A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k-nary compounds of diverse chemistries and structures....

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Detalles Bibliográficos
Autores principales: de Jong, Maarten, Chen, Wei, Notestine, Randy, Persson, Kristin, Ceder, Gerbrand, Jain, Anubhav, Asta, Mark, Gamst, Anthony
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5046120/
https://www.ncbi.nlm.nih.gov/pubmed/27694824
http://dx.doi.org/10.1038/srep34256

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5046120/
https://www.ncbi.nlm.nih.gov/pubmed/27694824
http://dx.doi.org/10.1038/srep34256

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