Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization

Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications,...
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Autor*in:	Sergei Manzhos [verfasserIn] Manabu Ihara [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Neural network Gaussian process regression High-dimensional model representation Potential energy surface

Übergeordnetes Werk:	In: Artificial Intelligence Chemistry - Elsevier, 2023, 1(2023), 2, Seite 100013-
Übergeordnetes Werk:	volume:1 ; year:2023 ; number:2 ; pages:100013-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.aichem.2023.100013

Katalog-ID:	DOAJ099774623

Internformat


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520			\|a Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces.
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publishDate	2023
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allfields_unstemmed	10.1016/j.aichem.2023.100013 doi (DE-627)DOAJ099774623 (DE-599)DOAJ83b49e6c04b7420fbe1a226dad1cfb4f DE-627 ger DE-627 rakwb eng QD1-999 QA75.5-76.95 Sergei Manzhos verfasserin aut Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces. Neural network Gaussian process regression High-dimensional model representation Potential energy surface Chemistry Electronic computers. Computer science Manabu Ihara verfasserin aut In Artificial Intelligence Chemistry Elsevier, 2023 1(2023), 2, Seite 100013- (DE-627)187084498X (DE-600)3172749-9 29497477 nnns volume:1 year:2023 number:2 pages:100013- https://doi.org/10.1016/j.aichem.2023.100013 kostenfrei https://doaj.org/article/83b49e6c04b7420fbe1a226dad1cfb4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2949747723000131 kostenfrei https://doaj.org/toc/2949-7477 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2023 2 100013-
allfieldsGer	10.1016/j.aichem.2023.100013 doi (DE-627)DOAJ099774623 (DE-599)DOAJ83b49e6c04b7420fbe1a226dad1cfb4f DE-627 ger DE-627 rakwb eng QD1-999 QA75.5-76.95 Sergei Manzhos verfasserin aut Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces. Neural network Gaussian process regression High-dimensional model representation Potential energy surface Chemistry Electronic computers. Computer science Manabu Ihara verfasserin aut In Artificial Intelligence Chemistry Elsevier, 2023 1(2023), 2, Seite 100013- (DE-627)187084498X (DE-600)3172749-9 29497477 nnns volume:1 year:2023 number:2 pages:100013- https://doi.org/10.1016/j.aichem.2023.100013 kostenfrei https://doaj.org/article/83b49e6c04b7420fbe1a226dad1cfb4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2949747723000131 kostenfrei https://doaj.org/toc/2949-7477 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2023 2 100013-
allfieldsSound	10.1016/j.aichem.2023.100013 doi (DE-627)DOAJ099774623 (DE-599)DOAJ83b49e6c04b7420fbe1a226dad1cfb4f DE-627 ger DE-627 rakwb eng QD1-999 QA75.5-76.95 Sergei Manzhos verfasserin aut Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces. Neural network Gaussian process regression High-dimensional model representation Potential energy surface Chemistry Electronic computers. Computer science Manabu Ihara verfasserin aut In Artificial Intelligence Chemistry Elsevier, 2023 1(2023), 2, Seite 100013- (DE-627)187084498X (DE-600)3172749-9 29497477 nnns volume:1 year:2023 number:2 pages:100013- https://doi.org/10.1016/j.aichem.2023.100013 kostenfrei https://doaj.org/article/83b49e6c04b7420fbe1a226dad1cfb4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2949747723000131 kostenfrei https://doaj.org/toc/2949-7477 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2023 2 100013-
language	English
source	In Artificial Intelligence Chemistry 1(2023), 2, Seite 100013- volume:1 year:2023 number:2 pages:100013-
sourceStr	In Artificial Intelligence Chemistry 1(2023), 2, Seite 100013- volume:1 year:2023 number:2 pages:100013-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Neural network Gaussian process regression High-dimensional model representation Potential energy surface Chemistry Electronic computers. Computer science
isfreeaccess_bool	true
container_title	Artificial Intelligence Chemistry
authorswithroles_txt_mv	Sergei Manzhos @@aut@@ Manabu Ihara @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	187084498X
id	DOAJ099774623
language_de	englisch
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callnumber-first	Q - Science
author	Sergei Manzhos
spellingShingle	Sergei Manzhos misc QD1-999 misc QA75.5-76.95 misc Neural network misc Gaussian process regression misc High-dimensional model representation misc Potential energy surface misc Chemistry misc Electronic computers. Computer science Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
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topic_title	QD1-999 QA75.5-76.95 Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization Neural network Gaussian process regression High-dimensional model representation Potential energy surface
topic	misc QD1-999 misc QA75.5-76.95 misc Neural network misc Gaussian process regression misc High-dimensional model representation misc Potential energy surface misc Chemistry misc Electronic computers. Computer science
topic_unstemmed	misc QD1-999 misc QA75.5-76.95 misc Neural network misc Gaussian process regression misc High-dimensional model representation misc Potential energy surface misc Chemistry misc Electronic computers. Computer science
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title	Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
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title_full	Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
author_sort	Sergei Manzhos
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author_browse	Sergei Manzhos Manabu Ihara
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title_sort	orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
callnumber	QD1-999
title_auth	Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
abstract	Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces.
abstractGer	Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces.
abstract_unstemmed	Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces.
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title_short	Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
url	https://doi.org/10.1016/j.aichem.2023.100013 https://doaj.org/article/83b49e6c04b7420fbe1a226dad1cfb4f http://www.sciencedirect.com/science/article/pii/S2949747723000131 https://doaj.org/toc/2949-7477
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Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization

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