High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory
Abstract We introduce a simple criterion to identify two-dimensional (2D) materials based on the comparison between experimental lattice constants and lattice constants mainly obtained from Materials-Project (MP) density functional theory (DFT) calculation repository. Specifically, if the relative d...
Enregistré dans:
Auteurs principaux: | Kamal Choudhary, Irina Kalish, Ryan Beams, Francesca Tavazza |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2017
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/cf7ceb3464a94a8cad0d53c3123d99c3 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Database of Wannier tight-binding Hamiltonians using high-throughput density functional theory
par: Kevin F. Garrity, et autres
Publié: (2021) -
Nonlinear dynamic characterization of two-dimensional materials
par: D. Davidovikj, et autres
Publié: (2017) -
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
par: Dipendra Jha, et autres
Publié: (2019) -
Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
par: Vishu Gupta, et autres
Publié: (2021) -
Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
par: Dipendra Jha, et autres
Publié: (2020)