Cargando…
Rapid discovery of stable materials by coordinate-free coarse graining
A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships, allowing the stability and functional properties of materials to...
Autores principales: | Goodall, Rhys E. A., Parackal, Abhijith S., Faber, Felix A., Armiento, Rickard, Lee, Alpha A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328671/ https://www.ncbi.nlm.nih.gov/pubmed/35895811 http://dx.doi.org/10.1126/sciadv.abn4117 |
Ejemplares similares
-
Crystal graph attention networks for the prediction of stable materials
por: Schmidt, Jonathan, et al.
Publicado: (2021) -
Predicting materials properties without crystal structure: deep representation learning from stoichiometry
por: Goodall, Rhys E. A., et al.
Publicado: (2020) -
In situ neutron diffraction for analysing complex coarse-grained functional materials
por: Hinterstein, Manuel, et al.
Publicado: (2023) -
Multiconfigurational Coarse-Grained Molecular Dynamics
por: Sharp, Morris E., et al.
Publicado: (2019) -
The power of coarse graining in biomolecular simulations
por: Ingólfsson, Helgi I, et al.
Publicado: (2014)