Machine learning based energy-free structure predictions of molecules, transition states, and solids

Accurate computational prediction of atomistic structure with traditional methods is challenging. The authors report a kernel-based machine learning model capable of reconstructing 3D atomic coordinates from predicted interatomic distances across a variety of system classes.

Guardado en:
Detalles Bibliográficos
Autores principales: Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Q
Acceso en línea:https://doaj.org/article/63dbdfe92c2f4710af3a11c94b9cdfc4
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!