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About this Symposium
Meeting 2024 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
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 07/15/2023
Proceedings Plan Planned:

A Combined Physics-based and Data-driven Approach to Optimize the Device Characteristics of Multi-component Organic-photovoltaics
Accelerating Property Predictions in NiTi Shape Memory Alloys with Machine Learning and DFT
Augmenting the Discovery of Computationally Complex Ceramics for Extreme Environments with Machine Learning
Composition Design of High-entropy Alloys with Deep Sets Learning
Computational Design of Dual-metal-site Catalysts for Oxygen Reduction Reaction
Computational Discovery of B2 Phases in the Refractory High Entropy Alloys
Data-Driven Optimization of Interlocking Metasurface Design
Design Principles of N-doped Carbon Supported Single Atom Catalyst --- A High-throughput Computational Investigation
Discovery of Surfaces with Extreme Work Functions and High Stability by Machine Learning
Enhancing Drug-target Affinity Predictions with the Binding Site-augmented DTA Framework: A Deep Learning Approach for Expedited Material Design
Evaluation of Effective, Nonlinear Material Behavior of Fibrous Soft Tissues Using Embedded Finite Elements
First-principles Tools for the Design of Multi-component Materials
High-Throughput Artificial Neural Network - Kinetic Monte Carlo (ANN-KMC) Framework for Diffusion Studies in FeNiCrCoCu High-entropy Alloys of Versatile Compositions
Homogeneous Solute Segregation Suppressing Strain Localization in Nanocrystalline Ni-Nb Alloys
Impacts of Oxygen Doping on Sodium-ion Diffusion in Solid-state Batteries with Glassy Electrolyte: A Molecular Dynamics Perspective
Influence of the Local Environment on the Formation of Sulfur Vacancies in Calcium Lanthanum Sulfide
Interactions between Oxygen Vacancies and Polarons in Perovskite Oxides
Large-scale Ab-Initio Computation of Core Energetics of Pyramidal Dislocations in Mg and Mg-Y Alloy Using DFT-FE: Implications Towards Ductility Enhancement
Machine Learning Accelerated Thermodynamic Search for Ductile Cr-based Alloys for High-Temperature Applications Complemented by Ab-Initio Simulations
Machine Learning Driven Discovery and Modeling of Materials for Hydrogen Storage and Generation
Machine learning methods for improving molecular simulations
Materials Discovery via Machine Learning on Li-based Battery Materials
Methodology And Performance of a Deep Learning Model for Property Predictions and Discovery of Ni-based Superalloys
Microstructure-sensitive Calculations of Metal Nanocomposite Electrical Conductivity
MISPR: A High-throughput Multi-scale Infrastructure for Automating Materials Science Computations
Model of defect evolution and electrical performance of semiconductor devices under ionizing radiation
Modeling the Morphological Dependent Performance of an All Solid-state Battery
Optimization of Vaspsol Solvation Free Energy Predictions
Point Defect Engineering to Tune the Optical Absorption of Tetragonal Yttria-stabilized Zirconia
Representation-based Generative Models for Materials
Strengthening Glass Fiber-Epoxy Composites with Cellulose Nanocrystals: A Molecular Dynamics Investigation
Systematic Method for Material Selection for Nuclear Applications
Tailoring Oxidation Resistance of Refractory High Entropy Alloys by a Combined First-principles and CALPHAD Approach
The Integration of VASP 6’s Machine Learning Algorithms into the Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces Code to for Melting Point Determination
Unraveling the Mechanisms of Stability in CoMoFeNiCu High Entropy Alloys via Physically Interpretable Graph Neural Networks

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