Machine Learning-accelerated Density Functional Theory (ML-DFT) screening of bimetallic transition metal surfaces based on single-atom adsorption energy predictions

Machine Learning-accelerated Density Functional Theory (ML-DFT) screening of bimetallic transition metal surfaces based on single-atom adsorption energy predictions

Detalhes bibliográficos
Autor principal: Tomacruz, Jan Goran T. (Autor)
Outros Autores: Joey D. Ocon (adviser.), Padama, Allan Abraham B. (adviser.)
Formato: Tese
Idioma:English
Publicado em: Quezon City College of Engineering, University of the Philippines Diliman 2022.
Assuntos:
Density functionals.
Machine learning.
Acesso em linha:Abstract