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Meeting 2018 TMS Annual Meeting & Exhibition
Symposium Computational Method and Experimental Approaches for Model Development and Validation, Uncertainty Quantification, and Stochastic Predictions
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Francesca M Tavazza, National Institute of Standards and Technology
Mark Tschopp, Army Research Laboratory
Richard G. Hennig, University of Florida
Avinash M. Dongare, University of Connecticut
Shawn P. Coleman, U.S. Army Research Laboratory
Niaz Abdolrahim, University of Rochester
Joseph Bishop, Sandia National Laboratories
Fadi Abdeljawad, Sandia National Laboratories
Li Ma, National Institute of Standards and Technology
Scope Experimental measurements exhibit uncertainties that are described by their precision and accuracy. The same holds true for computational results, as all models behind simulation methodologies have limitations. Traditionally, results from computational approaches have been reported without error bars. However, to be interpreted correctly, such results at any length scale need a careful evaluation of their uncertainties. Furthermore, a way to evaluate the predictability of simulation techniques is to validate their findings using other, experimental or computational, approaches.

This symposium will focus on stochastic methods, computational methodology development, and validation, as well as uncertainty evaluation for experimental and computational approaches at various length scale. The goal of the symposium is to cover these research topics in an interdisciplinary approach, which connects theory and experiment, with a view towards materials applications.
One of this year focus will be the development and validation of interatomic potentials for materials research and design, as advances in classical molecular dynamics and Monte Carlo Simulations are now paving the way for the rational design and development of materials. The advances in the predictive capability of these modeling methods are attributed to the increases in computational resources and advanced algorithms that have enabled the continued development of interatomic potentials to model complex interactions (metallic, covalent, ionic, van der Waals, or mixed) and phenomena at the atomic scales. This symposium brings together materials scientists and engineers, physicists, chemists, biologists, mathematicians, and data management experts, discuss the current state of the capabilities of interatomic potentials and their role in accelerated materials design for metals and metallic alloys.

There are 5 sessions planned covering: (1) validation and uncertainty evaluation for quantum-mechanical approaches, (2) interatomic potential development, (3) interatomic potential validation and uncertainty evaluation, (4) validation and uncertainty evaluation for finite element and multiscale modeling (effect of chosen constitutive equations, meshing, element types, coupling methods etc.), (5) advancements in stochastic methodologies (for material discovery)

Topics addressed in this symposium will include (but not be limited to):

- Evaluation of DFT exchange-correlation functional and pseudopotentials
- Development, validation and uncertainty quantification for interatomic potentials:
- Metals and Metallic alloys
- Reactive material systems
- Molecular, biomolecular and polymer systems
- Semiconductors and 2D materials
- Ceramics and ceramic composites
- Complex interfaces
- Phase transformation behavior at high temperatures and pressures
- Neural network and genetic algorithm approaches
- Advances in optimization algorithms and materials informatics
- Data mining
- Verification, validation, and uncertainty quantification for meso- and continuum scale modeling
Abstracts Due 07/16/2017
Proceedings Plan Planned: Supplemental Proceedings volume

Bayesian Linear Regression and Kriging Methods for Uncertainty Quantification in Process-structure-property Linkages of Low Carbon Steels and Superalloys
Benchmarking Density Functional Theory Based Methods to Predict Optical and Electronics Properties of 2H-TaX2 (X=S, Se)
Calibration of a Titanium Modified Embedded Atom Method Potential to High Temperature Behavior
Correlations of Numerical Precision in Material Properties Derived from Density Functional Theory
Development of a Semi-empirical Potential for Simulation of Ni Solutes Segregated in Ag Grain Boundaries
Dynamic Failure of High Energy Materials: Uncertainty Quantification and Stochastic Predictions
Errors of Molecular Dynamics Simulations, and Development of “Accurate” Analytical Bond Order Potentials for Al-Cu-H and Mg-H Systems
Extending the Angular-embedded Atom Method (A-EAM) Framework to an Al-Mg-Si Ternary System
Extending the Reach of DFT to Molecular Simulations Using Neural Networks
It's a SNAP: Automated Generation of High-accuracy Interatomic Potentials Using Quantum Data
Large Scale Sensitivity of Uncertain Parameters on Optimal Control Solutions: An Example in Additive Manufacturing
Lattice Thermal Conductivity: Uncertainty Quantification in First Principles Predictions and Experimental Validation
Linear Scaling, Quantum-accurate Interatomic Potentials with SNAP; Reaching those Hard-to-reach Places in Classical Molecular Dynamics
Machine Learning Based Atomistic Force Fields
Machine Learning Methods for Interatomic Potentials: Application to Boron Carbide
Machine Learnt Interatomic Potentials for Stanene and Germanene to Study Thermal Conductivity and Growth
New Advances in Semi-empirical Interatomic Potentials - the Modified Embedded Atom Method (MEAM)
Overcoming Singularities within Rate-independent Crystal Plasticity to Enable Realistic Latent Hardening
Parametrically Homogenized Models of Deformation and Failure of Metals and Alloys with Uncertainty-quantification
Property Localization: Quantifying the Uncertainty of Inferred Constitutive Models for Grain Boundaries
The Current State of Phase Field Benchmark Problems Developed by CHiMaD/NIST
The OpenKIM Testing Framework for Interatomic Potentials
The Role of Data Analysis in Uncertainty Quantification: Examples from Materials Science
Uncertainty Quantification for Additive Manufacturing Applications across Scales
Uncertainty Quantification for Solute Transport Modeling
Uncertainty Quantification in Materials Strength Models Using Bayesian Inference
Uncertainty Quantification of the Effect of Charge Noise on Silicon Quantum Dots
Utilizing Error in First-principle Lattice Constants to Discover Novel Low-dimensional Materials

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