| Scope |
This symposium will explore recent advancements in the understanding, design, manufacturing, and applications of high entropy materials (HEMs)—including alloys, ceramics, and composites—by synergizing experimental characterization, computational modeling, and machine learning approaches. HEMs consist of multiple principal elements with significant configurational disorder. HEMs present both a fertile ground for novel physical phenomena and a large design space. This inherent complexity presents challenges across scales, from electronic, atomic to microstructure levels. To tackle these challenges, the materials community is increasingly leveraging interdisciplinary methodologies that include: (1) Advanced experimental techniques such as in situ microscopy, atom probe tomography, and neutron scattering to probe local structure, defect chemistry, and thermomechanical behavior, (2) Computational approaches spanning electronic structure calculations, atomistic simulations, and multiscale modeling to predict phase stability, lattice distortions, chemical short-range order, and microstructural evolution, and (3) Machine learning and data science in accelerating discovery, interpreting large datasets, navigating high-dimensional compositional space, and establishing structure–property–processing relationships.
The symposium welcomes abstracts on all aspects of HEMs research. Submissions highlighting the development or use of open data repositories, interpretable machine learning models, or automation in experimental and computational workflows are encouraged. We also seek contributions that advance ICME (Integrated Computational Materials Engineering) strategies to support scalable manufacturing and deployment of HEMs in extreme environments (e.g., high temperature, radiation, corrosive conditions).
Example topics include, but are not limited to:
• Experimental exploration of HEMs using high-throughput synthesis, combinatorial approaches, and in situ characterization.
• Phase stability and kinetics investigations including thermodynamic and diffusion modeling to predict multi-phase equilibria and microstructural transitions.
• Various properties including mechanical, physical, corrosion, oxidation, irradiation, hydrogen embrittlement, magnetic, magnetocaloric, thermoelectric, superconducting, catalytic, and other functional properties.
• Microstructure analysis under normal and extreme environments.
• Multiscale modeling including electronic, atomistic, mesoscale and continuum levels.
• Machine learning for property prediction, uncertainty quantification, phase-space exploration, and design optimization.
• Development of interoperable data infrastructure and ontologies for HEMs.
• ICME and AI-guided workflows for accelerated materials discovery and deployment. |