Predicting Materials Properties with Little Data Using Shotgun Transfer Learning

[Image: see text] There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent...

Descripción completa

Detalles Bibliográficos
Autores principales: Yamada, Hironao, Liu, Chang, Wu, Stephen, Koyama, Yukinori, Ju, Shenghong, Shiomi, Junichiro, Morikawa, Junko, Yoshida, Ryo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2019
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813555/
https://www.ncbi.nlm.nih.gov/pubmed/31660440
http://dx.doi.org/10.1021/acscentsci.9b00804

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813555/
https://www.ncbi.nlm.nih.gov/pubmed/31660440
http://dx.doi.org/10.1021/acscentsci.9b00804

Ejemplares similares