Automated nanoindention and its role in data-driven materials research

被引：0

作者：

Bali, Krisztian ^{[1
]}

Tarjanyi, Tamas ^{[1
]}

Wang, Kun ^{[2
]}

Wang, Xingwu ^{[2
]}

机构：

[1] Semilab, Budapest, Hungary

[2] Alfred Univ, Alfred, NY 14802 USA

来源：

AMERICAN CERAMIC SOCIETY BULLETIN | 2024年 / 103卷 / 06期

关键词：

Automation;

D O I：

暂无

中图分类号：

TQ174 [陶瓷工业]; TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Machine learning has the potential to revolutionize the discovery and development process of novel materials.1 However, to properly train these models, researchers must have access to massive amounts of experimental data. Automated systems provide a way to collect and process high-quality data at record speeds by reducing the need for manual operation and oversight.

引用

页数：52