Npj Computational Materials

杂志介绍

Npj Computational Materials杂志介绍

《Npj Computational Materials》是一本以English为主的开放获取国际优秀期刊，中文名称Npj 计算材料，本刊主要出版、报道材料科学-物理化学领域的研究动态以及在该领域取得的各方面的经验和科研成果，介绍该领域有关本专业的最新进展，探讨行业发展的思路和方法，以促进学术信息交流，提高行业发展。该刊已被国际权威数据库SCI、SCIE收录，为该领域相关学科的发展起到了良好的推动作用，也得到了本专业人员的广泛认可。该刊最新影响因子为13.1，最新CiteScore 指数为17.4。

本刊近期中国学者发表的论文主要有：

• Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculation

  Author: Ohnishi, Masato; Deng, Tianqi; Torres, Pol; Xu, Zhihao; Tadano, Terumasa; Zhang, Haoming; Nong, Wei; Hanai, Masatoshi; Wang, Zeyu; Morita, Michimasa; Tian, Zhiting; Hu, Ming; Ruan, Xiulin; Yoshida, Ryo; Suzumura, Toyotaro; Lindsay, Lucas; McGaughey, Alan J. H.; Luo, Tengfei; Hippalgaonkar, Kedar; Shiomi, Junichiro

• Origin of suppressed ferroelectricity in κ-Ga2O3: interplay between polarization and lattice domain wall

  Author: Zhu, Yonghao; Liu, Wen-Hao; Long, Run; Wang, Lin-Wang; Luo, Jun-Wei; Wang, Zhi

• The dual role of 90° domain walls in ferroelectric switching of Hf0.5Zr0.5O2 thin films: Insights from phase-field simulation

  Author: Wen, Shubin; Peng, Ren-Ci; Cheng, Xiaoxing; Liao, Min; Zhou, Yichun

• Hybrid magnetic skyrmions with near-zero Hall angle and electrical switchability in a 2D multiferroi

  Author: Li, Xuhong; Zhou, Mo; Wei, Yongteng; Cao, Tengfei; Fan, Xiaoli

英文介绍

Npj Computational Materials杂志英文介绍

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.