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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Hume-Rothery Symposium on First-Principles Materials Design
Presentation Title Holistic Integration of Experimental and Computational Data and Simple Empirical Models for Diffusion Coefficients of Metallic Solid Solutions
Author(s) Wei Zhong, Ji-Cheng Zhao
On-Site Speaker (Planned) Ji-Cheng Zhao
Abstract Scope Large amounts of both experimental and computational diffusion coefficients are available in the literature for careful assessments of the degree of agreements between the datasets. Such systematic assessments allow us to employ the best of both datasets and provide complementary data to establish the most reliable diffusion databases. The power of such holistic integration will be illustrated with unary and binary solid solutions. In addition, simple and yet generally applicable semi-empirical models of diffusion coefficients of binary and multicomponent metallic solid solutions are developed. Only one-fitting parameter is required to fully describe all the diffusion coefficients as a function of composition and temperature of a binary solution. Future calculations of this parameter and impurity (dilute) diffusion coefficients using first principles and machine-learning methods would be extremely valuable for the establishment of reliable diffusion (atomic mobility) databases for kinetic simulations of alloys.
Proceedings Inclusion? Planned:
Keywords ICME, Computational Materials Science & Engineering, Magnesium

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advances in Natural Language Processing for Building Datasets in Materials
Available methods for predicting materials synthesizability using computational and machine learning approaches
Computational Design of Multicomponent Nanoparticle Morphologies
Computational Discovery of Materials with Fast Oxygen Kinetics
Computational materials design and discovery for next-generation solid-state batteries
Computational tools for the generation of high-dimensional phase diagrams
Design of Novel Electrode and Solid Electrolyte Materials Guided by Crystal Structure Characterization and Understanding
Disorder and degradation in rock-salt-type lithium-ion battery cathodes
Double Descent, Linear Regression, and Fundamental Questions in Alloy Model Building
Dynamic stability design of materials for solid-state batteries
Establishing links between synthesis, defect landscape, and ion conduction in halide-type solid electrolytes
First principle design of high entropy materials for energy storage and conversion
From atom to system - how to build better batteries
Holistic Integration of Experimental and Computational Data and Simple Empirical Models for Diffusion Coefficients of Metallic Solid Solutions
Learning Rules for High-Throughput Screening of Materials Properties and Functions
Linking phenomenological theories of materials to electronic structure
Machine Learning Assisted Materials Generation
Machine Learning for Simulating Complex Energy Materials with Non-Crystalline Structures
Matterverse.ai - A graph deep learning database of materials properties
Microstructure modeling with machine learning
Millisecond-ion Transport in Mixed Polyanion in Energy Materials
New battery chemistry from conventional layered cathode materials for advanced lithium-ion batteries
Origin of the Invar effect
Plasmonic high-entropy carbides
Predicting synthesis and synthesizability beyond the DFT convex hull
Probing Local Structures, Electronic Structures and Defects in Battery Materials by Combining NMR and DFT Calculations
Structure determination – from materials design to characterization
The Stewardship of a Materials Genome
Understanding Complex Materials and Interfaces through Molecular Dynamics Simulations
Understanding key properties of disordered rock-salt Li-ion cathode materials based on ab initio calculations and experiments
William Hume-Rothery Award Lecture: Ab initio Thermodynamics and Kinetics from Alloys to Complex Oxides

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