Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence
A new Reverse Monte Carlo (RMC) package “fullrmc” for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programmin...
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Veröffentlicht in: | Journal of computational chemistry 2016-05, Vol.37 (12), p.1102-1111 |
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description | A new Reverse Monte Carlo (RMC) package “fullrmc” for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group. © 2016 Wiley Periodicals, Inc.
fullrmc is a Reverse Monte Carlo package designed with artificial intelligence to create atomic and molecular models from a set of experimental data and constraints. fullrmc class hierarchy and implementation are quite innovative, allowing easy setup for almost any kind of reverse modeling engine for all sorts of applications. Concepts such as Group, Group‐Selector, and MoveGenerator and RMC modeling modes (recurring, refining, and exploring) stand out from all other existing RMC software and packages. |
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fullrmc is a Reverse Monte Carlo package designed with artificial intelligence to create atomic and molecular models from a set of experimental data and constraints. fullrmc class hierarchy and implementation are quite innovative, allowing easy setup for almost any kind of reverse modeling engine for all sorts of applications. Concepts such as Group, Group‐Selector, and MoveGenerator and RMC modeling modes (recurring, refining, and exploring) stand out from all other existing RMC software and packages.</description><identifier>ISSN: 0192-8651</identifier><identifier>EISSN: 1096-987X</identifier><identifier>DOI: 10.1002/jcc.24304</identifier><identifier>PMID: 26800289</identifier><identifier>CODEN: JCCHDD</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Amorphous materials ; Artificial intelligence ; Atomic structure ; Chemistry ; Computer simulation ; Consistency ; Crystal structure ; Customization ; Experimental data ; Expert systems ; Machine learning ; Materials selection ; Mathematical models ; MATHEMATICS AND COMPUTING ; modeling ; modelling ; Molecular structure ; Monte Carlo simulation ; pair distribution function ; Programming languages ; Reinforcement ; reverse Monte Carlo ; rigid body ; Software ; Uniqueness</subject><ispartof>Journal of computational chemistry, 2016-05, Vol.37 (12), p.1102-1111</ispartof><rights>2016 Wiley Periodicals, Inc.</rights><rights>Copyright Wiley Subscription Services, Inc. May 5, 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5514-39acd91c15e124838f59bda4ffe4b17ccc6121ef6c815374823b992642b5544d3</citedby><cites>FETCH-LOGICAL-c5514-39acd91c15e124838f59bda4ffe4b17ccc6121ef6c815374823b992642b5544d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjcc.24304$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjcc.24304$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26800289$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1332980$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Aoun, Bachir</creatorcontrib><creatorcontrib>Argonne National Laboratory (ANL), Argonne, IL (United States)</creatorcontrib><title>Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence</title><title>Journal of computational chemistry</title><addtitle>J. Comput. Chem</addtitle><description>A new Reverse Monte Carlo (RMC) package “fullrmc” for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group. © 2016 Wiley Periodicals, Inc.
fullrmc is a Reverse Monte Carlo package designed with artificial intelligence to create atomic and molecular models from a set of experimental data and constraints. fullrmc class hierarchy and implementation are quite innovative, allowing easy setup for almost any kind of reverse modeling engine for all sorts of applications. Concepts such as Group, Group‐Selector, and MoveGenerator and RMC modeling modes (recurring, refining, and exploring) stand out from all other existing RMC software and packages.</description><subject>Algorithms</subject><subject>Amorphous materials</subject><subject>Artificial intelligence</subject><subject>Atomic structure</subject><subject>Chemistry</subject><subject>Computer simulation</subject><subject>Consistency</subject><subject>Crystal structure</subject><subject>Customization</subject><subject>Experimental data</subject><subject>Expert systems</subject><subject>Machine learning</subject><subject>Materials selection</subject><subject>Mathematical models</subject><subject>MATHEMATICS AND COMPUTING</subject><subject>modeling</subject><subject>modelling</subject><subject>Molecular structure</subject><subject>Monte Carlo simulation</subject><subject>pair distribution function</subject><subject>Programming languages</subject><subject>Reinforcement</subject><subject>reverse Monte Carlo</subject><subject>rigid body</subject><subject>Software</subject><subject>Uniqueness</subject><issn>0192-8651</issn><issn>1096-987X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp90c1uEzEUBWALgWgJLHgBZMEGJKb174y9RBENRFURUhHdWR7PncSpxxPsCSVvj0vaLpBgZS--e2Tfg9BLSk4oIex049wJE5yIR-iYEl1XWjVXj9ExoZpVqpb0CD3LeUMI4bIWT9ERq1WZU_oYTWe7ENLg3mOLk1_5Drdjt8cJfkLKgIcxToCdTWEs9w6Cjyu8te7argBDtG2ADt_4aY0H69Y-Ag5gU7xVNnbYpsn33nkbsC9BIfgVRAfP0ZPehgwv7s4Z-nb28XL-qTr_svg8_3BeOSmpqLi2rtPUUQmUCcVVL3XbWdH3IFraOOdqyij0tVNU8kYoxlutWS1YK6UQHZ-h14fcMU_eZOcncGs3xghuMpRzphUp6O0BbdP4Ywd5MoPPrrzVRhh32dBGEUlZ2WKhb_6im3GXYvmCoZo0nPKm5v9VjRKiVk0pa4beHZRLY84JerNNfrBpbygxt62a0qr502qxr-4Sd-0A3YO8r7GA0wO48QH2_04yy_n8PrI6TPg8wa-HCZuuTd3wRprvFwtztbz8ulzIhbngvwGs0rkS</recordid><startdate>20160505</startdate><enddate>20160505</enddate><creator>Aoun, Bachir</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><general>Wiley</general><scope>BSCLL</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>7X8</scope><scope>OIOZB</scope><scope>OTOTI</scope></search><sort><creationdate>20160505</creationdate><title>Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence</title><author>Aoun, Bachir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5514-39acd91c15e124838f59bda4ffe4b17ccc6121ef6c815374823b992642b5544d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Amorphous materials</topic><topic>Artificial intelligence</topic><topic>Atomic structure</topic><topic>Chemistry</topic><topic>Computer simulation</topic><topic>Consistency</topic><topic>Crystal structure</topic><topic>Customization</topic><topic>Experimental data</topic><topic>Expert systems</topic><topic>Machine learning</topic><topic>Materials selection</topic><topic>Mathematical models</topic><topic>MATHEMATICS AND COMPUTING</topic><topic>modeling</topic><topic>modelling</topic><topic>Molecular structure</topic><topic>Monte Carlo simulation</topic><topic>pair distribution function</topic><topic>Programming languages</topic><topic>Reinforcement</topic><topic>reverse Monte Carlo</topic><topic>rigid body</topic><topic>Software</topic><topic>Uniqueness</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aoun, Bachir</creatorcontrib><creatorcontrib>Argonne National Laboratory (ANL), Argonne, IL (United States)</creatorcontrib><collection>Istex</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>MEDLINE - Academic</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><jtitle>Journal of computational chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aoun, Bachir</au><aucorp>Argonne National Laboratory (ANL), Argonne, IL (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence</atitle><jtitle>Journal of computational chemistry</jtitle><addtitle>J. Comput. Chem</addtitle><date>2016-05-05</date><risdate>2016</risdate><volume>37</volume><issue>12</issue><spage>1102</spage><epage>1111</epage><pages>1102-1111</pages><issn>0192-8651</issn><eissn>1096-987X</eissn><coden>JCCHDD</coden><abstract>A new Reverse Monte Carlo (RMC) package “fullrmc” for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group. © 2016 Wiley Periodicals, Inc.
fullrmc is a Reverse Monte Carlo package designed with artificial intelligence to create atomic and molecular models from a set of experimental data and constraints. fullrmc class hierarchy and implementation are quite innovative, allowing easy setup for almost any kind of reverse modeling engine for all sorts of applications. Concepts such as Group, Group‐Selector, and MoveGenerator and RMC modeling modes (recurring, refining, and exploring) stand out from all other existing RMC software and packages.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>26800289</pmid><doi>10.1002/jcc.24304</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Amorphous materials Artificial intelligence Atomic structure Chemistry Computer simulation Consistency Crystal structure Customization Experimental data Expert systems Machine learning Materials selection Mathematical models MATHEMATICS AND COMPUTING modeling modelling Molecular structure Monte Carlo simulation pair distribution function Programming languages Reinforcement reverse Monte Carlo rigid body Software Uniqueness |
title | Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence |
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