数据科学
钙钛矿(结构)
计算机科学
财产(哲学)
点(几何)
纳米技术
材料科学
工程类
几何学
数学
化学工程
认识论
哲学
作者
Rayan Chakraborty,Volker Blüm
标识
DOI:10.1016/j.trechm.2023.08.005
摘要
Over the past decade, hybrid perovskite research has evolved to a point where the literature contains an enormous volume of chemical and physical information. However, many essential material design challenges remain open for researchers to address. The dispersed nature of the large, rapidly growing body of hybrid perovskite materials data poses a barrier to systematic discovery efforts, which can be solved by materials property databases, either by high-throughput or by systematic, accurate human-curated efforts. This opinioned review article discusses the necessity, challenges, and requirements of building such data libraries. In light of using machine learning (ML) and related tools to solve specific problems, the importance of information related to different material attributes and properties is also highlighted.
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