Machine learned features from density of states for accurate adsorption energy prediction
Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as par...
Guardado en:
Autores principales: | Victor Fung, Guoxiang Hu, P. Ganesh, Bobby G. Sumpter |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/db4b29d6fa904989b00edcedcb18059e |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Machine learning accurate exchange and correlation functionals of the electronic density
por: Sebastian Dick, et al.
Publicado: (2020) -
Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy
por: Ayana Ghosh, et al.
Publicado: (2021) -
Component Parts of Bacteriophage Virions Accurately Defined by a Machine-Learning Approach Built on Evolutionary Features
por: Tze Y. Thung, et al.
Publicado: (2021) -
Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning
por: Zhe Zhang, et al.
Publicado: (2021) -
Machine learning based energy-free structure predictions of molecules, transition states, and solids
por: Dominik Lemm, et al.
Publicado: (2021)