Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields
Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of inte...
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creator | Goldman, Nir Kweon, Kyoung Eun Sadigh, Babak Heo, Tae Wook Lindsey, Rebecca K Pham, C Huy Fried, Laurence E Aradi, Bálint Holliday, Kiel Jeffries, Jason R Wood, Brandon C |
description | Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH\(_2\) as a model system and show that a relatively small training set based on unit-cell sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield accurate DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results. |
doi_str_mv | 10.48550/arxiv.2102.03668 |
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However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH\(_2\) as a model system and show that a relatively small training set based on unit-cell sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield accurate DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2102.03668</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Binding ; Chebyshev approximation ; Chemical properties ; Condensed matter physics ; Density functional theory ; Electron states ; Many body interactions ; Molecular dynamics ; Physics - Materials Science ; Polynomials ; Surface properties ; Unit cell ; Workflow</subject><ispartof>arXiv.org, 2021-04</ispartof><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27924</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2102.03668$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1021/acs.jctc.1c00172$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Goldman, Nir</creatorcontrib><creatorcontrib>Kweon, Kyoung Eun</creatorcontrib><creatorcontrib>Sadigh, Babak</creatorcontrib><creatorcontrib>Heo, Tae Wook</creatorcontrib><creatorcontrib>Lindsey, Rebecca K</creatorcontrib><creatorcontrib>Pham, C Huy</creatorcontrib><creatorcontrib>Fried, Laurence E</creatorcontrib><creatorcontrib>Aradi, Bálint</creatorcontrib><creatorcontrib>Holliday, Kiel</creatorcontrib><creatorcontrib>Jeffries, Jason R</creatorcontrib><creatorcontrib>Wood, Brandon C</creatorcontrib><title>Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields</title><title>arXiv.org</title><description>Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH\(_2\) as a model system and show that a relatively small training set based on unit-cell sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield accurate DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.</description><subject>Binding</subject><subject>Chebyshev approximation</subject><subject>Chemical properties</subject><subject>Condensed matter physics</subject><subject>Density functional theory</subject><subject>Electron states</subject><subject>Many body interactions</subject><subject>Molecular dynamics</subject><subject>Physics - Materials Science</subject><subject>Polynomials</subject><subject>Surface properties</subject><subject>Unit cell</subject><subject>Workflow</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotkE1Lw0AQhhdBsNT-AE8ueE7d7Ec-jjWaKlQUzD1sspNkS5Ktu0kxB_-7aetcBuZ9mGEehO58suaREORR2h99XFOf0DVhQRBdoQVlzPciTukNWjm3J4TQIKRCsAX6_YJOe5txMJ0cQOHEghy06bGp8DP0Tg8TTse-PM1kizNdNwN-0r3SfY3fjYLW4ayxZqwbvIMjWFmfkqSBYnINHPGnaafedFq2XiHdfCE1tgScamiVu0XXlWwdrP77EmXpS5a8eruP7Vuy2XlS0MgLFeGhqlQQSR4GLBa-kjHQuFRcAONBXNGC-yErxUwIUgpWqLmgrCQr_ThkS3R_WXt2kx-s7qSd8pOj_OxoJh4uxMGa7xHckO_NaOePXU55FAacchqxP5fMa5A</recordid><startdate>20210414</startdate><enddate>20210414</enddate><creator>Goldman, Nir</creator><creator>Kweon, Kyoung Eun</creator><creator>Sadigh, Babak</creator><creator>Heo, Tae Wook</creator><creator>Lindsey, Rebecca K</creator><creator>Pham, C Huy</creator><creator>Fried, Laurence E</creator><creator>Aradi, Bálint</creator><creator>Holliday, Kiel</creator><creator>Jeffries, Jason R</creator><creator>Wood, Brandon C</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>GOX</scope></search><sort><creationdate>20210414</creationdate><title>Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields</title><author>Goldman, Nir ; Kweon, Kyoung Eun ; Sadigh, Babak ; Heo, Tae Wook ; Lindsey, Rebecca K ; Pham, C Huy ; Fried, Laurence E ; Aradi, Bálint ; Holliday, Kiel ; Jeffries, Jason R ; Wood, Brandon C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a528-7d047dfd68a4763951da9e29cd45e3469f2b4173c568a50c53bddddecfa3c1973</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Binding</topic><topic>Chebyshev approximation</topic><topic>Chemical properties</topic><topic>Condensed matter physics</topic><topic>Density functional theory</topic><topic>Electron states</topic><topic>Many body interactions</topic><topic>Molecular dynamics</topic><topic>Physics - Materials Science</topic><topic>Polynomials</topic><topic>Surface properties</topic><topic>Unit cell</topic><topic>Workflow</topic><toplevel>online_resources</toplevel><creatorcontrib>Goldman, Nir</creatorcontrib><creatorcontrib>Kweon, Kyoung Eun</creatorcontrib><creatorcontrib>Sadigh, Babak</creatorcontrib><creatorcontrib>Heo, Tae Wook</creatorcontrib><creatorcontrib>Lindsey, Rebecca K</creatorcontrib><creatorcontrib>Pham, C Huy</creatorcontrib><creatorcontrib>Fried, Laurence E</creatorcontrib><creatorcontrib>Aradi, Bálint</creatorcontrib><creatorcontrib>Holliday, Kiel</creatorcontrib><creatorcontrib>Jeffries, Jason R</creatorcontrib><creatorcontrib>Wood, Brandon C</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goldman, Nir</au><au>Kweon, Kyoung Eun</au><au>Sadigh, Babak</au><au>Heo, Tae Wook</au><au>Lindsey, Rebecca K</au><au>Pham, C Huy</au><au>Fried, Laurence E</au><au>Aradi, Bálint</au><au>Holliday, Kiel</au><au>Jeffries, Jason R</au><au>Wood, Brandon C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields</atitle><jtitle>arXiv.org</jtitle><date>2021-04-14</date><risdate>2021</risdate><eissn>2331-8422</eissn><abstract>Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH\(_2\) as a model system and show that a relatively small training set based on unit-cell sized calculations yields a model accurate for both bulk and surface properties. 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subjects | Binding Chebyshev approximation Chemical properties Condensed matter physics Density functional theory Electron states Many body interactions Molecular dynamics Physics - Materials Science Polynomials Surface properties Unit cell Workflow |
title | Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields |
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