Agents for sequential learning using multiple-fidelity data

Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In r...

Descripción completa

Detalles Bibliográficos
Autores principales: Palizhati, Aini, Torrisi, Steven B., Aykol, Muratahan, Suram, Santosh K., Hummelshøj, Jens S., Montoya, Joseph H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933401/
https://www.ncbi.nlm.nih.gov/pubmed/35304496
http://dx.doi.org/10.1038/s41598-022-08413-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933401/
https://www.ncbi.nlm.nih.gov/pubmed/35304496
http://dx.doi.org/10.1038/s41598-022-08413-8

Ejemplares similares