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

Dades bibliogràfiques
Autor principal: Tomacruz, Jan Goran T. (Autor)
Altres autors: Joey D. Ocon (adviser.), Padama, Allan Abraham B. (adviser.)
Format: Thesis
Idioma:English
Publicat: Quezon City College of Engineering, University of the Philippines Diliman 2022.
Matèries:
Density functionals.
Machine learning.
Accés en línia:Abstract