Automated pipeline for superalloy data by text mining

IF 9.4 1区材料科学 Q1 CHEMISTRY, PHYSICAL

npj Computational Materials Pub Date : 2022-01-19 DOI:10.1038/s41524-021-00687-2

Weiren Wang, Xue Jiang, Shaohan Tian, Pei Liu, Depeng Dang, Yanjing Su, T. Lookman, Jianxin Xie

引用次数: 23

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基于文本挖掘的高温合金数据自动流水线

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

npj Computational Materials Mathematics-Modeling and Simulation

CiteScore

15.30

自引率

5.20%

发文量

229

审稿时长

6 weeks

期刊介绍： npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.