Machine Learning for the edge energies of high symmetry Au nanoparticles (bibtex)

by Emmanouil Pervolarakis, Georgios A. Tritsaris, Phoebus Rosakis and Ioannis N. Remediakis

Reference:

Machine Learning for the edge energies of high symmetry Au nanoparticles (Emmanouil Pervolarakis, Georgios A. Tritsaris, Phoebus Rosakis and Ioannis N. Remediakis), In Surface Science, volume 732, 2023.

Bibtex Entry:

@article{doi:10.1016/j.susc.2023.122265,
	call={pr009},
	acronym={NANOCOMPDESIGN},
	fulltitle={Computational design of nanostructured materials },
	pi={Georgios Kopidakis},
	affiliation={University of Crete},
	researchfield={Chemical Sciences and Materials},
	title = {Machine Learning for the edge energies of high symmetry Au nanoparticles},
	journal = {Surface Science},
	volume = {732},
	pages = {122265},
	year = {2023},
	bibyear = {2023},
	issn = {0039-6028},
	doi = {https://doi.org/10.1016/j.susc.2023.122265},
	url = {https://www.sciencedirect.com/science/article/pii/S0039602823000195},
	author = {Emmanouil Pervolarakis and Georgios A. Tritsaris and Phoebus Rosakis and Ioannis N. Remediakis},
	keywords = {Edge energy, Nanoparticles, DFT, Machine learning},
}