Automated optimization of convergence parameters in plane wave density functional theory calculations via a tensor decomposition-based uncertainty quantification

TL;DR

This paper introduces an automated method using tensor decomposition-based uncertainty quantification to optimize convergence parameters in plane wave density functional theory calculations, aiming for parameter-free high-throughput material simulations.

Contribution

It presents a novel, fully automated approach that predicts convergence parameters based on target accuracy, reducing manual tuning in density functional theory calculations.

Findings

Efficient representation of errors in convergence parameters.

Automated approach achieves target accuracy without manual parameter selection.

Validated on a large set of cubic fcc elements.

Abstract

First principles approaches have revolutionized our ability in using computers to predict, explore and design materials. A major advantage commonly associated with these approaches is that they are fully parameter free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations it becomes exceedingly important to achieve a truly parameter free approach. Utilizing uncertainty quantification (UQ) and tensor decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters. Based on this formalism we implement a fully automated approach that requires as input the target accuracy rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice.