维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

11篇 您的检索式：作者名="Libisch"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	Mutagenesis and heterologous expression in yeast of a plant Delta6-fatty acid desaturase显示文摘	Sayanova O Beaudoin F Libisch B	2001	J Exp Bot2001，52，：	1
	2	Functional identifieation of a deltaS-sphingolipid desaturase from Borago offieinalis 显示文摘	Sperling P Libisch B Zahringer U	2001	Arch Biochem Biophys2001，388，：	1
	3	New composite genetic element of the Tn916 family with dual macrolide resistance genes in a Streptococcus pneumoniae isolate belonging to clonal complex 271显示文摘	Camilli R Libisch B Fuzi M	2009	J Antimicrob Agents Chemother2009，53，5：	1
	4	Isolation of an integron - borne blaVIM-4 type metallo - β - lactamase gene from a carbapenem - resistant 显示文摘	Libisch B Gacs M Csiszar K	2004	Antimicrob Agents Chemother2004，48，9：	1
	5	Isolation of an integronborne blaVIM-4 type metallo-beta-laetamase gene from a carbapenem-resistant Pseudomonas aeruginosa clinical isolate in Hungary显示文摘	Libisch B Gacs M Csiszar K etal	2004	Antimicrob Agents Chemother2004，48，9：	1
	6	Identification of two muhidrug-re sistant Pseudornonas aeruginosa clonal lineages with a country wide distribution in Hungary显示文摘	Libisch B Balogh B Fuzi M	2009	Curr Microbio12009，58，2：	1
	7	Topological insulator in the presence of spatially correlated disorder显示文摘	Girschik A Libisch F Rotter S		0，，：	1
	8	Identification of two multidrug-resistant Pseudomonas aeruginosa clonal lineages with a countrywide distribution in Hungary显示文摘	Libisch B Balogh B Füzi M		0，，02：	1
	9	Mutagenesis and heterologous expression in yeast of a plant △^6- fatty acid desattLraSe 显示文摘	Sayanova O Beaudoin F Libisch B	2001	Journalof ExperimentaIBotany2001，52，360：	1
	10	Establishing clonal relationships between VIM - 1 - like metallo - beta - lactamase - producing Pseudomonas aeruginosa strains from four European countries by multilocus sequence typing 显示文摘	Giske CG Libisch B Colinon C	2006	J Clin Microbiol2006，44，12：	1
	11	Machine learning sparse tight-binding parameters for defects显示文摘We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects.We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization.Since Multi-layer perceptrons(i.e.,feed-forward neural networks)perform best we adopt them for our further investigations.We demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure,local density of states,transport and level spacing simulations for two common defects in single layer graphene.Our machine learning approach achieves results comparable to maximally localized Wannier functions(i.e.,DFT accuracy)without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation time.It is general and can be applied to a wide range of other materials,enabling accurate large-scale simulations of material properties in the presence of different defects.	Christoph Schattauer Milica Todorović Kunal Ghosh Patrick Rinke Florian Libisch	2022	npj Computational Materials2022，，1：	0

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费