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

Bibliografiske detaljer
Hovedforfatter: Tomacruz, Jan Goran T. (Author)
Andre forfattere: Joey D. Ocon (adviser.), Padama, Allan Abraham B. (adviser.)
Format: Thesis
Sprog:English
Udgivet: Quezon City College of Engineering, University of the Philippines Diliman 2022.
Fag:
Density functionals.
Machine learning.
Online adgang:Abstract