Band unfolding with a general transformation matrix: from code implementation to interpretation of photoemission spectra
Oleg Rubel, Jean-Baptiste Moussy, Paul Foulquier, V\'eronique Brouet

TL;DR
This paper introduces an extended computational tool for band structure unfolding using arbitrary transformation matrices, enhancing interpretation of electronic structures and ARPES data in complex materials.
Contribution
It develops a generalized fold2Bloch package supporting arbitrary transformations, rotations, and various cell types, improving band unfolding capabilities beyond previous limitations.
Findings
Successfully unfolded Sr2IrO4 band structure and compared with ARPES data.
Highlighted importance of unfolding for interpreting SrIrO3 ARPES measurements.
Provided publicly available code and utilities for broader use.
Abstract
Unfolding of a supercell band structure into a primitive Brillouin zone is important for understanding implications of structural distortions, disorder, defects, solid solutions on materials electronic structure. Necessity of the band unfolding is also recognised in interpretation of angle-resolved photoemission spectroscopy (ARPES) measurements. We describe an extension of the fold2Bloch package by implementing an arbitrary transformation matrix used to establish a relation between primitive cell and supercell. This development allows us to overcome limitations of supercells constructed exclusively by scaling of primitive cell lattice vectors. It becomes possible to transform between primitive and conventional cells as well as include rotations. The fold2Bloch is publicaly available from a GitHub repository as a FORTRAN code. It interfaces with the all-electron full-potential WIEN2k…
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
TopicsCatalysis and Oxidation Reactions · Electronic and Structural Properties of Oxides · Machine Learning in Materials Science
