Kick–Fukui: A Fukui Function-Guided Method for Molecular Structure Prediction
Here, we introduce a hybrid method, named Kick–Fukui, to explore the potential energy surface (PES) of clusters and molecules using the Coulombic integral between the Fukui functions in the first screening of the best individuals. In the process, small stable molecules or clusters whose combination...
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Veröffentlicht in: | Journal of chemical information and modeling 2021-08, Vol.61 (8), p.3955-3963 |
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creator | Yañez, Osvaldo Báez-Grez, Rodrigo Inostroza, Diego Pino-Rios, Ricardo Rabanal-León, Walter A Contreras-García, Julia Cardenas, Carlos Tiznado, William |
description | Here, we introduce a hybrid method, named Kick–Fukui, to explore the potential energy surface (PES) of clusters and molecules using the Coulombic integral between the Fukui functions in the first screening of the best individuals. In the process, small stable molecules or clusters whose combination has the stoichiometry of the explored species are used as assembly units. First, a small set of candidates has been selected from a large and stochastically generated (Kick) population according to the maximum value of the Coulombic integral between the Fukui functions of both fragments. Subsequently, these few candidates are optimized using a gradient method and density functional theory (DFT) calculations. The performance of the program has been evaluated to explore the PES of various systems, including atomic and molecular clusters. In most cases studied, the global minimum (GM) has been identified with a low computational cost. The strategy does not allow to identify the GM of some silicon clusters; however, it predicts local minima very close in energy to the GM that could be used as the initial population of evolutionary algorithms. |
doi_str_mv | 10.1021/acs.jcim.1c00605 |
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In the process, small stable molecules or clusters whose combination has the stoichiometry of the explored species are used as assembly units. First, a small set of candidates has been selected from a large and stochastically generated (Kick) population according to the maximum value of the Coulombic integral between the Fukui functions of both fragments. Subsequently, these few candidates are optimized using a gradient method and density functional theory (DFT) calculations. The performance of the program has been evaluated to explore the PES of various systems, including atomic and molecular clusters. In most cases studied, the global minimum (GM) has been identified with a low computational cost. The strategy does not allow to identify the GM of some silicon clusters; however, it predicts local minima very close in energy to the GM that could be used as the initial population of evolutionary algorithms.</description><identifier>ISSN: 1549-9596</identifier><identifier>EISSN: 1549-960X</identifier><identifier>DOI: 10.1021/acs.jcim.1c00605</identifier><language>eng</language><publisher>Washington: American Chemical Society</publisher><subject>Chemical Sciences ; Computational Chemistry ; Density functional theory ; Evolutionary algorithms ; Integrals ; Molecular clusters ; Molecular structure ; or physical chemistry ; Potential energy ; Stoichiometry ; Theoretical and</subject><ispartof>Journal of chemical information and modeling, 2021-08, Vol.61 (8), p.3955-3963</ispartof><rights>2021 American Chemical Society</rights><rights>Copyright American Chemical Society Aug 23, 2021</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a417t-766d94264dda7eedb59a8874fa7e06067e19338bcd026de58370f0451814d26e3</citedby><cites>FETCH-LOGICAL-a417t-766d94264dda7eedb59a8874fa7e06067e19338bcd026de58370f0451814d26e3</cites><orcidid>0000-0003-4756-1115 ; 0000-0002-6061-8879 ; 0000-0001-8993-9353 ; 0000-0001-9303-1559 ; 0000-0002-0648-6502 ; 0000-0002-8947-9526</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c00605$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.jcim.1c00605$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>230,315,781,785,886,2766,27077,27925,27926,56739,56789</link.rule.ids><backlink>$$Uhttps://hal.sorbonne-universite.fr/hal-03356631$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Yañez, Osvaldo</creatorcontrib><creatorcontrib>Báez-Grez, Rodrigo</creatorcontrib><creatorcontrib>Inostroza, Diego</creatorcontrib><creatorcontrib>Pino-Rios, Ricardo</creatorcontrib><creatorcontrib>Rabanal-León, Walter A</creatorcontrib><creatorcontrib>Contreras-García, Julia</creatorcontrib><creatorcontrib>Cardenas, Carlos</creatorcontrib><creatorcontrib>Tiznado, William</creatorcontrib><title>Kick–Fukui: A Fukui Function-Guided Method for Molecular Structure Prediction</title><title>Journal of chemical information and modeling</title><addtitle>J. Chem. Inf. Model</addtitle><description>Here, we introduce a hybrid method, named Kick–Fukui, to explore the potential energy surface (PES) of clusters and molecules using the Coulombic integral between the Fukui functions in the first screening of the best individuals. In the process, small stable molecules or clusters whose combination has the stoichiometry of the explored species are used as assembly units. First, a small set of candidates has been selected from a large and stochastically generated (Kick) population according to the maximum value of the Coulombic integral between the Fukui functions of both fragments. Subsequently, these few candidates are optimized using a gradient method and density functional theory (DFT) calculations. The performance of the program has been evaluated to explore the PES of various systems, including atomic and molecular clusters. In most cases studied, the global minimum (GM) has been identified with a low computational cost. The strategy does not allow to identify the GM of some silicon clusters; however, it predicts local minima very close in energy to the GM that could be used as the initial population of evolutionary algorithms.</description><subject>Chemical Sciences</subject><subject>Computational Chemistry</subject><subject>Density functional theory</subject><subject>Evolutionary algorithms</subject><subject>Integrals</subject><subject>Molecular clusters</subject><subject>Molecular structure</subject><subject>or physical chemistry</subject><subject>Potential energy</subject><subject>Stoichiometry</subject><subject>Theoretical and</subject><issn>1549-9596</issn><issn>1549-960X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kcFKAzEQhhdRsFbvHhe8KLg1yW6SXW-l2FZsqaCCt5AmWZp229RkI3jzHXxDn8S023oQvExmMt8_zPBH0TkEHQgQvOHCdeZCLztQAEAAPohaEGdFUhDwerjPcUGOoxPn5gCkaUFQK5o8aLH4_vzq-4XXt3E33iYhrkStzSoZeC2VjMeqnhkZl8bGY1Mp4Stu46faelF7q-JHq6TeCk6jo5JXTp3t3nb00r977g2T0WRw3-uOEp5BWieUEFlkiGRScqqUnOKC5znNylCF7QlVsEjTfCokQEQqnKcUlCDDMIeZRESl7eiqmTvjFVtbveT2gxmu2bA7Ypu_cCAmJIXvMLCXDbu25s0rV7OldkJVFV8p4x1DmABUUApoQC_-oHPj7SpcEigKUJrlGAUKNJSwxjmryt8NIGAbN1hwg23cYDs3guS6kWw7-5n_4j_Yeow4</recordid><startdate>20210823</startdate><enddate>20210823</enddate><creator>Yañez, Osvaldo</creator><creator>Báez-Grez, Rodrigo</creator><creator>Inostroza, Diego</creator><creator>Pino-Rios, Ricardo</creator><creator>Rabanal-León, Walter A</creator><creator>Contreras-García, Julia</creator><creator>Cardenas, Carlos</creator><creator>Tiznado, William</creator><general>American Chemical Society</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-4756-1115</orcidid><orcidid>https://orcid.org/0000-0002-6061-8879</orcidid><orcidid>https://orcid.org/0000-0001-8993-9353</orcidid><orcidid>https://orcid.org/0000-0001-9303-1559</orcidid><orcidid>https://orcid.org/0000-0002-0648-6502</orcidid><orcidid>https://orcid.org/0000-0002-8947-9526</orcidid></search><sort><creationdate>20210823</creationdate><title>Kick–Fukui: A Fukui Function-Guided Method for Molecular Structure Prediction</title><author>Yañez, Osvaldo ; 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Chem. Inf. Model</addtitle><date>2021-08-23</date><risdate>2021</risdate><volume>61</volume><issue>8</issue><spage>3955</spage><epage>3963</epage><pages>3955-3963</pages><issn>1549-9596</issn><eissn>1549-960X</eissn><abstract>Here, we introduce a hybrid method, named Kick–Fukui, to explore the potential energy surface (PES) of clusters and molecules using the Coulombic integral between the Fukui functions in the first screening of the best individuals. In the process, small stable molecules or clusters whose combination has the stoichiometry of the explored species are used as assembly units. First, a small set of candidates has been selected from a large and stochastically generated (Kick) population according to the maximum value of the Coulombic integral between the Fukui functions of both fragments. Subsequently, these few candidates are optimized using a gradient method and density functional theory (DFT) calculations. The performance of the program has been evaluated to explore the PES of various systems, including atomic and molecular clusters. In most cases studied, the global minimum (GM) has been identified with a low computational cost. The strategy does not allow to identify the GM of some silicon clusters; however, it predicts local minima very close in energy to the GM that could be used as the initial population of evolutionary algorithms.</abstract><cop>Washington</cop><pub>American Chemical Society</pub><doi>10.1021/acs.jcim.1c00605</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-4756-1115</orcidid><orcidid>https://orcid.org/0000-0002-6061-8879</orcidid><orcidid>https://orcid.org/0000-0001-8993-9353</orcidid><orcidid>https://orcid.org/0000-0001-9303-1559</orcidid><orcidid>https://orcid.org/0000-0002-0648-6502</orcidid><orcidid>https://orcid.org/0000-0002-8947-9526</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Chemical Sciences Computational Chemistry Density functional theory Evolutionary algorithms Integrals Molecular clusters Molecular structure or physical chemistry Potential energy Stoichiometry Theoretical and |
title | Kick–Fukui: A Fukui Function-Guided Method for Molecular Structure Prediction |
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