A framework for quantifying uncertainty in DFT energy corrections
Abstract In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other en...
Guardado en:
Autores principales: | Amanda Wang, Ryan Kingsbury, Matthew McDermott, Matthew Horton, Anubhav Jain, Shyue Ping Ong, Shyam Dwaraknath, Kristin A. Persson |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/01cbcca218d344ddb52db613c074fb03 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis
por: Matthew J. McDermott, et al.
Publicado: (2021) -
Database of ab initio L-edge X-ray absorption near edge structure
por: Yiming Chen, et al.
Publicado: (2021) -
Quantifying the impact of uncertainty on threat management for biodiversity
por: Sam Nicol, et al.
Publicado: (2019) -
A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
por: Qi Wang, et al.
Publicado: (2019) -
Quantifying the unknown impact of segmentation uncertainty on image-based simulations
por: Michael C. Krygier, et al.
Publicado: (2021)