ProgramMaster Logo
Conference Tools for 2018 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2018 TMS Annual Meeting & Exhibition
Symposium Computational Method and Experimental Approaches for Model Development and Validation, Uncertainty Quantification, and Stochastic Predictions
Presentation Title Utilizing Error in First-principle Lattice Constants to Discover Novel Low-dimensional Materials
Author(s) Kamal Choudhary, Francesca Tavazza
On-Site Speaker (Planned) Kamal Choudhary
Abstract Scope Density functional theory using GGA-PBE is known to predict incorrect lattice constant for Vanderwaal bonded structures. DFT results from materials project have been utilized to find materials. Specifically, if the relative difference between the lattice constants from ICSD and DFT-PBE for a specific material is greater than or equal to 5%, we predict them to be good candidates for 2D materials. We have predicted at least 1356 such 2D materials. We manually create single layer systems and calculate their energetics, structural, electronic, and elastic properties. Currently the database consists of 1012 bulk and 430 single layer materials, of which 371 systems are common to bulk and single layer. The rest of calculations are underway. We also calculated the exfoliation energy of the suggested layered materials, and we found that in 88.9% of the cases the currently accepted criterion for exfoliation was satisfied. The database is publicly available at www.ctcms.nist.gov/~knc6/JVASP.html
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

Questions about ProgramMaster? Contact programming@programmaster.org