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About this Symposium
Meeting 2015 TMS Annual Meeting & Exhibition
Symposium Computational Modeling and Stochastic Methods for Materials Discovery and Properties
Sponsorship TMS Functional Materials Division (formerly EMPMD)
TMS Materials Processing and Manufacturing Division
TMS Structural Materials Division
TMS: Chemistry and Physics of Materials Committee
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Richard G. Hennig, University of Florida
Francesca M Tavazza, National Institute of Standards and Technology
Dallas R. Trinkle, University of Illinois at Urbana-Champaign
Mikhail I. Mendelev, Ames Laboratory
Adri C. T. van Duin, Pennsylvania State University
Scope Advances in theoretical understanding, algorithms and computational power are enabling computational tools to play an increasing role in materials discovery, development and optimization. For example, recently developed data mining techniques, genetic algorithms, machine-learning approaches, and predictive empirical potentials enable the “virtual synthesis” of novel materials, with their properties being predicted on a computer before ever being synthesized in a laboratory. Stochastic computational techniques and data analysis methods play an increasing role in materials characterization, design, and optimization. Large-scale computations for complex materials, that are needed to guide and complement novel experiments benefit from reliable empirical energy models.

This symposium will cover recent applications and methodological developments at the frontier of computational materials science, ranging from quantum-level prediction to macro-scale property optimization, to stochastic methods for materials optimization and analysis. 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:
• First principles materials discovery
• Optimization algorithm to search the structure-composition design space
• Data mining techniques, genetic algorithms, neural networks, cluster expansions, and machine-learning algorithms for structures, properties, and processing
• Bayesian statistics based systems analysis
• Development of empirical and semi-empirical energy models
• Innovations that improve accuracy and efficiency of computational materials design

Planned sessions:
• Stochastic methods in materials discovery and characterization
• Optimization, validation, and application of empirical potentials
• Computational methods and applications for materials discovery
• Computational modeling for materials characterization and design
Abstracts Due 07/15/2014
Proceedings Plan Planned: A print-only volume
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Stochastic Approach for Predicting the Mechanical Properties of Graphene
A Variable Charge Reactive Potential for Cyanogens for Organic-Metal Nitrides Interactions
Ab Initio Prediction of the Material with Highest Known Melting Point
Accelerating Materials Discovery with Machine Learning
An Ab-Initio Investigation of Water Dissociation on Two-Dimensional MoS2 Edges
An Interatomic Potential for Ionic+Covalent+Metallic Materials Based on the Modified Embedded-Atom Method
Application and Validation of Inter-Atomic Potentials for Modeling Helium-3 Bubble Growth in Aging Palladium Tritides
Application of Concepts from Modeling Integrated Computing for the Design of Soft Materials
Atomic Scale Investigation of Ni3AlX Alloys Using a Combined First-Principles and Statistical Learning Approach
Atomistic Modeling Study of the Role of Oxygen Interstitials in the Behavior of Titanium Alloys
Atomistic Simulation and Virtual Diffraction Characterization of Stable and Metastable Alumina Surfaces
Atoms-to-Continuum Simulation of the Rapid Solidification of Metallic Liquids
Cantilever Box-Beam Application of Composite Stacking Sequence Optimization Using Adaptive Genetic Algorithm
Computational Database for Elastic Properties of Materials
Computational Design of Earth-Abundant Thermoelectrics
Computational Design of Nanosegregated Pt Alloy Catalysts
Computational Discovery of Novel Two-Dimensional Materials with an Evolutionary Algorithm
Computational Materials Design via the Inductive Design Exploration Method (IDEM)
Computational Modeling of Structural and Dynamic Properties of Al-Li-Zn and Al-Li-Cu Alloys
Computational Nanomechanics of Single-Chain Molecular Bond Rupture in Hydrocarbon-Based Polymers Using Modified Embedded-Atom Method Potential
Computationally Efficient Method to Generate Multi-Component EAM Potentials
Computer Simulation of Martensite Spread: A Stochastic Approach
Conductivity of Metal Alloys Based on First Principles Calculations
Defect reduced GaN heterostructures
Developing Multiscale Models to Understand the Mechanics of Transition Metal Carbides
Development of a New Angular-Dependent Interatomic Potential for the Cu-Ta System and Applications to the Design of Immiscible Nano-Crystalline Alloys
Development of Semi-Empirical Potentials Suitable for Simulation of Solidification in Al-Sm Alloys
Effect of Interwall Interaction, Doping and Defects on the Electronic Structure of DWCNTs
Effects of Grain Size on the Martensitic Phase Transformation of Nano-Polycrystalline NiAl Shape Memory Alloys via Cooling or Strain
First-Principles Study of Thermionic Emission from Os-Coated Tungsten Cathodes
G10: Quantitative Atomistic Modeling of Metals at Melting Point Using Phase-Field Crystals
G2: Computational Modeling Studies of the Minerals Sulphides with the Pentlandite Structure: Derivation of the Potential Models
G3: Cross-Sectional Size and Interface Roughness Effects on Thermal Conductivity of Ge/Si Core/Shell Nanowires
G4: Effect of Pore Characteristics on Elastic Modulus of Porous Titanium by Numerical Simulation
G6: Indentation of Zirconium and Zirconia by Atomistic Simulation
G7: Modelling Rhizophora Mangle L Bark-Extract Effects on Concrete Steel-Rebar in 0.5 M H2SO4: Implications on Concentration for Effective Corrosion-Inhibition
G8: Numerical Analysis for Thermal Stress of Side Wall with Composite Structure on Twin Roll Strip Casting
G9: Numerical Simulation for the Mixing Process of Converter with Preheating Oxygen
Global Optimization Improves Molecular Packing, Molecular Properties and Reactive Force Fields
Grain Network Representation of Microstructure: Predicting Rare Microstructural Events
Investigations of Early Stages of Nanoindentation through Combined DFT, MD and Hybrid MD/FEM Simulations
Kinetic Monte Carlo Enabled Modeling of Diffusion Assisted Plastic Deformation
Microstructural Optimization of High Temperature Ni-Cr ODS Alloy Using Genetic Algorithm
Modeling Chemical Fluctuations Across Stacking Faults in L12-Containing Co-base Superalloys Using Cluster-Assisted Statistical Mechanics
Modeling Stress Corrosion Cracking in Metals and Alloys
Molecular Dynamics Study of the effect of Number of Walls and Temperature on Cohesive Zone Properties of Multi-Walled Carbon Nanotubes/Copper Interface
Molecular Simulation of Ultra-Fast Resistance Switching in Electrometallization Cells: Optimizing Geometry and Processing Conditions
Monte Carlo Methods for Free Energy Calculations
Multi-Objectives Computational Design of Nickel-Based Superalloys
Novel Fast Cluster Expansion Method for Arbitrary Finite and Infinite Geometries
Obtaining a Bimodal Grain Size Distribution via Thermal Treatment for Property Optimization
Parameter Estimation in Mechanistic Tool Wear Model: A Bayesian Approach
Predicting Lithium and Electron Transport in Solid Electrolyte Interphases in Li-Ion Batteries
Predictive Simulations of Amorphous Polymers: Processing and Ultimate Thermo-Mechanical Properties
Quantifying Experimental Characterization Choices in Optimal Learning and Materials Design
ReaxFF Molecular Dynamics Simulation on Oxidation Behaviors of 3C-SiC: Uniaxial Strain Effect
Response Embedded Atom Method of Interatomic Potentials
Thermally-Activated Non-Schmid Glide of Screw Dislocations in W Using Atomistically-Informed Kinetic Monte Carlo Simulations
Towards a Fully Automated Framework for Generation and Optimization of Empirical Potentials
Unraveling Catalytic Mechanism of Co3O4 for Oxygen Evolution Reaction in Li-O2 Battery


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