Automated Workflow for Non-Empirical Wannier-Localized Optimal Tuning of Range-Separated Hybrid Functionals
Stephen E. Gant (1), Francesco Ricci (2,3,4), Guy Ohad (5), Ashwin Ramasubramaniam (6,7), Leeor Kronik (5), Jeffrey B. Neaton (1,2,8) ((1) Department of Physics, University of California Berkeley, Berkeley CA, United States, (2) Materials Sciences Division

TL;DR
This paper presents an automated, efficient workflow for non-empirical tuning of range-separated hybrid functionals using Wannier localization, significantly improving the accuracy and applicability of electronic property calculations for semiconductors and insulators.
Contribution
The authors develop a fully automated workflow for non-empirical tuning of WOT-SRSH functionals, reducing manual effort and computational cost compared to previous methods.
Findings
Achieves high accuracy in band gap and optical spectra predictions
Automates the tuning process with minimal user input
Validated on 23 semiconductors and insulators
Abstract
We introduce an automated workflow for generating non-empirical Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH) functionals. WOT-SRSH functionals have been shown to yield highly accurate fundamental band gaps, band structures, and optical spectra for bulk and 2D semiconductors and insulators. Our workflow automatically and efficiently determines the WOT-SRSH functional parameters for a given crystal structure and composition, approximately enforcing the correct screened long-range Coulomb interaction and an ionization potential ansatz. In contrast to previous manual tuning approaches, our tuning procedure relies on a new search algorithm that only requires a few hybrid functional calculations with minimal user input. We demonstrate our workflow on 23 previously studied semiconductors and insulators, reporting the same high level of accuracy. By automating…
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Taxonomy
Topics2D Materials and Applications · Machine Learning in Materials Science · Electronic and Structural Properties of Oxides
