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

Bibliografske podrobnosti
Glavni avtor: Tomacruz, Jan Goran T. (Author)
Drugi avtorji: Joey D. Ocon (adviser.), Padama, Allan Abraham B. (adviser.)
Format: Thesis
Jezik:English
Izdano: Quezon City College of Engineering, University of the Philippines Diliman 2022.
Teme:
Density functionals.
Machine learning.
Online dostop:Abstract