Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods

The molecular requirements are explored for achieving efficient signal up-conversion in a recently developed technique for terahertz (THz) detection based on molecular optomechanics. We discuss which molecular and spectroscopic properties are most important for predicting efficient THz detection and...

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Autores principales:	Zsuzsanna Koczor-Benda, Alexandra L. Boehmke, Angelos Xomalis, Rakesh Arul, Charlie Readman, Jeremy J. Baumberg, Edina Rosta
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/68982b87eb8146e18c83f0c2c21fab1c
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spelling	oai:doaj.org-article:68982b87eb8146e18c83f0c2c21fab1c2021-11-18T15:33:37ZMolecular Screening for Terahertz Detection with Machine-Learning-Based Methods10.1103/PhysRevX.11.0410352160-3308https://doaj.org/article/68982b87eb8146e18c83f0c2c21fab1c2021-11-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.11.041035http://doi.org/10.1103/PhysRevX.11.041035https://doaj.org/toc/2160-3308The molecular requirements are explored for achieving efficient signal up-conversion in a recently developed technique for terahertz (THz) detection based on molecular optomechanics. We discuss which molecular and spectroscopic properties are most important for predicting efficient THz detection and outline a computational approach based on quantum-chemistry and machine-learning methods for calculating these properties. We validate this approach by bulk and surface-enhanced Raman scattering and infrared absorption measurements. We develop a virtual screening methodology performed on databases of millions of commercially available compounds. Quantum-chemistry calculations for about 3000 compounds are complemented by machine-learning methods to predict applicability of 93 000 organic molecules for detection. Training is performed on vibrational spectroscopic properties based on absorption and Raman scattering intensities. Our top molecules have conversion intensity two orders of magnitude higher than an average molecule from the database. We also discuss how other properties like molecular shape and self-assembling properties influence the detection efficiency. We identify molecular moieties whose presence in the molecules indicates high activity for THz detection and show an example where a simple modification of a frequently used self-assembling compound can enhance activity 85-fold. The capabilities of our screening method are demonstrated on narrow-band and broadband detection examples, and its possible applications in surface-enhanced spectroscopy are also discussed.Zsuzsanna Koczor-BendaAlexandra L. BoehmkeAngelos XomalisRakesh ArulCharlie ReadmanJeremy J. BaumbergEdina RostaAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 11, Iss 4, p 041035 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Zsuzsanna Koczor-Benda Alexandra L. Boehmke Angelos Xomalis Rakesh Arul Charlie Readman Jeremy J. Baumberg Edina Rosta Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods
description	The molecular requirements are explored for achieving efficient signal up-conversion in a recently developed technique for terahertz (THz) detection based on molecular optomechanics. We discuss which molecular and spectroscopic properties are most important for predicting efficient THz detection and outline a computational approach based on quantum-chemistry and machine-learning methods for calculating these properties. We validate this approach by bulk and surface-enhanced Raman scattering and infrared absorption measurements. We develop a virtual screening methodology performed on databases of millions of commercially available compounds. Quantum-chemistry calculations for about 3000 compounds are complemented by machine-learning methods to predict applicability of 93 000 organic molecules for detection. Training is performed on vibrational spectroscopic properties based on absorption and Raman scattering intensities. Our top molecules have conversion intensity two orders of magnitude higher than an average molecule from the database. We also discuss how other properties like molecular shape and self-assembling properties influence the detection efficiency. We identify molecular moieties whose presence in the molecules indicates high activity for THz detection and show an example where a simple modification of a frequently used self-assembling compound can enhance activity 85-fold. The capabilities of our screening method are demonstrated on narrow-band and broadband detection examples, and its possible applications in surface-enhanced spectroscopy are also discussed.
format	article
author	Zsuzsanna Koczor-Benda Alexandra L. Boehmke Angelos Xomalis Rakesh Arul Charlie Readman Jeremy J. Baumberg Edina Rosta
author_facet	Zsuzsanna Koczor-Benda Alexandra L. Boehmke Angelos Xomalis Rakesh Arul Charlie Readman Jeremy J. Baumberg Edina Rosta
author_sort	Zsuzsanna Koczor-Benda
title	Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods
title_short	Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods
title_full	Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods
title_fullStr	Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods
title_full_unstemmed	Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods
title_sort	molecular screening for terahertz detection with machine-learning-based methods
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/68982b87eb8146e18c83f0c2c21fab1c
work_keys_str_mv	AT zsuzsannakoczorbenda molecularscreeningforterahertzdetectionwithmachinelearningbasedmethods AT alexandralboehmke molecularscreeningforterahertzdetectionwithmachinelearningbasedmethods AT angelosxomalis molecularscreeningforterahertzdetectionwithmachinelearningbasedmethods AT rakesharul molecularscreeningforterahertzdetectionwithmachinelearningbasedmethods AT charliereadman molecularscreeningforterahertzdetectionwithmachinelearningbasedmethods AT jeremyjbaumberg molecularscreeningforterahertzdetectionwithmachinelearningbasedmethods AT edinarosta molecularscreeningforterahertzdetectionwithmachinelearningbasedmethods
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Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods

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