Identifying domains of applicability of machine learning models for materials science
oleh: Christopher Sutton, Mario Boley, Luca M. Ghiringhelli, Matthias Rupp, Jilles Vreeken, Matthias Scheffler
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2020-09-01 |
Deskripsi
Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides.