Loading…
Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials
Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a sys...
Saved in:
Published in: | The Journal of chemical physics 2023-04, Vol.158 (14), p.144112-144112 |
---|---|
Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c410t-c0de1d208f8b60f22b17d3e86af4f44d6e027d218e8f0ba9e4a94e4f1ac402d53 |
---|---|
cites | cdi_FETCH-LOGICAL-c410t-c0de1d208f8b60f22b17d3e86af4f44d6e027d218e8f0ba9e4a94e4f1ac402d53 |
container_end_page | 144112 |
container_issue | 14 |
container_start_page | 144112 |
container_title | The Journal of chemical physics |
container_volume | 158 |
creator | Goldman, Nir Fried, Laurence E. Lindsey, Rebecca K. Pham, C. Huy Dettori, R. |
description | Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme 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 | 10.1063/5.0141616 |
format | article |
fullrecord | <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_scitation_primary_10_1063_5_0141616</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2801976035</sourcerecordid><originalsourceid>FETCH-LOGICAL-c410t-c0de1d208f8b60f22b17d3e86af4f44d6e027d218e8f0ba9e4a94e4f1ac402d53</originalsourceid><addsrcrecordid>eNp9kcFu1DAURS0EokNhwQ8gCzZQKeXZ8TjxshoNtFJRF4V15NjPE1eJPY2dRf6eRDOwYMHqbs49enqXkPcMrhnI8uv2GphgkskXZMOgVkUlFbwkGwDOCiVBXpA3KT0BAKu4eE0uygokE5XakOd96HQwPhxo7pBqY6ZRm5lGRy2G5PNM3RRM9jHonmZ_6DJtfbBrYYgW-7T0xjgdOrrr7n7sH-mgw1y00c7Uh4yLbO3SY8wYstd9ekteuSXw3Tkvya9v-5-72-L-4fvd7ua-MIJBLgxYZJZD7epWguO8ZZUtsZbaCSeElQi8spzVWDtotUKhlUDhmDYCuN2Wl-TjyRtT9k0yPqPpTAwBTW44B644W6DPJ-g4xucJU24Gnwz2vQ4Yp9TwGpiqJJSr79M_6FOcxuUrC1UpVVeLc6W-nCgzxpRGdM1x9IMe54ZBs47VbJvzWAv74Wyc2gHtX_LPOgtwdQLW6_X6x__YfgPRXpwc</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2799872205</pqid></control><display><type>article</type><title>Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials</title><source>American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)</source><source>American Institute of Physics</source><creator>Goldman, Nir ; Fried, Laurence E. ; Lindsey, Rebecca K. ; Pham, C. Huy ; Dettori, R.</creator><creatorcontrib>Goldman, Nir ; Fried, Laurence E. ; Lindsey, Rebecca K. ; Pham, C. Huy ; Dettori, R. ; Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)</creatorcontrib><description>Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme 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>ISSN: 0021-9606</identifier><identifier>EISSN: 1089-7690</identifier><identifier>DOI: 10.1063/5.0141616</identifier><identifier>PMID: 37061479</identifier><identifier>CODEN: JCPSA6</identifier><language>eng</language><publisher>United States: American Institute of Physics</publisher><subject>Accuracy ; Binding ; Chebyshev approximation ; Chebyshev functions ; Chemical properties ; Computational efficiency ; computer simulation ; Density ; density-functional tight-binding ; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY ; Interaction models ; machine learning ; Many body problem ; Neural networks ; Organic chemistry ; quantum mechanical calculations ; quantum mechanical systems and processes ; Simulation</subject><ispartof>The Journal of chemical physics, 2023-04, Vol.158 (14), p.144112-144112</ispartof><rights>Author(s)</rights><rights>2023 Author(s). Published under an exclusive license by AIP Publishing.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c410t-c0de1d208f8b60f22b17d3e86af4f44d6e027d218e8f0ba9e4a94e4f1ac402d53</citedby><cites>FETCH-LOGICAL-c410t-c0de1d208f8b60f22b17d3e86af4f44d6e027d218e8f0ba9e4a94e4f1ac402d53</cites><orcidid>0000-0002-9437-7700 ; 0000-0002-9465-9361 ; 0000-0002-3438-9064 ; 0000-0002-4678-1098 ; 0000-0003-3052-2128 ; 0000000246781098 ; 0000000294377700 ; 0000000294659361 ; 0000000330522128 ; 0000000234389064</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/jcp/article-lookup/doi/10.1063/5.0141616$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>230,314,777,779,781,792,882,27905,27906,76132</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37061479$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/2202921$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Goldman, Nir</creatorcontrib><creatorcontrib>Fried, Laurence E.</creatorcontrib><creatorcontrib>Lindsey, Rebecca K.</creatorcontrib><creatorcontrib>Pham, C. Huy</creatorcontrib><creatorcontrib>Dettori, R.</creatorcontrib><creatorcontrib>Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)</creatorcontrib><title>Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials</title><title>The Journal of chemical physics</title><addtitle>J Chem Phys</addtitle><description>Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme 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>Accuracy</subject><subject>Binding</subject><subject>Chebyshev approximation</subject><subject>Chebyshev functions</subject><subject>Chemical properties</subject><subject>Computational efficiency</subject><subject>computer simulation</subject><subject>Density</subject><subject>density-functional tight-binding</subject><subject>INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY</subject><subject>Interaction models</subject><subject>machine learning</subject><subject>Many body problem</subject><subject>Neural networks</subject><subject>Organic chemistry</subject><subject>quantum mechanical calculations</subject><subject>quantum mechanical systems and processes</subject><subject>Simulation</subject><issn>0021-9606</issn><issn>1089-7690</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kcFu1DAURS0EokNhwQ8gCzZQKeXZ8TjxshoNtFJRF4V15NjPE1eJPY2dRf6eRDOwYMHqbs49enqXkPcMrhnI8uv2GphgkskXZMOgVkUlFbwkGwDOCiVBXpA3KT0BAKu4eE0uygokE5XakOd96HQwPhxo7pBqY6ZRm5lGRy2G5PNM3RRM9jHonmZ_6DJtfbBrYYgW-7T0xjgdOrrr7n7sH-mgw1y00c7Uh4yLbO3SY8wYstd9ekteuSXw3Tkvya9v-5-72-L-4fvd7ua-MIJBLgxYZJZD7epWguO8ZZUtsZbaCSeElQi8spzVWDtotUKhlUDhmDYCuN2Wl-TjyRtT9k0yPqPpTAwBTW44B644W6DPJ-g4xucJU24Gnwz2vQ4Yp9TwGpiqJJSr79M_6FOcxuUrC1UpVVeLc6W-nCgzxpRGdM1x9IMe54ZBs47VbJvzWAv74Wyc2gHtX_LPOgtwdQLW6_X6x__YfgPRXpwc</recordid><startdate>20230414</startdate><enddate>20230414</enddate><creator>Goldman, Nir</creator><creator>Fried, Laurence E.</creator><creator>Lindsey, Rebecca K.</creator><creator>Pham, C. Huy</creator><creator>Dettori, R.</creator><general>American Institute of Physics</general><general>American Institute of Physics (AIP)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>7X8</scope><scope>OIOZB</scope><scope>OTOTI</scope><orcidid>https://orcid.org/0000-0002-9437-7700</orcidid><orcidid>https://orcid.org/0000-0002-9465-9361</orcidid><orcidid>https://orcid.org/0000-0002-3438-9064</orcidid><orcidid>https://orcid.org/0000-0002-4678-1098</orcidid><orcidid>https://orcid.org/0000-0003-3052-2128</orcidid><orcidid>https://orcid.org/0000000246781098</orcidid><orcidid>https://orcid.org/0000000294377700</orcidid><orcidid>https://orcid.org/0000000294659361</orcidid><orcidid>https://orcid.org/0000000330522128</orcidid><orcidid>https://orcid.org/0000000234389064</orcidid></search><sort><creationdate>20230414</creationdate><title>Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials</title><author>Goldman, Nir ; Fried, Laurence E. ; Lindsey, Rebecca K. ; Pham, C. Huy ; Dettori, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c410t-c0de1d208f8b60f22b17d3e86af4f44d6e027d218e8f0ba9e4a94e4f1ac402d53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Binding</topic><topic>Chebyshev approximation</topic><topic>Chebyshev functions</topic><topic>Chemical properties</topic><topic>Computational efficiency</topic><topic>computer simulation</topic><topic>Density</topic><topic>density-functional tight-binding</topic><topic>INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY</topic><topic>Interaction models</topic><topic>machine learning</topic><topic>Many body problem</topic><topic>Neural networks</topic><topic>Organic chemistry</topic><topic>quantum mechanical calculations</topic><topic>quantum mechanical systems and processes</topic><topic>Simulation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goldman, Nir</creatorcontrib><creatorcontrib>Fried, Laurence E.</creatorcontrib><creatorcontrib>Lindsey, Rebecca K.</creatorcontrib><creatorcontrib>Pham, C. Huy</creatorcontrib><creatorcontrib>Dettori, R.</creatorcontrib><creatorcontrib>Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><jtitle>The Journal of chemical physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goldman, Nir</au><au>Fried, Laurence E.</au><au>Lindsey, Rebecca K.</au><au>Pham, C. Huy</au><au>Dettori, R.</au><aucorp>Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials</atitle><jtitle>The Journal of chemical physics</jtitle><addtitle>J Chem Phys</addtitle><date>2023-04-14</date><risdate>2023</risdate><volume>158</volume><issue>14</issue><spage>144112</spage><epage>144112</epage><pages>144112-144112</pages><issn>0021-9606</issn><eissn>1089-7690</eissn><coden>JCPSA6</coden><abstract>Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme 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.</abstract><cop>United States</cop><pub>American Institute of Physics</pub><pmid>37061479</pmid><doi>10.1063/5.0141616</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-9437-7700</orcidid><orcidid>https://orcid.org/0000-0002-9465-9361</orcidid><orcidid>https://orcid.org/0000-0002-3438-9064</orcidid><orcidid>https://orcid.org/0000-0002-4678-1098</orcidid><orcidid>https://orcid.org/0000-0003-3052-2128</orcidid><orcidid>https://orcid.org/0000000246781098</orcidid><orcidid>https://orcid.org/0000000294377700</orcidid><orcidid>https://orcid.org/0000000294659361</orcidid><orcidid>https://orcid.org/0000000330522128</orcidid><orcidid>https://orcid.org/0000000234389064</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0021-9606 |
ispartof | The Journal of chemical physics, 2023-04, Vol.158 (14), p.144112-144112 |
issn | 0021-9606 1089-7690 |
language | eng |
recordid | cdi_scitation_primary_10_1063_5_0141616 |
source | American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list); American Institute of Physics |
subjects | Accuracy Binding Chebyshev approximation Chebyshev functions Chemical properties Computational efficiency computer simulation Density density-functional tight-binding INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY Interaction models machine learning Many body problem Neural networks Organic chemistry quantum mechanical calculations quantum mechanical systems and processes Simulation |
title | Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T12%3A22%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Enhancing%20the%20accuracy%20of%20density%20functional%20tight%20binding%20models%20through%20ChIMES%20many-body%20interaction%20potentials&rft.jtitle=The%20Journal%20of%20chemical%20physics&rft.au=Goldman,%20Nir&rft.aucorp=Lawrence%20Livermore%20National%20Laboratory%20(LLNL),%20Livermore,%20CA%20(United%20States)&rft.date=2023-04-14&rft.volume=158&rft.issue=14&rft.spage=144112&rft.epage=144112&rft.pages=144112-144112&rft.issn=0021-9606&rft.eissn=1089-7690&rft.coden=JCPSA6&rft_id=info:doi/10.1063/5.0141616&rft_dat=%3Cproquest_scita%3E2801976035%3C/proquest_scita%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c410t-c0de1d208f8b60f22b17d3e86af4f44d6e027d218e8f0ba9e4a94e4f1ac402d53%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2799872205&rft_id=info:pmid/37061479&rfr_iscdi=true |