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...

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Autores principales: Victor Fung, Guoxiang Hu, P. Ganesh, Bobby G. Sumpter
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/db4b29d6fa904989b00edcedcb18059e
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Sumario: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 part of the training.