Communication: Inverse design for self-assembly via on-the-fly optimization
Inverse methods of statistical mechanics have facilitated the discovery of pair potentials that stabilize a wide variety of targeted lattices at zero temperature. However, such methods are complicated by the need to compare, within the optimization framework, the energy of the desired lattice to all...
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Veröffentlicht in: | The Journal of chemical physics 2016-09, Vol.145 (11) |
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creator | Lindquist, Beth A. Jadrich, Ryan B. Truskett, Thomas M. |
description | Inverse methods of statistical mechanics have facilitated the discovery of pair potentials that stabilize a wide variety of targeted lattices at zero temperature. However, such methods are complicated by the need to compare, within the optimization framework, the energy of the desired lattice to all possibly relevant competing structures, which are not generally known in advance. Furthermore, ground-state stability does not guarantee that the target will readily assemble from the fluid upon cooling from higher temperature. Here, we introduce a molecular dynamics simulation-based, optimization design strategy that iteratively and systematically refines the pair interaction according to the fluid and crystalline structural ensembles encountered during the assembly process. We successfully apply this probabilistic, machine-learning approach to the design of repulsive, isotropic pair potentials that assemble into honeycomb, kagome, square, rectangular, truncated square, and truncated hexagonal lattices. |
doi_str_mv | 10.1063/1.4962754 |
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However, such methods are complicated by the need to compare, within the optimization framework, the energy of the desired lattice to all possibly relevant competing structures, which are not generally known in advance. Furthermore, ground-state stability does not guarantee that the target will readily assemble from the fluid upon cooling from higher temperature. Here, we introduce a molecular dynamics simulation-based, optimization design strategy that iteratively and systematically refines the pair interaction according to the fluid and crystalline structural ensembles encountered during the assembly process. 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However, such methods are complicated by the need to compare, within the optimization framework, the energy of the desired lattice to all possibly relevant competing structures, which are not generally known in advance. Furthermore, ground-state stability does not guarantee that the target will readily assemble from the fluid upon cooling from higher temperature. Here, we introduce a molecular dynamics simulation-based, optimization design strategy that iteratively and systematically refines the pair interaction according to the fluid and crystalline structural ensembles encountered during the assembly process. We successfully apply this probabilistic, machine-learning approach to the design of repulsive, isotropic pair potentials that assemble into honeycomb, kagome, square, rectangular, truncated square, and truncated hexagonal lattices.</description><subject>Design optimization</subject><subject>Honeycomb construction</subject><subject>Inverse design</subject><subject>Lattices</subject><subject>Machine learning</subject><subject>Molecular dynamics</subject><subject>Self-assembly</subject><subject>Statistical analysis</subject><subject>Statistical mechanics</subject><subject>Statistical methods</subject><issn>0021-9606</issn><issn>1089-7690</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqd0EtLAzEQB_AgCtbqwW-w4EkhNe9svEnxUSx40XNI89CU7mZNtoX66V3bgndPw8BvZpg_AJcYTTAS9BZPmBJEcnYERhjVCkqh0DEYIUQwVAKJU3BWyhIhhCVhI_AyTU2zbqM1fUztXTVrNz4XXzlf4kdbhZSr4lcBmlJ8s1htq000VWph_-lhGNrU9bGJ37vpc3ASzKr4i0Mdg_fHh7fpM5y_Ps2m93NoGal7aEzNkHQL6Sx3HCtubFhgISiRznFDheQc11T4QIKRjNaKOcy8cYpba5mhY3C139vl9LX2pdfLtM7tcFITTLBgVEkyqOu9sjmVkn3QXY6NyVuNkf7NSmN9yGqwN3tbbOx3v_wPb1L-g7pzgf4AOX932w</recordid><startdate>20160921</startdate><enddate>20160921</enddate><creator>Lindquist, Beth A.</creator><creator>Jadrich, Ryan B.</creator><creator>Truskett, Thomas M.</creator><general>American Institute of Physics</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-6607-6468</orcidid></search><sort><creationdate>20160921</creationdate><title>Communication: Inverse design for self-assembly via on-the-fly optimization</title><author>Lindquist, Beth A. ; Jadrich, Ryan B. ; Truskett, Thomas M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c428t-aa8407db7dc5d5195acfb166327dd5a367551836ef2fa743894d14ead95ccc4a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Design optimization</topic><topic>Honeycomb construction</topic><topic>Inverse design</topic><topic>Lattices</topic><topic>Machine learning</topic><topic>Molecular dynamics</topic><topic>Self-assembly</topic><topic>Statistical analysis</topic><topic>Statistical mechanics</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lindquist, Beth A.</creatorcontrib><creatorcontrib>Jadrich, Ryan B.</creatorcontrib><creatorcontrib>Truskett, Thomas M.</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>The Journal of chemical physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lindquist, Beth A.</au><au>Jadrich, Ryan B.</au><au>Truskett, Thomas M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Communication: Inverse design for self-assembly via on-the-fly optimization</atitle><jtitle>The Journal of chemical physics</jtitle><date>2016-09-21</date><risdate>2016</risdate><volume>145</volume><issue>11</issue><issn>0021-9606</issn><eissn>1089-7690</eissn><coden>JCPSA6</coden><abstract>Inverse methods of statistical mechanics have facilitated the discovery of pair potentials that stabilize a wide variety of targeted lattices at zero temperature. 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subjects | Design optimization Honeycomb construction Inverse design Lattices Machine learning Molecular dynamics Self-assembly Statistical analysis Statistical mechanics Statistical methods |
title | Communication: Inverse design for self-assembly via on-the-fly optimization |
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