|About this Abstract
||2019 TMS Annual Meeting & Exhibition
||Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
||Automated Sensitivity Analysis for High-throughput Ab Initio Calculations
||Jan Janssen, Tilmann Hickel, Joerg Neugebauer
|On-Site Speaker (Planned)
Over the last years methodological and computational progress in atomistic simulations have substantially improved the predictive power of materials design. To compare the simulation results, it is essential to quantify the various sources of uncertainty in ab initio calculation. Contributions include the systematic error of convergence, the statistical or numerical error, and the model error. Even the determination of the equilibrium lattice constant and bulk modulus requires a careful analysis of the fitting of energy-volume curves, going beyond the consideration of standard convergence parameters like cutoff and k-points. We therefore developed an algorithm which takes the precision in the derived quantity like the bulk modulus as a convergence goal and automatically determines the convergence parameter needed to achieve it. It is implemented using pyiron – http://pyiron.org. Our investigations revealed that commonly used rules of the thumb for fitting ground state materials properties become invalid for high precision calculations.
||Planned: Supplemental Proceedings volume