Machine learning and data science in materials design: a themed collection

被引：4
|
作者：
Ferguson, Andrew [1 ,2 ,3 ]
Hachmann, Johannes [4 ,5 ,6 ]
机构：
[1] Univ Illinois, Dept Mat Sci & Engn, 1304 West Green St, Urbana, IL 61801 USA
[2] Univ Illinois, Dept Chem & Biomol Engn, 600 South Mathews Ave, Urbana, IL 61801 USA
[3] Univ Illinois, Dept Phys, 1110 West Green St, Urbana, IL 61801 USA
[4] SUNY Buffalo, Dept Chem & Biol Engn, Buffalo, NY 14260 USA
[5] New York State Ctr Excellence Mat Informat, Buffalo, NY 14203 USA
[6] SUNY Buffalo, Computat & Data Enabled Sci & Engn Grad Program, Buffalo, NY 14260 USA
来源：
MOLECULAR SYSTEMS DESIGN & ENGINEERING | 2018年 / 3卷 / 03期
关键词：
D O I：
10.1039/c8me90007h
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Guest Editors Andrew Ferguson and Johannes Hachmann introduce this themed collection of papers showcasing the latest research leveraging data science and machine learning approaches to guide the understanding and design of hard, soft, and biological materials with tailored properties, function and behaviour.
引用
收藏
页码：429 / 430
页数：2
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