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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Organizer(s) Houlong Zhuang, Arizona State University
Ismaila Dabo, Pennsylvania State University
Arezoo Emdadi, Missouri University Of Science And Technology
Yang Jiao, Arizona State University
Sara Kadkhodaei, University of Illinois Chicago
Mahesh Neupane, DEVCOM Army Research Laboratory
Xiaofeng Qian, Texas A&M University
Arunima K. Singh, Arizona State University
Natasha Vermaak, Lehigh University
Scope Recent advancements in computational methods, computing power and materials informatics present us with an exciting opportunity to predictively discover and design materials for a variety of technologically relevant applications. In particular, classical molecular dynamics and quantum mechanical ab-initio methods such as density-functional theory simulations, dynamical mean-field theory, quantum Monte-Carlo simulations and time-dependent density functional theory have been pivotal in developing an atomistic-scale fundamental understanding of complex phenomena, and in the discovery and the design of several emerging materials, such as superconductors, topological insulators, magnetic materials, photocatalysts, battery materials, and most recently, quantum materials. This symposium will cover the state-of-the art in the application as well as the integration of computational methods, particularly ab-initio simulation methods, with experiments and materials informatics applied to the discovery and design of emerging materials.

Topics addressed in this symposium will include (but not be limited to):
• Computational discovery and design of correlated electron materials, quantum materials, and superconductors
• Computational discovery and design of magnetic materials and topological insulators
• Application of computational methods for photocatalytic and battery materials discovery and design
• Computational discovery and design of materials for nanoelectronics, and power and RF electronics
• Application of materials informatics approaches such as machine learning, genetic algorithms, and cluster expansion for an accelerated discovery and design of materials

Abstracts Due 07/01/2023
Proceedings Plan Undecided
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