Ye, Y.; Lish, S. D.; Xu, L.; Woods, E.; Chen, S.; Ren, Y.; Wittmann, M. W.; Xu, H.; Gault, B.; Baker, I.: Exceptional Soft Magnetic Properties of an Ordered Multi-principal Element Alloy with Disordered Nanoprecipitates. High Entropy Alloys & Materials (2022)
Wu, M.; Li, Z.; Gault, B.; Munroe, P.; Baker, I.: The Effects of Carbon on the Phase Stability and Mechanical Properties of Heat-Treated FeNiMnCrAl High Entropy Alloys. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 748, pp. 59 - 73 (2019)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.