1 resultado para Russia--Maps
em Duke University
Filtro por publicador
- Aberystwyth University Repository - Reino Unido (2)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (9)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (3)
- Applied Math and Science Education Repository - Washington - USA (1)
- Aquatic Commons (6)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archive of European Integration (4)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (4)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (13)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (19)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (87)
- Boston University Digital Common (5)
- Brock University, Canada (3)
- Bucknell University Digital Commons - Pensilvania - USA (4)
- Cambridge University Engineering Department Publications Database (36)
- CamPuce - an association for the promotion of science and humanities in African Countries (1)
- CentAUR: Central Archive University of Reading - UK (57)
- Center for Jewish History Digital Collections (12)
- Central European University - Research Support Scheme (6)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (13)
- Cochin University of Science & Technology (CUSAT), India (4)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (2)
- Dalarna University College Electronic Archive (2)
- Department of Computer Science E-Repository - King's College London, Strand, London (1)
- Digital Archives@Colby (2)
- Digital Commons - Montana Tech (1)
- Digital Commons @ Winthrop University (1)
- Digital Peer Publishing (7)
- DigitalCommons - The University of Maine Research (1)
- DigitalCommons@The Texas Medical Center (1)
- DigitalCommons@University of Nebraska - Lincoln (2)
- Digitale Sammlungen - Goethe-Universität Frankfurt am Main (8)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (4)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (1)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (4)
- Greenwich Academic Literature Archive - UK (2)
- Harvard University (16)
- Helda - Digital Repository of University of Helsinki (25)
- Indian Institute of Science - Bangalore - Índia (39)
- Instituto Politécnico do Porto, Portugal (1)
- Massachusetts Institute of Technology (1)
- Ministerio de Cultura, Spain (10)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (5)
- Publishing Network for Geoscientific & Environmental Data (347)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (48)
- Queensland University of Technology - ePrints Archive (51)
- RDBU - Repositório Digital da Biblioteca da Unisinos (2)
- Repositório digital da Fundação Getúlio Vargas - FGV (1)
- Repositorio Institucional da UFLA (RIUFLA) (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (40)
- School of Medicine, Washington University, United States (2)
- SerWisS - Server für Wissenschaftliche Schriften der Fachhochschule Hannover (2)
- Universidad Autónoma de Nuevo León, Mexico (2)
- Universidad del Rosario, Colombia (2)
- Universidade Federal do Rio Grande do Norte (UFRN) (1)
- Universitat de Girona, Spain (5)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (1)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (3)
- University of Connecticut - USA (2)
- University of Southampton, United Kingdom (2)
- WestminsterResearch - UK (2)
Resumo:
The ability to predict the existence and crystal type of ordered structures of materials from their components is a major challenge of current materials research. Empirical methods use experimental data to construct structure maps and make predictions based on clustering of simple physical parameters. Their usefulness depends on the availability of reliable data over the entire parameter space. Recent development of high-throughput methods opens the possibility to enhance these empirical structure maps by ab initio calculations in regions of the parameter space where the experimental evidence is lacking or not well characterized. In this paper we construct enhanced maps for the binary alloys of hcp metals, where the experimental data leaves large regions of poorly characterized systems believed to be phase separating. In these enhanced maps, the clusters of noncompound-forming systems are much smaller than indicated by the empirical results alone. © 2010 The American Physical Society.