Flexible and Efficient Semi-Empirical DFTB Parameters for Electronic Structure Prediction of 3D, 2D Iodide Perovskites and Heterostructures
Junke Jiang, Tammo van der Heide, Simon Th\'ebaud, Carlos Ra\'ul, Lien-Medrano, Arnaud Fihey, Laurent Pedesseau, Claudio Quarti, Marios, Zacharias, George Volonakis, Mikael Kepenekian, B\'alint Aradi, Michael A., Sentef, Jacky Even, Claudine Katan

TL;DR
This paper develops tailored DFTB parameters to accurately predict electronic properties of 3D and 2D iodide perovskites and heterostructures, enabling large-scale simulations with reduced computational cost.
Contribution
The authors introduce semi-empirical DFTB parameters specifically optimized for lead-iodide perovskites, improving accuracy in band gap and effective mass calculations compared to previous methods.
Findings
DFTB parameters accurately predict band gaps and effective masses.
Electronic band structures of defective perovskites are explored.
Efficient computation of band alignments in heterostructures is demonstrated.
Abstract
Density Functional Tight-Binding (DFTB), an approximative approach derived from Density Functional Theory (DFT), has the potential to pave the way for simulations of large periodic or non-periodic systems. We have specifically tailored DFTB parameters to enhance the accuracy of electronic band gap calculations in both 3D and 2D lead-iodide perovskites, at a significantly reduced computational cost relative to state-of-the-art ab initio calculations. Our electronic DFTB parameters allow computing not only the band gap but also effective masses of perovskite materials with reasonable accuracy compared to existing experimental data and state-of-the-art DFT calculations. The electronic band structures of vacancy-ordered and, lead- and iodide- deficient perovskites are also explored. Additionally, we demonstrate the efficiency of DFTB in computing electronic band alignments in perovskite…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPerovskite Materials and Applications · Advanced Thermoelectric Materials and Devices · Machine Learning in Materials Science
