Cargando…
A framework for quantifying uncertainty in DFT energy corrections
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 energy-deri...
Autores principales: | Wang, Amanda, Kingsbury, Ryan, McDermott, Matthew, Horton, Matthew, Jain, Anubhav, Ong, Shyue Ping, Dwaraknath, Shyam, Persson, Kristin A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322326/ https://www.ncbi.nlm.nih.gov/pubmed/34326361 http://dx.doi.org/10.1038/s41598-021-94550-5 |
Ejemplares similares
-
A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis
por: McDermott, Matthew J., et al.
Publicado: (2021) -
Database of ab initio L-edge X-ray absorption near edge structure
por: Chen, Yiming, et al.
Publicado: (2021) -
A representation-independent electronic charge density database for crystalline materials
por: Shen, Jimmy-Xuan, et al.
Publicado: (2022) -
High-throughput computation and evaluation of raman spectra
por: Liang, Qiaohao, et al.
Publicado: (2019) -
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
por: McDermott, Patrick L., et al.
Publicado: (2019)