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About this Symposium
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Hume-Rothery Symposium on First-Principles Materials Design
Sponsorship TMS Functional Materials Division
TMS Structural Materials Division
TMS: Alloy Phases Committee
Organizer(s) Bin Ouyang, Florida State University
Mark D. Asta, University of California, Berkeley
Geoffroy Hautier, Dartmouth College
Wei Xiong, University of Pittsburgh
Anton Van der Ven, University of California, Santa Barbara
Scope This symposium will bring together experts in the application of first principles calculations of complex and functional materials, to assess the current state of the art in their application to ab-initio and data-driven materials discovery and design. Topics will cover but not limited to high throughput materials discovery, first principles-based phase diagram constructions, thermodynamic and kinetic properties of multi-component materials, and the use of ab-initio methods to understand the synthesis of materials. It will survey recent progress in method and theory developments that are driven by the materials genome initiatives, with a particular emphasis on development of computational and machine-learning methods and autonomous experimentation to guide materials synthesis, characterization, and new functionality.

Sessions will include talks by experts in computational methods and applications, as well as experimenting working at the forefront of data-driven synthesis and characterization.

The session is by invitation only.

Abstracts Due 07/17/2022
Proceedings Plan Planned:

Ab initio thermodynamics and kinetics from alloys to complex oxides
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 - 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

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