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About this Symposium
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Computational Thermodynamics and Kinetics
Sponsorship TMS Functional Materials Division
TMS Materials Processing and Manufacturing Division
TMS: Chemistry and Physics of Materials Committee
TMS: Computational Materials Science and Engineering Committee
TMS: Integrated Computational Materials Engineering Committee
TMS: Solidification Committee
Organizer(s) Hesam Askari, University Of Rochester
Damien Tourret, IMDEA Materials Institute
Eva Zarkadoula, Oak Ridge National Laboratory
Enrique Martinez Saez, Clemson University
Frederic Soisson, CEA Saclay
Fadi Abdeljawad, Clemson University
Scope The Computational Thermodynamics and Kinetics (CTK) symposium, held yearly for over 20 years, highlights the latest advances in computational tools and techniques that broaden our understanding of the thermodynamics and kinetics of materials. Advanced CTK methods play an ever-increasing role, not only in bringing new insight in the fundamental behavior of materials, but also for the conceptual design and discovery of novel materials systems with outstanding properties. This symposium will cover topics related to the stability, synthesis, properties, and discovery of new materials, based on computational methods, including data-based and high-throughput methods, and the integration of computational tools with experiments and processes.

Topics of interest include, but are not limited to:

• Phase prediction, equilibria, stability, transformations, electronic and photonic performance, and nano/micro-structural evolution, including the influence of defects and interfaces;
• Innovative computational approaches for materials discovery and design;
• Alloy design, microstructure control, multi-phase/multi-component systems;
• Prediction of materials properties (mechanics, chemistry, electronic, transport, etc.);
• Effect of external and internal constraints (elastic, plastic, electric, magnetic, etc.) on the stability, microstructure, and properties of materials;
• Integration of CTK with experiments and computationally-guided synthesis of materials;
• Advanced statistical and data-based methods (e.g. machine learning, uncertainty quantification) for CTK.

Abstracts Due 07/01/2022
Proceedings Plan Undecided
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