About this Symposium

Meeting

MS&T24: Materials Science & Technology

Symposium

Understanding High Entropy Materials via Data Science and Computational Approaches

Sponsorship

TMS: Alloy Phases Committee

Organizer(s)

Dilpuneet S. Aidhy, Clemson University
Raymundo Arroyave, Texas A&M University
Timothy J. Rupert, Johns Hopkins University
Liang Qi, University of Michigan
Wei Xiong, University of Pittsburgh
Prashant Singh, Ames National Labratory

Scope

This symposium will focus on the ongoing computational efforts to develop scientific understanding of high entropy materials (HEMs). Due to the presence of multiple elements in large proportions that are randomly distributed on a crystal lattice, on the one hand, HEMs present exciting opportunities for rich physics, whereas on the other, their large phase space leads to a multitude of challenges from computational expense to model development. The field has multiple open lines of questioning in the areas of phase stability, electronic frustration, lattice distortion, short-range order, grain boundary, dislocation, and microstructure. These features are well-known to be responsible for novel HEM properties including mechanical, thermophysical and electrochemical. Various computational modeling and simulation approaches are being used to unveil underlying correlations between the features and the properties. The symposium seeks abstracts that develop and apply such computational approaches at electronic, atomic, mesoscale, and multiscale levels to discover, understand and engineer new HEMs including alloys and ceramics.

Data-science modeling is playing a crucial role in developing understanding of structure-property-processing relationships, and in addressing the phase-space challenge in HEMs. These efforts are being assisted by emerging data repositories. The symposium also seeks abstracts on new data-science approaches being developed and deployed for HEMs. Finally, the symposium will also consider ICME approaches and their applications to HEM manufacturing.

Some examples include:
• Novel electronic-structure based methods and tools to understand phase stability, free energy, structure-property understanding, etc.

• Molecular dynamics and Monte Carlo simulations to understand deformation and microstructure evolution including interatomic potential development.

• Thermodynamic modeling for predicting microstructure and phase stability.

• Mesoscale and multiscale modeling to understand grain boundary and microstructure evolution.

• Data-science and high-throughput approaches to materials design.

• Data-science frameworks, data-repository development, and approaches to analyze experimental results.

• Computational methods for HEM development for extreme environments.