Cargando…
Physically inspired deep learning of molecular excitations and photoemission spectra
Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-principles methods. Consequently, a targeted design of materials with tailored optoelectronic properties by high-throughput s...
Autores principales: | Westermayr, Julia, Maurer, Reinhard J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372319/ https://www.ncbi.nlm.nih.gov/pubmed/34447563 http://dx.doi.org/10.1039/d1sc01542g |
Ejemplares similares
-
Photoemission Spectra from the Extended Koopman’s Theorem, Revisited
por: Di Sabatino, S., et al.
Publicado: (2021) -
Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic–inorganic interfaces
por: Westermayr, Julia, et al.
Publicado: (2022) -
Characterizing
Molecule–Metal Surface Chemistry
with Ab Initio Simulation of X-ray Absorption and Photoemission
Spectra
por: Hall, Samuel J., et al.
Publicado: (2023) -
Machine Learning for Electronically Excited States
of Molecules
por: Westermayr, Julia, et al.
Publicado: (2020) -
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
por: Ghosh, Kunal, et al.
Publicado: (2019)