4.5 Review

From DFT to machine learning: recent approaches to materials science-a review

期刊

JOURNAL OF PHYSICS-MATERIALS
卷 2, 期 3, 页码 -

出版社

IOP Publishing Ltd
DOI: 10.1088/2515-7639/ab084b

关键词

machine learning; artificial intelligence; materials informatics; density functional theory (DFT); high-throughput; data science; big data screening

资金

  1. FundacAo de Amparo a Pesquisa do Estado de SAo Paulo (FAPESP) [2017/18139-6, 18/05565-0, 18/11856-7, 16/14011-2, 17/02317-2]
  2. Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [18/11856-7, 16/14011-2] Funding Source: FAPESP

向作者/读者索取更多资源

Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据