About this Research Topic
In this article collection, the frontiers in HEMs will be reviewed and highlighted, presenting recent research on the fundamental understanding and theoretical modelling of the composition-processing-microstructure-property-performance relationship of HEMs. In contrast to conventional alloys based upon one principal element, HEMs have multiple principal elements, often five or more. The significantly-high entropy of the solid solution stabilizes the solid-solution phases in face-centered-cubic (FCC), body-centered-cubic (BCC), and hexagonal-close-packed (HCP) structures against intermetallic compounds. Moreover, carefully designed HEMs possess tailorable properties that far surpass their conventional alloys. Such properties in HEMs include high strength, ductility, ultra-high melting, electrical and thermal conductivities, corrosion resistance, oxidation resistance, fatigue and wear resistance. These properties will undoubtedly make HEMs of interest for use in biomedical, structural, mechanical, and energy applications. Given the novel and exciting nature of HEMs, they are poised for significant growth and present a perfect opportunity for a new symposium and research field.
Submissions should integrate different aspects of the following, including both experimental and computational aspects:
• Multi-scale computations and modelling using density functional theory, molecular dynamics, Monte Carlo simulations, phase-field and finite-elements method, and high-throughput CALPHAD modeling;
• Material fabrication and processing, such as homogenization, nanomaterials, and grain-boundary engineering;
• Advanced characterization, such as neutron and synchrotron scattering and three-dimensional (3D) atom probe;
• Thermodynamics and diffusivity: measurements and modeling;
• ICME studies of HEMs mechanical behaviors (fatigue, creep, wear, high strain rate deformation, and fracture), corrosion, physical, magnetic, electric, thermal, thermoelectric, coating, biomedical behavior, ultra-high melting, electrical and thermal conductivities, etc.
• Data mining & machine learning for accelerating the discovery of advanced HEMs.
Keywords: Data Mining, Machine Learning, Database, Properties, High-entropy materials, Modeling, Computation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.