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

太赫兹辐射 计算机科学 人工智能 光电子学 物理
作者
Zsuzsanna Koczor-Benda,Alexandra Boehmke,Angelos Xomalis,Rakesh Arul,Charlie Readman,Jeremy J. Baumberg,Edina Rosta
出处
期刊:Physical Review X [American Physical Society]
卷期号:11 (4) 被引量:6
标识
DOI:10.1103/physrevx.11.041035
摘要

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.Received 9 April 2021Revised 10 August 2021Accepted 7 September 2021DOI:https://doi.org/10.1103/PhysRevX.11.041035Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)TechniquesDensity functional theoryElectronic structureHigh-throughput calculationsMachine learningRaman spectroscopySurface-enhanced Raman spectroscopyTerahertz spectroscopyAtomic, Molecular & OpticalCondensed Matter, Materials & Applied Physics
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