大数据
材料信息学
数据科学
信息学
财产(哲学)
计算机科学
Boosting(机器学习)
实现(概率)
分析
领域(数学)
科学发现
健康信息学
透视图(图形)
数据分析
工程类
人工智能
数据挖掘
电气工程
认识论
工程信息学
哲学
心理学
统计
认知科学
纯数学
数学
医学
护理部
公共卫生
作者
Ankit Agrawal,Alok Choudhary
出处
期刊:APL Materials
[American Institute of Physics]
日期:2016-04-15
卷期号:4 (5)
被引量:1142
摘要
Our ability to collect “big data” has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Materials Genome Initiative (MGI), further boosting the emerging field of materials informatics. In this article, we look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery). Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.
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