Npj Computational Materials

雜志介紹

Npj Computational Materials雜志介紹

《Npj Computational Materials》是一本以English為主的開放獲取國際優秀期刊，中文名稱Npj 計算材料，本刊主要出版、報道材料科學-MATERIALS SCIENCE, MULTIDISCIPLINARY領域的研究動態以及在該領域取得的各方面的經驗和科研成果，介紹該領域有關本專業的最新進展，探討行業發展的思路和方法，以促進學術信息交流，提高行業發展。該刊已被國際權威數據庫SCIE收錄，為該領域相關學科的發展起到了良好的推動作用，也得到了本專業人員的廣泛認可。該刊最新影響因子為9.4，最新CiteScore 指數為15.3。

本刊近期中國學者發表的論文主要有：

• Machine-learning atomic simulation for heterogeneous catalysis

  Author: Chen, Dongxiao; Shang, Cheng; Liu, Zhi-Pan

• Predicting lattice thermal conductivity via machine learning: a mini review

  Author: Luo, Yufeng; Li, Mengke; Yuan, Hongmei; Liu, Huijun; Fang, Ying

• In-plane ferroelectric tunnel junctions based on 2D alpha-In2Se3/semiconductor heterostructures

  Author: Liu, Zifang; Hou, Pengfei; Sun, Lizhong; Tsymbal, Evgeny Y.; Jiang, Jie; Yang, Qiong

• Shift current response in elemental two-dimensional ferroelectrics

  Author: Qian, Zhuang; Zhou, Jian; Wang, Hua; Liu, Shi

英文介紹

Npj Computational Materials雜志英文介紹

npj Computational Materials publishes high-quality research papers that apply computational approaches for the design of new materials, and for enhancing our understanding of existing ones. New computational techniques – and the refinement of current approaches – that facilitate these aims are also welcome, as are experimental papers that complement computational findings.

Representative journal scope includes:

application and development of existing and emerging theoretical and simulation approaches for the study of materials in their entirety (organic and inorganic).

structure and property prediction of new materials, such as by the ‘materials by design’ paradigm.

significant new understanding of materials fundamentals, behavior and properties.

integrative/complementary experimental works that serve to validate, support and extend computational findings.

high-throughput techniques – both computational and experimental – for large data set generation, and materials data mining.