Topological representation of layered hybrid lead halides for machine learning using universal clusters
Prediction of band gaps in layered hybrid halide compounds promising for photovoltaic and optoelectronic applications was performed using a machine learning approach. In order to facilitate the discovery and design of new hybrid halide materials with tailored electronic properties, machine learning models were enhanced with invariant topological representations of these materials using the atom-specific persistent homology method.
Authors
- Екатерина Марченко (ORCID: https://orcid.org/0000-0003-4615-5270)
- А. В. Тарасов (ORCID: https://orcid.org/0009-0007-9109-619X)
- Eugene A. Goodilin (ORCID: https://orcid.org/0000-0001-7322-525X)
- Maria G. Khrenova (ORCID: https://orcid.org/0000-0001-7117-3089)
- Vadim Korolev (ORCID: https://orcid.org/0000-0001-6117-5662)
Institutions
- Lomonosov Moscow State University (RU)
Publication Details
- Journal
- Mendeleev Communications
- Published
- 2025-01-01
- DOI
- https://doi.org/10.71267/mencom.7653
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52