npj Computational Materials两名编委获学术殊荣

Ceder教授是美国加州大学伯克利分校材料科学与工程学院院长，因其“计算材料研究及其在储能技术改进中的应用”的突出成就而当选。去年他的团队在npj Computational Materials发表了综述文章“Computational understanding of Li-ion batteries” , npj Computational Materials 2,16002 (2016)（http://www.nature.com/articles/npjcompumats20162），引起了广大读者的兴趣，现已有17000多次下载。Ceder教授是倡导高通量计算收集数据并用作材料设计的先驱者，他的建议直接导致了多个大型数据库的建立。由Kirklin 等发表在npj Computational Materials的“The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies”, npj Computational Materials 1:15010 (2015); doi:10.1038/npjcompumats.2015.10 (http://www.nature.com/articles/npjcompumats201510) 介绍的就是此类典型的数据库。

Schlom教授是美国康奈尔大学工业化学、材料科学与工程系特聘教授，因其“分子束外延复合氧化物的´材料-设计`从而影响高介电氧化物在半导体器件中的集成”的重要成就而当选。他的一篇综述“Oxides by design”将会在npj Computational Materials发表。Schlom教授非常强调由理论指导下的材料设计、材料的实验研究与计算研究相结合。npj Computational Materials也发表了多篇此类论著，以引导 1）用实验来验证计算模拟所作的预测结论，2）材料数据库指导的实验研究，和 3）融实验与计算于一炉的材料研究，如：Kumar 等的“Experimental verification of the ab initio phase transition sequence in SrZrO3 and comparisons with SrHfO3 and SrSnO3”, npj Computational Materials 3: 2 (2017); doi:10.1038/s41524-016-0002-y (http://www. nature.com/articles/s41524-016-0002-y)，和Iwasaki等的“Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries”, npj Computational Materials 3: 4 (2017); doi:10.1038/s41524-017-0006-2. （http://www.nature.com/articles/s41524-017-0006-2)等。

According to the press release of the US National Academy of Engineering (Feb. 8, 2017), two editorial board members of npj Computational Materials, Professor Gerbrand Ceder and Professor Darrell G. Schlom were elected to the US National Academy of Engineering. Npj Computational Materials is an open access journal published by the Nature Research, formerly the Nature Publishing Group, in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences.

Prof. Ceder is Chancellor’s Professor of Materials Science and Engineering, University of California, Berkeley. He was elected to the NAE for the development of practical computational materials design and its application to the improvement of energy storage technology. His group published a review article in npj Computational Materials on “Computational understanding of Li-ion batteries”, npj Computational Materials 2,16002 (2016); doi:10.1038/npjcompumats.2016.2 (http://www.nature.com/articles/npjcompumats20162) which has attracted wide interests and has been downloaded more than 17000 times. Prof. Ceder was the very first to propose the high throughput approach to computational data generation and materials design, which has led to massive materials databases. One of the examples was published in npj Computational Materials by Kirklin S, et al., “The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies”, npj Computational Materials 1:15010 (2015); doi:10.1038/npjcompumats.2015.10 (http://www.nature.com/articles/npjcompumats201510).

Prof. Schlom is the Herbert Fisk Johnson Professor of Industrial Chemistry, department of materials science and engineering, Cornell University, Ithaca, N.Y. He was elected for molecular-beam epitaxy “Materials-by-Design” of complex oxides impacting the integration of high dielectric oxides in semiconductor devices. He is commissioned to write an article for npj Computational Materials on “Oxides by design”. Prof. Schlom is one of the strongest advocates for design of materials guided by theory and computation and integrated experimental and computational studies of materials. Indeed npj Computational Materials also published articles on experimental validation of computational predictions, experimental generation of materials databases, and integrated computational and experimental studies of materials, e.g, Kumar A, et al. Experimental verification of the ab initio phase transition sequence in SrZrO3 and comparisons with SrHfO3 and SrSnO3, npj Computational Materials 3: 2 (2017); doi:10.1038/s41524-016-0002-y (http://www.nature.com/articles/s41524-016-0002-y); and Iwasaki Y, et al. Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries, npj Computational Materials 3: 4 (2017); doi:10.1038/s41524-017-0006-2. （http://www.nature.com/articles/s41524-017-0006-2).