Interpretable machine learning for materials design

被引：0
|
作者：
James Dean
Matthias Scheffler
Thomas A. R. Purcell
Sergey V. Barabash
Rahul Bhowmik
Timur Bazhirov
机构：
[1] Exabyte Inc.,
[2] University of California Santa Barbara,undefined
[3] The NOMAD Laboratory at the Fritz Haber Institute,undefined
[4] Intermolecular Inc.,undefined
[5] Polaron Analytics,undefined
来源：
Journal of Materials Research | 2023年 / 38卷
关键词：
Keywords; Machine learning; Materials science; Chemistry; Interpretability; Rational design;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：4477 / 4496
页数：19
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