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About this Symposium
Meeting 2019 TMS Annual Meeting & Exhibition
Symposium Computational Materials Discovery and Design
Sponsorship TMS: Computational Materials Science and Engineering Committee
Organizer(s) Oliver Johnson, Brigham Young University
Arunima K. Singh, Arizona State University
Jacob L. Bair, Oklahoma State University
Christopher R. Weinberger, Colorado State University
Timofey Frolov, Lawrence Livermore National Laboratory
Ning Zhang, Baylor University
Fadi Abdeljawad, Clemson University
Richard G. Hennig, University of Florida
Mikhail Mendelev, KBR
Avinash M. Dongare, University of Connecticut
Scope Advances in theoretical understanding, algorithms, and computational power are enabling computational tools to play an increasing role in materials discovery, development, and optimization . Recently application of data mining techniques, genetic algorithms, machine learning approaches, and predictive empirical potentials demonstrate the “virtual synthesis” of novel materials, with their properties being predicted on a computer before ever being synthesized in a laboratory. This symposium will cover recent methodological developments and applications at the frontier of computational materials science and materials informatics, ranging from quantum-level prediction to macro-scale property optimization. The goal is to cover basic research topics in an interdisciplinary approach, which connects theory and experiment, with a view towards materials applications. Of particular interest is computational and theoretical work that features a strong connection to experiment.

Topics Include:
- Application of materials informatics approaches such as data mining, genetic algorithms, cluster expansions, and machine-learning for structures, properties, and processing
- Innovations that improve accuracy and efficiency of computational materials design
- Computational discovery and design of novel materials, such as 2D materials and materials for energy technologies
- Semi-empirical models of interatomic interaction

Abstracts Due 07/16/2018
Proceedings Plan Planned: Supplemental Proceedings volume

3D Reconstruction of Microstructure from Surface Images Using Graph Theoretic Approaches
A Machine Learning Approach for Process Optimization of Polycrystalline Materials
A Python-based Toolkit for Material Design
A Screening of Pt Alloys with P-block Elements and the DFT Study of Alloying Effect for Oxygen Reduction Reaction
A Statistical Dislocation-mediated Crystal Plasticity Model for Predicting Size Effects on the Yield Strength of Single and Polycrystalline Metals
Accelerating Hierarchical Materials Discovery and Design through a Combined Machine Learning and Experimental Framework
Building Microstructure Evolution Linkages for Sintering of Polycrystalline Ceramics
CALPHAD-guided Alloy Design and Processing of Novel Ceramics and Cermets in Titanium-Boron System
Computational Characterization Using the Local Spectroscopy Data Initiative (LSDI)
Computational Design of Non-precious Transition Metal/Nitrogen Doped Carbon as Effective Fuel Cell Electrocatalysts
Computational Discovery and Design of 2D Transition Metal Dichalcogenide Heterostructures
Correlate the Local Structural Characteristics with the Activation Energy of CuZr Metallic Glasses by Using Activation-relaxation Technique and Machine Learning Methods
Designer 2D Metals and Weyl Semimetals
Elastic Properties of Bulk and Low-dimensional Materials Using DFT with Van Der Waals Functional
Enhancement of Chemical Stability of Phosphorene and Heterostructures on Its Basis: Results of Ab-initio Modelling
Exploration of Interfacial Transitions by Correlating Atomic Scale Microscopy with Atomistic Simulations
eXtremeMAT: Computational Materials Discovery for Existing and Advanced FE Power Cycles
Goniopolarity of Thermal Transport Behavior in Layered 2D Materials
Grain Growth in Yttria-doped Alumina - A Simulation Study
Interpretable Machine Learning for Polycrystal Plasticity Micromechanics
Intrinsic Ductility of Alloys from Nonlinear Elasticity Theory
Learning from Correlations Based on Local Structure: Rare-earth Nickelates Revisited
M-12: Electric Properties of Isovalently Substituted Bi2O2Se: A Computational Study
Machine-learning Phase Prediction of High-entropy Alloys
Machine Learned Defect Level Prediction for Lead-based Hybrid Perovskites
Machine Learning Guided Accelerated Search for New Materials with Experimental Data
Materials Discovery under Electrochemical Conditions
Materials Informatics for Autonomous Materials Design
Microstructure Stabilization and the Herring Condition
Modeling Microstructural Evolution under Applied Magnetic Fields
Multi-objective Design of Functionally Graded Materials in Multicomponent Alloy Systems
New Spectral Graph Theoretic Metrics for Grain Boundary Network Design
Optimisation of Plasticity-induced Transformations and Strengthening in TRIP/TWIP Titanium Alloys
Optimizing Elastic Moduli of the Silicate Glasses through High-throughput Atomistic Modeling and Machine Learning Techniques
Phase-field Modeling of Stacked Dislocation Pile-ups in Face-centered Cubic Metals
Predicting Small-scale Plasticity in Single Crystal Micropillars via Machine Learning
Prediction of the Strength of FeNiCrCo High Entropy Alloy Single Crystals
Presence of Chern Insulating and Weyl Semimetallic Phase in Bi2MnSe4/Bi2Se3 Multilayer Heterostructures
Reduced-order Model for Microstructure Evolution Simulation in Solid Oxide Fuel Cell with Dynamic Discrepancy Reduced Modeling
Simulations and Experiments of Template-directed Eutectic Solidification to Design Self-organizing Optical Metamaterials
Structure and Properties of High-mobility MoTe2-x Phases
Superior Structural, Elastic and Electronic Properties of 2D Titanium Nitride MXenes Over Carbide MXenes: A Comprehensive First Principles Study
The Effects of β-stabilizers on ω-phase Formation and Elastic Properties in Titanium Alloys
The Representation of Five-parameter Grain Boundary Functions Using Harmonics
Towards an Autonomous Efficient Materials Discovery Framework: An Example of Optimal Experiment Design under Model Uncertainty
Tuning Martensitic Behavior Using Free Energy Landscape Engineering

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