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About this Symposium
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Computational Materials Science and Engineering Committee
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Houlong Zhuang, Arizona State University
Duyu Chen, University of California, Santa Barbara
Ismaila Dabo, Pennsylvania State University
Yang Jiao, Arizona State University
Sara Kadkhodaei, University of Illinois Chicago
Mahesh Neupane, 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, 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 07/17/2022
Proceedings Plan Planned:

Adaptive Discovery and Mixed-Variable Bayesian Optimization of Next Generation Synthesizable Microelectronic Materials
An inverse materials design route based on structure-property linkages leveraging 3D Convolutional Neural Network and Bayesian Optimization
Applying data-driven models in materials science: unraveling hidden relationships between structures and properties
Atomistic Modeling of Electronic Transport and Electrochemistry
Bridging first-principles calculations with experiment: Insights from case studies on (photo)electrochemical systems
Building an ImageNet for materials grain boundaries
Closed Loop Computational Materials Discovery
Computational Design for Metallic Meso-architected Materials for Dynamics
Computational reconnoiter for the design of amorphous transition metal oxides for surface transfer doping of diamond
Computer Vision Problems in Transmission Electron Microscopy
Crystal Structure Generation Using Wasserstein Generative Adversarial Network
Crystal to PNG (xtal2png): a screening tool to accelerate domain transfer from state-of-the-art image-processing models to materials informatics and a case study on denoising diffusion probabilistic models
Data- and Physics-driven Approaches to Discovering the Governing Equations for Complex Phenomena in Heterogeneous Materials
Data-driven discovery of computationally complex ceramics for extreme environments
Design and Development of High Strength High Conductivity Alloys Using ICMDŽ Approach
Design of bistable metamaterials for desired dynamic behavior
Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
Designing Ohmic and Schottky interfaces for oxide electronics
Developing an Ab Initio-Kinetic Passivation Model for High-Throughput Screening of Material Stability
Electronic and Structural Properties of Ab-initio Predicted BxAl1-xN Alloy Structures
Elucidating the mechanisms for fast diffusion in doped LLZO
Exploiting first-principles based interpretation of X-ray Absorption Spectra of Ni, Cr, Fe elements in molten-salt system
Generative Adversarial Networks and Diffusion Models in Material Discovery
Graph Mining in materials science for the prediction of material properties
Machine-learning-boosted searching and optimization of emergent quantum materials
Machine learning assisted discovery of composite solid-state electrolytes in context of Li-ion batteries
Materials Modeling for Photoemission Electron Sources
Modeling of Local Lattice Distortion Effects on Vacancy Migrations in Multicomponent FCC Alloys
Modeling the high strain-rate deformation behavior of glass fiber reinforced epoxy composite in marine environments using molecular dynamics simulations
Molecular Dynamics Investigation of Electrochemical Systems
Searching for new "quantum defects" through high-throughput computational screening
Ultra-fast interpretable machine-learning potentials for accelerated structure prediction of materials
What is a minimal working example for a materials acceleration platform?

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