Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
Computational material design often does not account for temperature effects. The present manuscript combines quantum-mechanics based calculations with a machine-learned correction to establish a unified thermodynamics framework for accurate prediction of high temperature reaction free energies in o...
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/4e7b0698a52f42319f93d8d5a7ab17e2 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Computational material design often does not account for temperature effects. The present manuscript combines quantum-mechanics based calculations with a machine-learned correction to establish a unified thermodynamics framework for accurate prediction of high temperature reaction free energies in oxides. |
|---|