Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions
Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately interpolates three-body non-additive interaction dat...
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Veröffentlicht in: | Chemical communications (Cambridge, England) England), 2022-06, Vol.58 (49), p.6898-691 |
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creator | Graham, Richard S Wheatley, Richard J |
description | Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately interpolates three-body non-additive interaction data, using the machine learning technique, Gaussian Processes (GP). The GP approach needs no bespoke modification when the number or type of molecules is changed. Our method produces highly accurate interpolation from significantly fewer training points than typical approaches, meaning
ab initio
calculations can be performed at higher accuracy. As an exemplar we compute the PES for all three-body cross interactions for CO
2
-Ar mixtures. From these we calculate the CO
2
-Ar virial coefficients up to 5th order. The resulting virial equation of state (EoS) is convergent for densities up to the critical density. Where convergent, the EoS makes accurate first-principles predictions for a range of thermophysical properties for CO
2
-Ar mixtures, including the compressibility factor, speed of sound and Joule-Thomson coefficient. Our method has great potential to make wide-ranging first-principles predictions for mixtures of comparably sized molecules. Such predictions can replace the need for expensive, laborious and repetitive experiments and inform the continuum models required for applications.
Via
a generally applicable method, we interpolate
ab initio
calculations of intermolecular interactions and produce successful first-principles predictions. |
doi_str_mv | 10.1039/d2cc01820a |
format | Article |
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ab initio
calculations can be performed at higher accuracy. As an exemplar we compute the PES for all three-body cross interactions for CO
2
-Ar mixtures. From these we calculate the CO
2
-Ar virial coefficients up to 5th order. The resulting virial equation of state (EoS) is convergent for densities up to the critical density. Where convergent, the EoS makes accurate first-principles predictions for a range of thermophysical properties for CO
2
-Ar mixtures, including the compressibility factor, speed of sound and Joule-Thomson coefficient. Our method has great potential to make wide-ranging first-principles predictions for mixtures of comparably sized molecules. Such predictions can replace the need for expensive, laborious and repetitive experiments and inform the continuum models required for applications.
Via
a generally applicable method, we interpolate
ab initio
calculations of intermolecular interactions and produce successful first-principles predictions.</description><identifier>ISSN: 1359-7345</identifier><identifier>EISSN: 1364-548X</identifier><identifier>DOI: 10.1039/d2cc01820a</identifier><identifier>PMID: 35642644</identifier><language>eng</language><publisher>England: Royal Society of Chemistry</publisher><subject>Carbon dioxide ; Compressibility ; Continuum modeling ; Convergence ; Equations of state ; First principles ; Gaussian process ; Interpolation ; Machine learning ; Mixtures ; Potential energy ; Quantum chemistry ; Thermophysical properties ; Thomson coefficient ; Virial coefficients</subject><ispartof>Chemical communications (Cambridge, England), 2022-06, Vol.58 (49), p.6898-691</ispartof><rights>Copyright Royal Society of Chemistry 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-1886a028fc8e4f67272630d19c285b130c7a05d67c07319be96989b90a9d8df63</citedby><cites>FETCH-LOGICAL-c414t-1886a028fc8e4f67272630d19c285b130c7a05d67c07319be96989b90a9d8df63</cites><orcidid>0000-0002-5530-8120 ; 0000-0002-2096-7708</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35642644$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Graham, Richard S</creatorcontrib><creatorcontrib>Wheatley, Richard J</creatorcontrib><title>Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions</title><title>Chemical communications (Cambridge, England)</title><addtitle>Chem Commun (Camb)</addtitle><description>Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately interpolates three-body non-additive interaction data, using the machine learning technique, Gaussian Processes (GP). The GP approach needs no bespoke modification when the number or type of molecules is changed. Our method produces highly accurate interpolation from significantly fewer training points than typical approaches, meaning
ab initio
calculations can be performed at higher accuracy. As an exemplar we compute the PES for all three-body cross interactions for CO
2
-Ar mixtures. From these we calculate the CO
2
-Ar virial coefficients up to 5th order. The resulting virial equation of state (EoS) is convergent for densities up to the critical density. Where convergent, the EoS makes accurate first-principles predictions for a range of thermophysical properties for CO
2
-Ar mixtures, including the compressibility factor, speed of sound and Joule-Thomson coefficient. Our method has great potential to make wide-ranging first-principles predictions for mixtures of comparably sized molecules. Such predictions can replace the need for expensive, laborious and repetitive experiments and inform the continuum models required for applications.
Via
a generally applicable method, we interpolate
ab initio
calculations of intermolecular interactions and produce successful first-principles predictions.</description><subject>Carbon dioxide</subject><subject>Compressibility</subject><subject>Continuum modeling</subject><subject>Convergence</subject><subject>Equations of state</subject><subject>First principles</subject><subject>Gaussian process</subject><subject>Interpolation</subject><subject>Machine learning</subject><subject>Mixtures</subject><subject>Potential energy</subject><subject>Quantum chemistry</subject><subject>Thermophysical properties</subject><subject>Thomson coefficient</subject><subject>Virial coefficients</subject><issn>1359-7345</issn><issn>1364-548X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNpdkc9rFTEQx4NU7A-9eG8J9FIKq_m12aS3x1OrUPGi4G3JS2bblN1km2SF_vfm-Z5PcC4zMJ_5MjNfhN5S8o4Srt87Zi2hihHzAp1QLkXTCvXzaFu3uum4aI_Rac6PpAZt1St0zFspmBTiBIWvxj74AHgEk4IP93iICYcYGuOcL_4XYB8KpCmOYJfRJDzHAqF4M-Yb_LSYUJYJ2weYfC7pGZeIB59yaebkg_XzCBnPCZy3xceQX6OXQ52EN_t8hn58-vh9_bm5-3b7Zb26a6ygojRUKWkIU4NVIAbZsY5JThzVlql2QzmxnSGtk50lHad6A1pqpTeaGO2UGyQ_Q1c73TnFpwVy6et-FsbRBIhL7lnV5FRpTSp6-R_6GJcU6nZbqiOMilZU6npH2RRzTjD09cDJpOeekn7rQv-Brdd_XFhV-GIvuWwmcAf079srcL4DUraH7j8b-W-FyYzh</recordid><startdate>20220616</startdate><enddate>20220616</enddate><creator>Graham, Richard S</creator><creator>Wheatley, Richard J</creator><general>Royal Society of Chemistry</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>L7M</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-5530-8120</orcidid><orcidid>https://orcid.org/0000-0002-2096-7708</orcidid></search><sort><creationdate>20220616</creationdate><title>Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions</title><author>Graham, Richard S ; Wheatley, Richard J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c414t-1886a028fc8e4f67272630d19c285b130c7a05d67c07319be96989b90a9d8df63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Carbon dioxide</topic><topic>Compressibility</topic><topic>Continuum modeling</topic><topic>Convergence</topic><topic>Equations of state</topic><topic>First principles</topic><topic>Gaussian process</topic><topic>Interpolation</topic><topic>Machine learning</topic><topic>Mixtures</topic><topic>Potential energy</topic><topic>Quantum chemistry</topic><topic>Thermophysical properties</topic><topic>Thomson coefficient</topic><topic>Virial coefficients</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Graham, Richard S</creatorcontrib><creatorcontrib>Wheatley, Richard J</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Chemical communications (Cambridge, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Graham, Richard S</au><au>Wheatley, Richard J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions</atitle><jtitle>Chemical communications (Cambridge, England)</jtitle><addtitle>Chem Commun (Camb)</addtitle><date>2022-06-16</date><risdate>2022</risdate><volume>58</volume><issue>49</issue><spage>6898</spage><epage>691</epage><pages>6898-691</pages><issn>1359-7345</issn><eissn>1364-548X</eissn><abstract>Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately interpolates three-body non-additive interaction data, using the machine learning technique, Gaussian Processes (GP). The GP approach needs no bespoke modification when the number or type of molecules is changed. Our method produces highly accurate interpolation from significantly fewer training points than typical approaches, meaning
ab initio
calculations can be performed at higher accuracy. As an exemplar we compute the PES for all three-body cross interactions for CO
2
-Ar mixtures. From these we calculate the CO
2
-Ar virial coefficients up to 5th order. The resulting virial equation of state (EoS) is convergent for densities up to the critical density. Where convergent, the EoS makes accurate first-principles predictions for a range of thermophysical properties for CO
2
-Ar mixtures, including the compressibility factor, speed of sound and Joule-Thomson coefficient. Our method has great potential to make wide-ranging first-principles predictions for mixtures of comparably sized molecules. Such predictions can replace the need for expensive, laborious and repetitive experiments and inform the continuum models required for applications.
Via
a generally applicable method, we interpolate
ab initio
calculations of intermolecular interactions and produce successful first-principles predictions.</abstract><cop>England</cop><pub>Royal Society of Chemistry</pub><pmid>35642644</pmid><doi>10.1039/d2cc01820a</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-5530-8120</orcidid><orcidid>https://orcid.org/0000-0002-2096-7708</orcidid><oa>free_for_read</oa></addata></record> |
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source | Royal Society Of Chemistry Journals; Alma/SFX Local Collection |
subjects | Carbon dioxide Compressibility Continuum modeling Convergence Equations of state First principles Gaussian process Interpolation Machine learning Mixtures Potential energy Quantum chemistry Thermophysical properties Thomson coefficient Virial coefficients |
title | Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions |
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