APA (7th ed.) Citation

Tomacruz, J. G. T., Joey D. Ocon, & Padama, A. A. B. (2022). Machine Learning-accelerated Density Functional Theory (ML-DFT) screening of bimetallic transition metal surfaces based on single-atom adsorption energy predictions. College of Engineering, University of the Philippines Diliman.

Chicago Style (17th ed.) Citation

Tomacruz, Jan Goran T., Joey D. Ocon, and Allan Abraham B. Padama. Machine Learning-accelerated Density Functional Theory (ML-DFT) Screening of Bimetallic Transition Metal Surfaces Based on Single-atom Adsorption Energy Predictions. Quezon City: College of Engineering, University of the Philippines Diliman, 2022.

MLA (9th ed.) Citation

Tomacruz, Jan Goran T., et al. Machine Learning-accelerated Density Functional Theory (ML-DFT) Screening of Bimetallic Transition Metal Surfaces Based on Single-atom Adsorption Energy Predictions. College of Engineering, University of the Philippines Diliman, 2022.