Cargando…
Bias free multiobjective active learning for materials design and discovery
The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work,...
Autores principales: | Jablonka, Kevin Maik, Jothiappan, Giriprasad Melpatti, Wang, Shefang, Smit, Berend, Yoo, Brian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055971/ https://www.ncbi.nlm.nih.gov/pubmed/33875649 http://dx.doi.org/10.1038/s41467-021-22437-0 |
Ejemplares similares
-
The
Role of Machine Learning in the Understanding
and Design of Materials
por: Moosavi, Seyed Mohamad, et al.
Publicado: (2020) -
Big-Data Science
in Porous Materials: Materials Genomics
and Machine Learning
por: Jablonka, Kevin Maik, et al.
Publicado: (2020) -
Applicability of Tail Corrections in the Molecular
Simulations of Porous Materials
por: Jablonka, Kevin Maik, et al.
Publicado: (2019) -
An Ecosystem for Digital Reticular Chemistry
por: Jablonka, Kevin Maik, et al.
Publicado: (2023) -
Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials
por: Lo Dico, Giulia, et al.
Publicado: (2021)