Pseudopotential for electronic structure calculations of uranium compounds View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-09

AUTHORS

G. Smirnov, V. Stegailov

ABSTRACT

The density functional theory (DFT) is a research tool of the highest importance for electronic structure calculations. It is often the only affordable method for ab initio calculations of complex materials. The pseudopotential approach allows reducing the total number of electrons in the model that speeds up calculations. However, there is a lack of pseudopotentials for heavy elements suitable for condensed matter DFT models. In this work, we present a pseudopotential for uranium developed in the Goedecker–Teter–Hutter form. Its accuracy is illustrated using several molecular and solid-state calculations. More... »

PAGES

974-977

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s199508021705033x

DOI

http://dx.doi.org/10.1134/s199508021705033x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091827361


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0307", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Theoretical and Computational Chemistry", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Chemical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Moscow Institute of Physics and Technology", 
          "id": "https://www.grid.ac/institutes/grid.18763.3b", 
          "name": [
            "Joint Institute for High Temperatures of the Russian Academy of Sciences, 125412, Moscow, Russia", 
            "Moscow Institute of Physics and Technology, 141700, Dolgoprudny, Moscow oblast, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Smirnov", 
        "givenName": "G.", 
        "id": "sg:person.01176201755.87", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176201755.87"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Research University Higher School of Economics", 
          "id": "https://www.grid.ac/institutes/grid.410682.9", 
          "name": [
            "Joint Institute for High Temperatures of the Russian Academy of Sciences, 125412, Moscow, Russia", 
            "National Research University Higher School of Economics, 101000, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stegailov", 
        "givenName": "V.", 
        "id": "sg:person.013675326625.36", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013675326625.36"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1126/science.aad3000", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009597040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/qua.24978", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010918621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.54.1703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014394467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.54.1703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014394467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.58.3641", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014524036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.58.3641", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014524036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0953-8984/24/1/015702", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015293683"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jnucmat.2012.09.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022079149"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1134/s1066362213040012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028195584", 
          "https://doi.org/10.1134/s1066362213040012"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/chem.200601244", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029481641"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/14786436408222957", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047117084"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.commatsci.2013.01.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050536613"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp5101126", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056103722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1749559", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057811554"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.59.1758", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060591374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.59.1758", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060591374"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-09", 
    "datePublishedReg": "2017-09-01", 
    "description": "The density functional theory (DFT) is a research tool of the highest importance for electronic structure calculations. It is often the only affordable method for ab initio calculations of complex materials. The pseudopotential approach allows reducing the total number of electrons in the model that speeds up calculations. However, there is a lack of pseudopotentials for heavy elements suitable for condensed matter DFT models. In this work, we present a pseudopotential for uranium developed in the Goedecker\u2013Teter\u2013Hutter form. Its accuracy is illustrated using several molecular and solid-state calculations.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1134/s199508021705033x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136629", 
        "issn": [
          "1818-9962", 
          "1995-0802"
        ], 
        "name": "Lobachevskii Journal of Mathematics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "38"
      }
    ], 
    "name": "Pseudopotential for electronic structure calculations of uranium compounds", 
    "pagination": "974-977", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "e0cb67f312aaa36531a0dfe2743f591785d392f8dd0627d0c3808585bd7ea1c5"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1134/s199508021705033x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1091827361"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1134/s199508021705033x", 
      "https://app.dimensions.ai/details/publication/pub.1091827361"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T22:35", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8690_00000528.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1134%2FS199508021705033X"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1134/s199508021705033x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1134/s199508021705033x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1134/s199508021705033x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1134/s199508021705033x'


 

This table displays all metadata directly associated to this object as RDF triples.

113 TRIPLES      21 PREDICATES      40 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1134/s199508021705033x schema:about anzsrc-for:03
2 anzsrc-for:0307
3 schema:author N67e7cb16c3f74c918e2029936e971c18
4 schema:citation sg:pub.10.1134/s1066362213040012
5 https://doi.org/10.1002/chem.200601244
6 https://doi.org/10.1002/qua.24978
7 https://doi.org/10.1016/j.commatsci.2013.01.023
8 https://doi.org/10.1016/j.jnucmat.2012.09.019
9 https://doi.org/10.1021/jp5101126
10 https://doi.org/10.1063/1.1749559
11 https://doi.org/10.1080/14786436408222957
12 https://doi.org/10.1088/0953-8984/24/1/015702
13 https://doi.org/10.1103/physrevb.54.1703
14 https://doi.org/10.1103/physrevb.58.3641
15 https://doi.org/10.1103/physrevb.59.1758
16 https://doi.org/10.1126/science.aad3000
17 schema:datePublished 2017-09
18 schema:datePublishedReg 2017-09-01
19 schema:description The density functional theory (DFT) is a research tool of the highest importance for electronic structure calculations. It is often the only affordable method for ab initio calculations of complex materials. The pseudopotential approach allows reducing the total number of electrons in the model that speeds up calculations. However, there is a lack of pseudopotentials for heavy elements suitable for condensed matter DFT models. In this work, we present a pseudopotential for uranium developed in the Goedecker–Teter–Hutter form. Its accuracy is illustrated using several molecular and solid-state calculations.
20 schema:genre research_article
21 schema:inLanguage en
22 schema:isAccessibleForFree false
23 schema:isPartOf N143dc36cf9d1455b891e71d52b63b372
24 N1be020b968394b57a476cdc44d2c2d2a
25 sg:journal.1136629
26 schema:name Pseudopotential for electronic structure calculations of uranium compounds
27 schema:pagination 974-977
28 schema:productId N64d0ac0d16e948dcbdcca20531d2ab20
29 N7b7dbdbaa13b4dca85b537e63549606b
30 Nc3e2a508286d470990102cf521e96d1d
31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091827361
32 https://doi.org/10.1134/s199508021705033x
33 schema:sdDatePublished 2019-04-10T22:35
34 schema:sdLicense https://scigraph.springernature.com/explorer/license/
35 schema:sdPublisher N78798e1977484486af1db6e0538af614
36 schema:url http://link.springer.com/10.1134%2FS199508021705033X
37 sgo:license sg:explorer/license/
38 sgo:sdDataset articles
39 rdf:type schema:ScholarlyArticle
40 N143dc36cf9d1455b891e71d52b63b372 schema:issueNumber 5
41 rdf:type schema:PublicationIssue
42 N1be020b968394b57a476cdc44d2c2d2a schema:volumeNumber 38
43 rdf:type schema:PublicationVolume
44 N64d0ac0d16e948dcbdcca20531d2ab20 schema:name dimensions_id
45 schema:value pub.1091827361
46 rdf:type schema:PropertyValue
47 N67e7cb16c3f74c918e2029936e971c18 rdf:first sg:person.01176201755.87
48 rdf:rest N697700c999ac47d9afd5af4a9d3b9f89
49 N697700c999ac47d9afd5af4a9d3b9f89 rdf:first sg:person.013675326625.36
50 rdf:rest rdf:nil
51 N78798e1977484486af1db6e0538af614 schema:name Springer Nature - SN SciGraph project
52 rdf:type schema:Organization
53 N7b7dbdbaa13b4dca85b537e63549606b schema:name doi
54 schema:value 10.1134/s199508021705033x
55 rdf:type schema:PropertyValue
56 Nc3e2a508286d470990102cf521e96d1d schema:name readcube_id
57 schema:value e0cb67f312aaa36531a0dfe2743f591785d392f8dd0627d0c3808585bd7ea1c5
58 rdf:type schema:PropertyValue
59 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
60 schema:name Chemical Sciences
61 rdf:type schema:DefinedTerm
62 anzsrc-for:0307 schema:inDefinedTermSet anzsrc-for:
63 schema:name Theoretical and Computational Chemistry
64 rdf:type schema:DefinedTerm
65 sg:journal.1136629 schema:issn 1818-9962
66 1995-0802
67 schema:name Lobachevskii Journal of Mathematics
68 rdf:type schema:Periodical
69 sg:person.01176201755.87 schema:affiliation https://www.grid.ac/institutes/grid.18763.3b
70 schema:familyName Smirnov
71 schema:givenName G.
72 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176201755.87
73 rdf:type schema:Person
74 sg:person.013675326625.36 schema:affiliation https://www.grid.ac/institutes/grid.410682.9
75 schema:familyName Stegailov
76 schema:givenName V.
77 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013675326625.36
78 rdf:type schema:Person
79 sg:pub.10.1134/s1066362213040012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028195584
80 https://doi.org/10.1134/s1066362213040012
81 rdf:type schema:CreativeWork
82 https://doi.org/10.1002/chem.200601244 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029481641
83 rdf:type schema:CreativeWork
84 https://doi.org/10.1002/qua.24978 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010918621
85 rdf:type schema:CreativeWork
86 https://doi.org/10.1016/j.commatsci.2013.01.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050536613
87 rdf:type schema:CreativeWork
88 https://doi.org/10.1016/j.jnucmat.2012.09.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022079149
89 rdf:type schema:CreativeWork
90 https://doi.org/10.1021/jp5101126 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056103722
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1063/1.1749559 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057811554
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1080/14786436408222957 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047117084
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1088/0953-8984/24/1/015702 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015293683
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1103/physrevb.54.1703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014394467
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1103/physrevb.58.3641 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014524036
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1103/physrevb.59.1758 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060591374
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1126/science.aad3000 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009597040
105 rdf:type schema:CreativeWork
106 https://www.grid.ac/institutes/grid.18763.3b schema:alternateName Moscow Institute of Physics and Technology
107 schema:name Joint Institute for High Temperatures of the Russian Academy of Sciences, 125412, Moscow, Russia
108 Moscow Institute of Physics and Technology, 141700, Dolgoprudny, Moscow oblast, Russia
109 rdf:type schema:Organization
110 https://www.grid.ac/institutes/grid.410682.9 schema:alternateName National Research University Higher School of Economics
111 schema:name Joint Institute for High Temperatures of the Russian Academy of Sciences, 125412, Moscow, Russia
112 National Research University Higher School of Economics, 101000, Moscow, Russia
113 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...