Monte Carlo Approach for Modeling and Optimization of One-Dimensional Bimetallic Nanostructures View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019-01-18

AUTHORS

Vladimir Myasnichenko , Nickolay Sdobnyakov , Leoneed Kirilov , Rossen Mikhov , Stefka Fidanova

ABSTRACT

In this paper we present a method for optimizing of metal nanoparticle structures. The core of the method is a lattice Monte-Carlo method with different lattices combined with an approach from molecular dynamics. Interaction between atoms is calculated using multi-particle tight-binding potential of Gupta – Cleri&Rosato. The method allows solving of problems with periodic boundary conditions. It can be used for modeling of one-dimensional (nanowire, tube) and two-dimensional (nano-film) structures. If periodic boundary conditions are not given, we assume finite dimensions of the model lattice. In addition, automatic relaxation of the crystal lattice can be performed in order to minimize further the potential energy of the system. Both stretching and compressing of the lattice is permitted. A computer implementation of the method is developed. It allows easy and efficient operation. It uses the commonly accepted XYZ format for describing metal nanoparticles. The parameters of the method, such as number and type of metal atoms, temperature of the system, etc. are entered on a separate command line. The method is tested extensively on a large set of examples. More... »

PAGES

133-141

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-10692-8_15

DOI

http://dx.doi.org/10.1007/978-3-030-10692-8_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111517480


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/2008/03", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Chemical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0306", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Chemistry (incl. Structural)", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tver State University, Tver, Russia", 
          "id": "http://www.grid.ac/institutes/grid.438242.b", 
          "name": [
            "Tver State University, Tver, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Myasnichenko", 
        "givenName": "Vladimir", 
        "id": "sg:person.013562762060.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013562762060.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tver State University, Tver, Russia", 
          "id": "http://www.grid.ac/institutes/grid.438242.b", 
          "name": [
            "Tver State University, Tver, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sdobnyakov", 
        "givenName": "Nickolay", 
        "id": "sg:person.010244167706.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010244167706.80"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.424988.b", 
          "name": [
            "Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kirilov", 
        "givenName": "Leoneed", 
        "id": "sg:person.015700215743.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015700215743.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.424988.b", 
          "name": [
            "Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mikhov", 
        "givenName": "Rossen", 
        "id": "sg:person.014434131110.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014434131110.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.424988.b", 
          "name": [
            "Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fidanova", 
        "givenName": "Stefka", 
        "id": "sg:person.011173106320.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2019-01-18", 
    "datePublishedReg": "2019-01-18", 
    "description": "Abstract\nIn this paper we present a method for optimizing of metal nanoparticle structures. The core of the method is a lattice Monte-Carlo method with different lattices combined with an approach from molecular dynamics. Interaction between atoms is calculated using multi-particle tight-binding potential of Gupta \u2013 Cleri&Rosato. The method allows solving of problems with periodic boundary conditions. It can be used for modeling of one-dimensional (nanowire, tube) and two-dimensional (nano-film) structures. If periodic boundary conditions are not given, we assume finite dimensions of the model lattice. In addition, automatic relaxation of the crystal lattice can be performed in order to minimize further the potential energy of the system. Both stretching and compressing of the lattice is permitted. A computer implementation of the method is developed. It allows easy and efficient operation. It uses the commonly accepted XYZ format for describing metal nanoparticles. The parameters of the method, such as number and type of metal atoms, temperature of the system, etc. are entered on a separate command line. The method is tested extensively on a large set of examples.", 
    "editor": [
      {
        "familyName": "Nikolov", 
        "givenName": "Geno", 
        "type": "Person"
      }, 
      {
        "familyName": "Kolkovska", 
        "givenName": "Natalia", 
        "type": "Person"
      }, 
      {
        "familyName": "Georgiev", 
        "givenName": "Krassimir", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-030-10692-8_15", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-030-10691-1", 
        "978-3-030-10692-8"
      ], 
      "name": "Numerical Methods and Applications", 
      "type": "Book"
    }, 
    "keywords": [
      "periodic boundary conditions", 
      "boundary conditions", 
      "Monte Carlo method", 
      "Monte Carlo approach", 
      "finite dimensions", 
      "lattice Monte Carlo method", 
      "computer implementation", 
      "Carlo approach", 
      "different lattices", 
      "automatic relaxation", 
      "model lattice", 
      "solving of problems", 
      "lattice", 
      "two-dimensional structure", 
      "potential energy", 
      "metal nanoparticle structures", 
      "large set", 
      "crystal lattice", 
      "modeling", 
      "efficient operation", 
      "molecular dynamics", 
      "atoms", 
      "optimization", 
      "command line", 
      "dynamics", 
      "Gupta", 
      "solving", 
      "problem", 
      "approach", 
      "system", 
      "XYZ format", 
      "parameters", 
      "structure", 
      "set", 
      "binding potential", 
      "optimizing", 
      "relaxation", 
      "dimensions", 
      "metal atoms", 
      "conditions", 
      "energy", 
      "nanoparticle structure", 
      "order", 
      "nanostructures", 
      "number", 
      "implementation", 
      "operation", 
      "temperature", 
      "core", 
      "lines", 
      "bimetallic nanostructures", 
      "interaction", 
      "compressing", 
      "types", 
      "potential", 
      "addition", 
      "format", 
      "metal nanoparticles", 
      "nanoparticles", 
      "method", 
      "example", 
      "paper"
    ], 
    "name": "Monte Carlo Approach for Modeling and Optimization of One-Dimensional Bimetallic Nanostructures", 
    "pagination": "133-141", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111517480"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-030-10692-8_15"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-030-10692-8_15", 
      "https://app.dimensions.ai/details/publication/pub.1111517480"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-10T10:57", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/chapter/chapter_95.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-030-10692-8_15"
  }
]
 

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.10.1007/978-3-030-10692-8_15'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-10692-8_15'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-10692-8_15'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-10692-8_15'


 

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

163 TRIPLES      23 PREDICATES      87 URIs      80 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-030-10692-8_15 schema:about anzsrc-for:03
2 anzsrc-for:0306
3 schema:author Nae6bdd7a94c94c83b26005360c516a8e
4 schema:datePublished 2019-01-18
5 schema:datePublishedReg 2019-01-18
6 schema:description Abstract In this paper we present a method for optimizing of metal nanoparticle structures. The core of the method is a lattice Monte-Carlo method with different lattices combined with an approach from molecular dynamics. Interaction between atoms is calculated using multi-particle tight-binding potential of Gupta – Cleri&Rosato. The method allows solving of problems with periodic boundary conditions. It can be used for modeling of one-dimensional (nanowire, tube) and two-dimensional (nano-film) structures. If periodic boundary conditions are not given, we assume finite dimensions of the model lattice. In addition, automatic relaxation of the crystal lattice can be performed in order to minimize further the potential energy of the system. Both stretching and compressing of the lattice is permitted. A computer implementation of the method is developed. It allows easy and efficient operation. It uses the commonly accepted XYZ format for describing metal nanoparticles. The parameters of the method, such as number and type of metal atoms, temperature of the system, etc. are entered on a separate command line. The method is tested extensively on a large set of examples.
7 schema:editor N5da53120bd224004a839cf10e2667772
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N9047d06cfd484dfd95a91387250b79cc
12 schema:keywords Carlo approach
13 Gupta
14 Monte Carlo approach
15 Monte Carlo method
16 XYZ format
17 addition
18 approach
19 atoms
20 automatic relaxation
21 bimetallic nanostructures
22 binding potential
23 boundary conditions
24 command line
25 compressing
26 computer implementation
27 conditions
28 core
29 crystal lattice
30 different lattices
31 dimensions
32 dynamics
33 efficient operation
34 energy
35 example
36 finite dimensions
37 format
38 implementation
39 interaction
40 large set
41 lattice
42 lattice Monte Carlo method
43 lines
44 metal atoms
45 metal nanoparticle structures
46 metal nanoparticles
47 method
48 model lattice
49 modeling
50 molecular dynamics
51 nanoparticle structure
52 nanoparticles
53 nanostructures
54 number
55 operation
56 optimization
57 optimizing
58 order
59 paper
60 parameters
61 periodic boundary conditions
62 potential
63 potential energy
64 problem
65 relaxation
66 set
67 solving
68 solving of problems
69 structure
70 system
71 temperature
72 two-dimensional structure
73 types
74 schema:name Monte Carlo Approach for Modeling and Optimization of One-Dimensional Bimetallic Nanostructures
75 schema:pagination 133-141
76 schema:productId N27d0685cc7d3456db5a8cda190999041
77 N6152ef0f90194df1ba2224f838380b4f
78 schema:publisher N8ac8a8adbce443af8daf29471b2f4c01
79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111517480
80 https://doi.org/10.1007/978-3-030-10692-8_15
81 schema:sdDatePublished 2022-05-10T10:57
82 schema:sdLicense https://scigraph.springernature.com/explorer/license/
83 schema:sdPublisher N86249e2989a2470880ad65e98b4c84bd
84 schema:url https://doi.org/10.1007/978-3-030-10692-8_15
85 sgo:license sg:explorer/license/
86 sgo:sdDataset chapters
87 rdf:type schema:Chapter
88 N057ce80187f3442886dc3ea131902f4e rdf:first Ncb3d486800d246f98d09d330c6274491
89 rdf:rest N4477a22c6cf64ee08d8fc82019bee534
90 N27d0685cc7d3456db5a8cda190999041 schema:name doi
91 schema:value 10.1007/978-3-030-10692-8_15
92 rdf:type schema:PropertyValue
93 N3ac125919d334fcf80a85909155cecdc rdf:first sg:person.011173106320.18
94 rdf:rest rdf:nil
95 N4477a22c6cf64ee08d8fc82019bee534 rdf:first Nfa68c42c330b4661aa13a9f3c3022fd1
96 rdf:rest rdf:nil
97 N5b300000841149daa376c33b774c4663 schema:familyName Nikolov
98 schema:givenName Geno
99 rdf:type schema:Person
100 N5da53120bd224004a839cf10e2667772 rdf:first N5b300000841149daa376c33b774c4663
101 rdf:rest N057ce80187f3442886dc3ea131902f4e
102 N6152ef0f90194df1ba2224f838380b4f schema:name dimensions_id
103 schema:value pub.1111517480
104 rdf:type schema:PropertyValue
105 N626fed9e7a674d6bb86e51df0b79f800 rdf:first sg:person.010244167706.80
106 rdf:rest N8f3bdffa9fba40a09dc2af8e234e718e
107 N86249e2989a2470880ad65e98b4c84bd schema:name Springer Nature - SN SciGraph project
108 rdf:type schema:Organization
109 N8ac8a8adbce443af8daf29471b2f4c01 schema:name Springer Nature
110 rdf:type schema:Organisation
111 N8f3bdffa9fba40a09dc2af8e234e718e rdf:first sg:person.015700215743.52
112 rdf:rest Nc8e0d42ecb9d4348b4e7d3d848ee2076
113 N9047d06cfd484dfd95a91387250b79cc schema:isbn 978-3-030-10691-1
114 978-3-030-10692-8
115 schema:name Numerical Methods and Applications
116 rdf:type schema:Book
117 Nae6bdd7a94c94c83b26005360c516a8e rdf:first sg:person.013562762060.25
118 rdf:rest N626fed9e7a674d6bb86e51df0b79f800
119 Nc8e0d42ecb9d4348b4e7d3d848ee2076 rdf:first sg:person.014434131110.58
120 rdf:rest N3ac125919d334fcf80a85909155cecdc
121 Ncb3d486800d246f98d09d330c6274491 schema:familyName Kolkovska
122 schema:givenName Natalia
123 rdf:type schema:Person
124 Nfa68c42c330b4661aa13a9f3c3022fd1 schema:familyName Georgiev
125 schema:givenName Krassimir
126 rdf:type schema:Person
127 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
128 schema:name Chemical Sciences
129 rdf:type schema:DefinedTerm
130 anzsrc-for:0306 schema:inDefinedTermSet anzsrc-for:
131 schema:name Physical Chemistry (incl. Structural)
132 rdf:type schema:DefinedTerm
133 sg:person.010244167706.80 schema:affiliation grid-institutes:grid.438242.b
134 schema:familyName Sdobnyakov
135 schema:givenName Nickolay
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010244167706.80
137 rdf:type schema:Person
138 sg:person.011173106320.18 schema:affiliation grid-institutes:grid.424988.b
139 schema:familyName Fidanova
140 schema:givenName Stefka
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18
142 rdf:type schema:Person
143 sg:person.013562762060.25 schema:affiliation grid-institutes:grid.438242.b
144 schema:familyName Myasnichenko
145 schema:givenName Vladimir
146 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013562762060.25
147 rdf:type schema:Person
148 sg:person.014434131110.58 schema:affiliation grid-institutes:grid.424988.b
149 schema:familyName Mikhov
150 schema:givenName Rossen
151 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014434131110.58
152 rdf:type schema:Person
153 sg:person.015700215743.52 schema:affiliation grid-institutes:grid.424988.b
154 schema:familyName Kirilov
155 schema:givenName Leoneed
156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015700215743.52
157 rdf:type schema:Person
158 grid-institutes:grid.424988.b schema:alternateName Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria
159 schema:name Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria
160 rdf:type schema:Organization
161 grid-institutes:grid.438242.b schema:alternateName Tver State University, Tver, Russia
162 schema:name Tver State University, Tver, Russia
163 rdf:type schema:Organization
 




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


...