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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
Presentation Title Benchmarking Density Functional Theory Based Methods to Predict Optical and Electronics Properties of 2H-TaX2 (X=S, Se)
Author(s) Sugata Chowdhury, Kamal Choudhary, Angela R. Hight Walker, Francesca Tavazza
On-Site Speaker (Planned) Sugata Chowdhury
Abstract Scope Tantalum diselenide (2H-TaSe2) are electronically interesting materials because they exhibit several phase transitions due to a delicate balance among competing electronic ground states. At low temperature it changes to the charge density waves states by breaking translational symmetry. While Density Functional Theory (DFT) has a very important role in predicting properties and structures for this material. The choice of exchange-correlation functional and pseudopotential may affect its results. In this work, we compared results obtained using six popular density functionals (PBE, LDA, PW, PW91, PBESol, OptB88) and three pseudopotential (PAW, ultrasoft, and normconserving). To assess the performance of functional and pseudopotential, we compared our results to experimental values. We found that OptB88, and PBESol are good to predict the electronic structure and formation energy, and that the normconserving pseudopotentials and LDA are well reproduce experimental optical properties of 2H-TaX2. We will also discuss the relation between optical properties and convergence parameters.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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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