Austin Zadoks^1*, Antimo Marrazzo^2,3*, Nicola Marzari^1,4,5*

1 Theory and Simulation of Materials (THEOS), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

2 Scuola Internazionale Superiore di Studi Avanzati (SISSA), I-34136 Trieste, Italy

3 Dipartimento di Fisica, Università di Trieste, I-34151 Trieste, Italy

4 National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

5 Laboratory for Materials Simulations (LMS), Paul Scherrer Institut, CH-5232 Villigen, Switzerland

* Corresponding authors emails: austin.zadoks@epfl.ch, amarrazz@sissa.it, nicola.marzari@epfl.ch

DOI10.24435/materialscloud:vm-5n [version v1]

Publication date: Aug 26, 2024

How to cite this record

Austin Zadoks, Antimo Marrazzo, Nicola Marzari, Spectral operator representations, Materials Cloud Archive 2024.128 (2024), https://doi.org/10.24435/materialscloud:vm-5n

Description

Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an alternative framework for representing materials using descriptions of their electronic structure called Spectral Operator Representations (SOREPs). This record contains the code and data used to study carbon nanotubes (CNTs), barium titanate polymorphs, and the accelerated screening of transparent conducting materials with SOREPs. A data set for each application is provided: p_z tight binding band structures for the three CNT configurations studied; the structures, band dispersions, and SOREP features of 127 BaTiO₃ polymorphs; and the SOREP features and ML targets for the MC3D materials considered in the accelerated screening. Additionally, code including patch files for Quantum ESPRESSO, the "sorep" python package, and the set of scripts used to prepare these data, train ML models, and plot results is provided.

Materials Cloud sections using this data

Files

File name	Size	Description
README.md MD5md5:2f5c03ed52fa5e7639d2b84e937478c1	446 Bytes	Readme file
sorep_code_data.tar.gz MD5md5:b66184de43c241f077820c9e2b93b629	342.3 MiB	Code and data archive

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine learning electronic structure electronic bands EPFL MARVEL