Ab initio method for locating characteristic potential-energy minima of liquids
It is possible in principle to probe the many-atom potential surface using density functional theory (DFT). This will allow us to apply DFT to the Hamiltonian formulation of atomic motion in monatomic liquids by Wallace [Phys. Rev. E 56, 4179 (1997)]. For a monatomic system, analysis of the potentia...
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Veröffentlicht in: | Physical review. E, Statistical, nonlinear, and soft matter physics Statistical, nonlinear, and soft matter physics, 2009-11, Vol.80 (5 Pt 1), p.051111-051111, Article 051111 |
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container_title | Physical review. E, Statistical, nonlinear, and soft matter physics |
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creator | Holmström, E Bock, N Peery, Travis B Lizárraga, R De Lorenzi-Venneri, G Chisolm, Eric D Wallace, Duane C |
description | It is possible in principle to probe the many-atom potential surface using density functional theory (DFT). This will allow us to apply DFT to the Hamiltonian formulation of atomic motion in monatomic liquids by Wallace [Phys. Rev. E 56, 4179 (1997)]. For a monatomic system, analysis of the potential surface is facilitated by the random and symmetric classification of potential-energy valleys. Since the random valleys are numerically dominant and uniform in their macroscopic potential properties, only a few quenches are necessary to establish these properties. Here we describe an efficient technique for doing this. Quenches are done from easily generated "stochastic" configurations, in which the nuclei are distributed uniformly within a constraint limiting the closeness of approach. For metallic Na with atomic pair potential interactions, it is shown that quenches from stochastic configurations and quenches from equilibrium liquid molecular dynamics configurations produce statistically identical distributions of the structural potential energy. Again for metallic Na, it is shown that DFT quenches from stochastic configurations provide the parameters which calibrate the Hamiltonian. A statistical mechanical analysis shows how the underlying potential properties can be extracted from the distributions found in quenches from stochastic configurations. |
doi_str_mv | 10.1103/PhysRevE.80.051111 |
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This will allow us to apply DFT to the Hamiltonian formulation of atomic motion in monatomic liquids by Wallace [Phys. Rev. E 56, 4179 (1997)]. For a monatomic system, analysis of the potential surface is facilitated by the random and symmetric classification of potential-energy valleys. Since the random valleys are numerically dominant and uniform in their macroscopic potential properties, only a few quenches are necessary to establish these properties. Here we describe an efficient technique for doing this. Quenches are done from easily generated "stochastic" configurations, in which the nuclei are distributed uniformly within a constraint limiting the closeness of approach. For metallic Na with atomic pair potential interactions, it is shown that quenches from stochastic configurations and quenches from equilibrium liquid molecular dynamics configurations produce statistically identical distributions of the structural potential energy. 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For metallic Na with atomic pair potential interactions, it is shown that quenches from stochastic configurations and quenches from equilibrium liquid molecular dynamics configurations produce statistically identical distributions of the structural potential energy. Again for metallic Na, it is shown that DFT quenches from stochastic configurations provide the parameters which calibrate the Hamiltonian. A statistical mechanical analysis shows how the underlying potential properties can be extracted from the distributions found in quenches from stochastic configurations.</description><subject>Computer Simulation</subject><subject>Energy Transfer</subject><subject>Models, Chemical</subject><subject>Models, Statistical</subject><subject>Sodium - chemistry</subject><subject>Solutions - chemistry</subject><subject>Stochastic Processes</subject><issn>1539-3755</issn><issn>1550-2376</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNo9kE9LAzEUxIMotla_gAfJzdPWZJPsJsdS6h8oVETPSzZ9aSO7mzbJCv32bqn1XeYdZobhh9A9JVNKCXt63x7iB_wsppJMiaDDXaAxFYJkOSuLy-PPVMZKIUboJsZvQljOJL9Go5ywgitBx2g1q7HrXHIet5C2fo2tD7jxRifXbbDZ6qBNguBicgbvfIIuOd1k0EHYHHA7ZFuNvcWN2_duHW_RldVNhLs_naCv58Xn_DVbrl7e5rNlZhjJU6Y4AFUSaG5JWRI-TBNWG1YQC2ptLAdeWy5JbUqjlZS5LHRtuKBGGsWVZBP0eOrdBb_vIaaqddFA0-gOfB-rkjEpRKmKwZmfnCb4GAPYaheGzeFQUVIdOVZnjpUk1YnjEHr4q-_rFtb_kTM49gtqjHDC</recordid><startdate>20091101</startdate><enddate>20091101</enddate><creator>Holmström, E</creator><creator>Bock, N</creator><creator>Peery, Travis B</creator><creator>Lizárraga, R</creator><creator>De Lorenzi-Venneri, G</creator><creator>Chisolm, Eric D</creator><creator>Wallace, Duane C</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20091101</creationdate><title>Ab initio method for locating characteristic potential-energy minima of liquids</title><author>Holmström, E ; Bock, N ; Peery, Travis B ; Lizárraga, R ; De Lorenzi-Venneri, G ; Chisolm, Eric D ; Wallace, Duane C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c302t-94ee198e12f077040035fac360fe9dcf4e4bf480bc7ca988286abc451c8c94983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Computer Simulation</topic><topic>Energy Transfer</topic><topic>Models, Chemical</topic><topic>Models, Statistical</topic><topic>Sodium - chemistry</topic><topic>Solutions - chemistry</topic><topic>Stochastic Processes</topic><toplevel>online_resources</toplevel><creatorcontrib>Holmström, E</creatorcontrib><creatorcontrib>Bock, N</creatorcontrib><creatorcontrib>Peery, Travis B</creatorcontrib><creatorcontrib>Lizárraga, R</creatorcontrib><creatorcontrib>De Lorenzi-Venneri, G</creatorcontrib><creatorcontrib>Chisolm, Eric D</creatorcontrib><creatorcontrib>Wallace, Duane C</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Physical review. E, Statistical, nonlinear, and soft matter physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Holmström, E</au><au>Bock, N</au><au>Peery, Travis B</au><au>Lizárraga, R</au><au>De Lorenzi-Venneri, G</au><au>Chisolm, Eric D</au><au>Wallace, Duane C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ab initio method for locating characteristic potential-energy minima of liquids</atitle><jtitle>Physical review. E, Statistical, nonlinear, and soft matter physics</jtitle><addtitle>Phys Rev E Stat Nonlin Soft Matter Phys</addtitle><date>2009-11-01</date><risdate>2009</risdate><volume>80</volume><issue>5 Pt 1</issue><spage>051111</spage><epage>051111</epage><pages>051111-051111</pages><artnum>051111</artnum><issn>1539-3755</issn><eissn>1550-2376</eissn><abstract>It is possible in principle to probe the many-atom potential surface using density functional theory (DFT). This will allow us to apply DFT to the Hamiltonian formulation of atomic motion in monatomic liquids by Wallace [Phys. Rev. E 56, 4179 (1997)]. For a monatomic system, analysis of the potential surface is facilitated by the random and symmetric classification of potential-energy valleys. Since the random valleys are numerically dominant and uniform in their macroscopic potential properties, only a few quenches are necessary to establish these properties. Here we describe an efficient technique for doing this. Quenches are done from easily generated "stochastic" configurations, in which the nuclei are distributed uniformly within a constraint limiting the closeness of approach. For metallic Na with atomic pair potential interactions, it is shown that quenches from stochastic configurations and quenches from equilibrium liquid molecular dynamics configurations produce statistically identical distributions of the structural potential energy. Again for metallic Na, it is shown that DFT quenches from stochastic configurations provide the parameters which calibrate the Hamiltonian. A statistical mechanical analysis shows how the underlying potential properties can be extracted from the distributions found in quenches from stochastic configurations.</abstract><cop>United States</cop><pmid>20364951</pmid><doi>10.1103/PhysRevE.80.051111</doi><tpages>1</tpages></addata></record> |
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subjects | Computer Simulation Energy Transfer Models, Chemical Models, Statistical Sodium - chemistry Solutions - chemistry Stochastic Processes |
title | Ab initio method for locating characteristic potential-energy minima of liquids |
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