High-entropy materials (HEMs), including alloys, ceramics, oxides, and semiconductors, have attracted enormous activities to investigate their attractive/excellent properties and potential critical applications. With the contributions of multiple principal atoms, it expects the higher configurational entropy, driving a tendency to form simple solid solutions (amorphous or crystalline) rather than complex microstructures with many compounds. Together with the high-throughput experiments and modeling, the Integrated Computational Materials Engineering (ICME) approach consisted of CALPHAD, (ab initio) molecular dynamics, phase-field simulations, finite element calculations, Monte Carlo, etc. has been supported by the Material Genome Initiative/Engineering (MGI/MGE) and are boosting the database. Toward the inheritable integrated intelligent manufacturing (I3M) era, data driven ICME is critical to accelerate the discoveries and applications of novel advanced HEMs.
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.
High-entropy materials (HEMs), including alloys, ceramics, oxides, and semiconductors, have attracted enormous activities to investigate their attractive/excellent properties and potential critical applications. With the contributions of multiple principal atoms, it expects the higher configurational entropy, driving a tendency to form simple solid solutions (amorphous or crystalline) rather than complex microstructures with many compounds. Together with the high-throughput experiments and modeling, the Integrated Computational Materials Engineering (ICME) approach consisted of CALPHAD, (ab initio) molecular dynamics, phase-field simulations, finite element calculations, Monte Carlo, etc. has been supported by the Material Genome Initiative/Engineering (MGI/MGE) and are boosting the database. Toward the inheritable integrated intelligent manufacturing (I3M) era, data driven ICME is critical to accelerate the discoveries and applications of novel advanced HEMs.
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.