High throughput (combinatorial) materials science methodology is a relatively new research paradigm that offers the promise of rapid and efficient materials screening, optimization, and discovery as mentioned in this paper, which is characterized by synthesis of a "library" sample that contains the materials variation of interest (typically composition).

TL;DR: By tuning the presence of structural heterogeneity in textured Co(1-x)Fe(x) thin films, effective magnetostriction λ(eff) as large as 260 p.p.m. can be achieved at low-saturation field of ~10 mT, indicating that the recently proposed heterogeneous magnetostrict mechanism can be used to guide exploration of compounds with unusual magnetoelastic properties.