Adaptive Design of Ni-Mn-Ga-X Heusler alloys for high temperature magnetic shape memory applications (T03#)

• Subject Area: Computer-Aided Design of Materials and Simulation of Materials Behaviour from Atomic to Microscopic Scale
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 405553726

As a manifestation of multiferroic materials, the materials class of magnetic shape memory alloys exhibits fascinating physical properties such as magnetically induced martensite reorientation, superelasticity, magneto-caloric effect, resulting in a vast spectrum of potential applications. However, for the most promising Ni-Mn-Ga-based Heusler alloys, the origin of the martensite phases is still elusive, and there is still no efficient approach to systematically engineer such alloys for industrial applications. To this goal, it is of pivotal importance to stabilize the desired (pre-)martensite phases in the target working temperature range, especially towards a high temperature application (>400 K), and simultaneously to improve the magnetic field induced strain and work output by enhancing the MAE and reducing twinning stress via proper chemical composition engineering. The purpose of this project is to implement and apply an adaptive design strategy integrating database curation, machine learning modelling, density functional theory calculations, and experimental synthesis and characterization, in order to explore the huge chemical space efficiently to design Ni-Mn-Ga-based Heusler alloys for magnetic shape memory applications. We aim at establishing and conducting an operative closed-loop approach based on the Bayesian optimization, with the experimental validations incorporated as feedbacks to machine learning models. While a particular focus falls on the systematic evaluation of the relevant physical properties of Ni-Mn-Ga-based alloys based on accurate density functional theory calculations, as well as explicit data mining of the Ni-Mn-based systems with martensite structural transformations, we also dedicate special attention to developing and applying machine learning algorithms beyond the off-the-shelf solutions, together with quantitative benchmarking of both the theoretical and experimental results. Such an adaptive design strategy will be applied for engineering both permanent magnets and magnetocaloric materials in the future period of HoMMage.

• DFG Programme: CRC/Transregios (Transfer Project)
• Subproject of: TRR 270: Hysteresis Design of Magnetic Materials for Efficient Energy Conversion: HoMMage
• Applicant Institution: Technische Universität Darmstadt
• Business and Industry: ETO MAGNETIC GmbH
• Project Head: Professor Hongbin Zhang, Ph.D.