|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Computational Methods and Experimental Approaches for Uncertainty Quantification and Propagation, Model Validation, and Stochastic Predictions
||Uncertainty Quantification in Density Functional Theory: Non-intrusive vs. Intrusive Methodologies
||David Mebane, Wilfredo Ibarra-Hernandez, Aldo Humberto Romero
|On-Site Speaker (Planned)
Density functional theory (DFT) makes computational materials science into a practical tool for materials discovery. This contribution will examine the question of the best method to quantify “model form” error in DFT due to the functional form of the exchange correlation energy – that is, error due to its inherent physical approximations. Model-based Bayesian calibration in both intrusive and non-intrusive modes will be discussed. Intrusive methods locate the uncertainty within the DFT model itself, leading to stochastic predictions across chemistries and structures, but are challenging to implement. Recent results that speak to the feasibility of intrusive UQ for DFT will be presented.