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

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

Koczor-Benda, Z; Boehmke, AL; Xomalis, A; Arul, R; Readman, C; Baumberg, JJ; Rosta, E; (2021) Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods. Physical Review X , 11 (4) , Article 041035. 10.1103/physrevx.11.041035. Green open access

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Abstract

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.

Type:	Article
Title:	Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1103/physrevx.11.041035
Publisher version:	https://doi.org/10.1103/physrevx.11.041035
Language:	English
Additional information:	This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI:	https://discovery.ucl.ac.uk/id/eprint/10138915