Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Mayer, K. M. & Hafner, J. H. Localized surface plasmon resonance sensors. Chem. Rev. 111, 3828–3857 (2011).
Langer, J. et al. Present and future of surface-enhanced Raman scattering. ACS Nano 14, 28–117 (2020).
Willets, K. A., Wilson, A. J., Sundaresan, V. & Joshi, P. B. Super-resolution imaging and plasmonics. Chem. Rev. 117, 7538–7582 (2017).
Brongersma, M. L., Halas, N. J. & Nordlander, P. Plasmon-induced hot carrier science and technology. Nat. Nanotechnol. 10, 25–34 (2015).
Baffou, G. & Quidant, R. Nanoplasmonics for chemistry. Chem. Soc. Rev. 43, 3898–3907 (2014).
Park, W., Lu, D. & Ahn, S. Plasmon enhancement of luminescence upconversion. Chem. Soc. Rev. 44, 2940–2962 (2015).
Gu, M. et al. Nanoplasmonics: a frontier of photovoltaic solar cells. Nanophotonics 1, 235–248 (2012).
Azzam, S. I. et al. Ten years of spasers and plasmonic nanolasers. Light Sci. Appl 9, 90 (2020).
Abadeer, N. S. & Murphy, C. J. Recent progress in cancer thermal therapy using gold nanoparticles. J. Phys. Chem. C 120, 4691–4716 (2016).
Xavier, J., Yu, D. S., Jones, C., Zossimova, E. & Vollmer, F. Quantum nanophotonic and nanoplasmonic sensing: towards quantum optical bioscience laboratories on chip. Nanophotonics 10, 1387–1435 (2021).
Zhou, Z.-K. et al. Quantum plasmonics get applied. Prog. Quantum Electron. 65, 1–20 (2019).
Henry, A.-I. et al. Correlated structure and optical property studies of plasmonic nanoparticles. J. Phys. Chem. C 115, 9291–9305 (2011).
Carleo, G. et al. Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019).
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
Brown, K. A., Brittman, S., Maccaferri, N., Jariwala, D. & Celano, U. Machine learning in nanoscience: big data at small scales. Nano Lett. 20, 2–10 (2020).
Vahidzadeh, E. & Shankar, K. Artificial neural network-based prediction of the optical properties of spherical core–shell plasmonic metastructures. Nanomaterials 11, 633 (2021).
Malkiel, I. et al. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl. 7, 60 (2018).
Kim, W. et al. Paper-based surface-enhanced Raman spectroscopy for diagnosing prenatal diseases in women. ACS Nano 12, 7100–7108 (2018).
Lussier, F., Missirlis, D., Spatz, J. P. & Masson, J. F. Machine-learning-driven surface-enhanced Raman scattering optophysiology reveals multiplexed metabolite gradients near cells. ACS Nano 13, 1403–1411 (2019).
Shi, H. et al. Setting up a surface-enhanced Raman scattering database for artificial-intelligence-based label-free discrimination of tumor suppressor genes. Anal. Chem. 90, 14216–14221 (2018).
Tao, H. et al. Nanoparticle synthesis assisted by machine learning. Nat. Rev. Mater. 6, 701–716 (2021).
Yen, S.-C., Chen, Y.-L. & Su, Y.-H. Materials genome evolution of surface plasmon resonance characteristics of Au nanoparticles decorated ZnO nanorods. APL Mater. 8, 091109 (2020).
Leong, Y. X. et al. Surface-enhanced Raman scattering (SERS) taster: a machine-learning-driven multireceptor platform for multiplex profiling of wine flavors. Nano Lett. 21, 2642–2649 (2021).
Macias, G. et al. Whisky tasting using a bimetallic nanoplasmonic tongue. Nanoscale 11, 15216–15223 (2019).
Zhang, T. et al. Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks. Photon. Res. 7, 368–380 (2019).
Nelson, M. D. & Di Vece, M. Using a neural network to improve the optical absorption in halide perovskite layers containing core-shells silver nanoparticles. Nanomaterials 9, 437 (2019).
He, J., He, C., Zheng, C., Wang, Q. & Ye, J. Plasmonic nanoparticle simulations and inverse design using machine learning. Nanoscale 11, 17444–17459 (2019).
Roberts, N. B. & Keshavarz Hedayati, M. A deep learning approach to the forward prediction and inverse design of plasmonic metasurface structural color. Appl. Phys. Lett. 119, 061101 (2021).
Wang, M., Wang, T., Cai, P. & Chen, X. Nanomaterials discovery and design through machine learning. Small Methods 3, 1900025 (2019).
Kelly, K. L., Coronado, E., Zhao, L. L. & Schatz, G. C. The optical properties of metal nanoparticles: the influence of size, shape, and dielectric environment. J. Phys. Chem. B 107, 668–677 (2003).
Li, X., Shu, J., Gu, W. & Gao, L. Deep neural network for plasmonic sensor modeling. Opt. Mater. Express 9, 3857–3862 (2019).
Pashkov, D. M. et al. Quantitative analysis of the UV–vis spectra for gold nanoparticles powered by supervised machine learning. J. Phys. Chem. C 125, 8656–8666 (2021).
Arzola-Flores, J. A. & Gonzalez, A. L. Machine learning for predicting the surface plasmon resonance of perfect and concave gold nanocubes. J. Phys. Chem. C 124, 25447–25454 (2020).
Hiszpanski, A. M. et al. Nanomaterial synthesis insights from machine learning of scientific articles by extracting, structuring, and visualizing knowledge. J. Chem. Inf. Model. 60, 2876–2887 (2020).
Ashalley, E. et al. Multitask deep-learning-based design of chiral plasmonic metamaterials. Photon. Res. 8, 1213–1225 (2020).
Sajedian, I., Badloe, T. & Rho, J. Optimisation of colour generation from dielectric nanostructures using reinforcement learning. Opt. Express 27, 5874–5883 (2019).
Liu, D., Tan, Y., Khoram, E. & Yu, Z. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics 5, 1365–1369 (2018).
Kasani, S., Curtin, K. & Wu, N. A review of 2D and 3D plasmonic nanostructure array patterns: fabrication, light management and sensing applications. Nanophotonics 8, 2065–2089 (2019).
Glotzer, S. C. & Solomon, M. J. Anisotropy of building blocks and their assembly into complex structures. Nat. Mater. 6, 557–562 (2007).
MacFarlane, R. J. et al. Nanoparticle superlattice engineering with DNA. Science 334, 204–208 (2011).
Tao, H. C. et al. Nanoparticle synthesis assisted by machine learning. Nat. Rev. Mater. 6, 701–716 (2021).
Ringe, E., Van Duyne, R. P. & Marks, L. D. Kinetic and thermodynamic modified Wulff constructions for twinned nanoparticles. J. Phys. Chem. C 117, 15859–15870 (2013).
Salley, D. et al. A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticles. Nat. Commun. 11, 2771 (2020).
Pinho, B. & Torrente-Murciano, L. Dial-a-particle: precise manufacturing of plasmonic nanoparticles based on early growth information - redefining automation for slow material synthesis. Adv. Energy Mater. 11, 2100918 (2021).
Britton, J. & Raston, C. L. Multi-step continuous-flow synthesis. Chem. Soc. Rev. 46, 1250–1271 (2017).
Volk, A. A., Epps, R. W. & Abolhasani, M. Accelerated development of colloidal nanomaterials enabled by modular microfluidic reactors: toward autonomous robotic experimentation. Adv. Mater. 33, 2004495 (2021).
Coley, C. W., Green, W. H. & Jensen, K. F. Machine learning in computer-aided synthesis planning. Acc. Chem. Res. 51, 1281–1289 (2018).
Copp, S. M., Bogdanov, P., Debord, M., Singh, A. & Gwinn, E. Base motif recognition and design of DNA templates for fluorescent silver clusters by machine learning. Adv. Mater. 26, 5839–5845 (2014).
Copp, S. M. et al. Fluorescence color by data-driven design of genomic silver clusters. ACS Nano 12, 8240–8247 (2018).
Adorf, C. S., Moore, T. C., Melle, Y. J. U. & Glotzer, S. C. Analysis of self-assembly pathways with unsupervised machine learning algorithms. J. Phys. Chem. B 124, 69–78 (2020).
Dijkstra, M. & Luijten, E. From predictive modelling to machine learning and reverse engineering of colloidal self-assembly. Nat. Mater. 20, 762–773 (2021).
Nette, J., Howes, P. D. & deMello, A. J. Microfluidic synthesis of luminescent and plasmonic nanoparticles: fast, efficient, and data-rich. Adv. Mater. Technol. 5, 2000060 (2020).
Wu, C.-C., Pan, F. & Su, Y.-H. Surface plasmon resonance of gold nano-sea-urchins controlled by machine-learning-based regulation in seed-mediated growth. Adv. Photon. Res. 2, 2170031 (2021).
Mekki-Berrada, F. et al. Two-step machine learning enables optimized nanoparticle synthesis. npj Comput. Mater. 7, 55 (2021).
Dong, B. et al. Synthesis of monodisperse spherical AgNPs by ultrasound-intensified Lee-Meisel method, and quick evaluation via machine learning. Ultrason. Sonochem. 73, 105485 (2021).
Fernandes, D. L. A. et al. Green microfluidic synthesis of monodisperse silver nanoparticles via genetic algorithm optimization. RSC Adv. 6, 95693–95697 (2016).
Fukada, K. & Seyama, M. Microfluidic devices controlled by machine learning with failure experiments. Anal. Chem. 94, 7060–7065 (2022).
Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).
Hopper, E. R. et al. Size control in the colloidal synthesis of plasmonic magnesium nanoparticles. J. Phys. Chem. C 126, 563–577 (2022).
Woehrle, G. H., Hutchinson, J. E., Ozkar, S. & Finke, R. G. Analysis of nanoparticle transmission electron microscopy data using a public- domain image-processing program, image. Turk. J. Chem. 30, 1–13 (2006).
Wang, X. et al. Autodetect-mNP: an unsupervised machine learning algorithm for automated analysis of transmission electron microscope images of metal nanoparticles. JACS Au 1, 316–327 (2021).
Lee, B. et al. Statistical characterization of the morphologies of nanoparticles through machine learning based electron microscopy image analysis. ACS Nano 14, 17125–17133 (2020).
Xu, S. et al. Deep learning analysis of polaritonic wave images. ACS Nano 15, 18182–18191 (2021).
Yao, L., Ou, Z., Luo, B., Xu, C. & Chen, Q. Machine learning to reveal nanoparticle dynamics from liquid-phase TEM videos. ACS Cent. Sci. 6, 1421–1430 (2020).
Zhong, Y., Li, C., Zhou, H. & Wang, G. Developing noise-resistant three-dimensional single particle tracking using deep neural networks. Anal. Chem. 90, 10748–10757 (2018).
Moon, G., Son, T., Lee, H. & Kim, D. Deep learning approach for enhanced detection of surface plasmon scattering. Anal. Chem. 91, 9538–9545 (2019).
Ma, Y. P., Li, Q., Luo, J. B., Huang, C. Z. & Zhou, J. Weak reaction scatterometry of plasmonic resonance light scattering with machine learning. Anal. Chem. 93, 12131–12138 (2021).
Horgan, C. C. et al. High-throughput molecular imaging via deep-learning-enabled Raman spectroscopy. Anal. Chem. 93, 15850–15860 (2021).
García de Abajo, F. J. Optical excitations in electron microscopy. Rev. Mod. Phys. 82, 209–275 (2010).
Nelayah, J. et al. Mapping surface plasmons on a single metallic nanoparticle. Nat. Phys. 3, 348–353 (2007).
Collins, S. M. & Midgley, P. A. Progress and opportunities in EELS and EDS tomography. Ultramicroscopy 180, 133–141 (2017).
Nicoletti, O. et al. Three-dimensional imaging of localized surface plasmon resonances of metal nanoparticles. Nature 502, 80–84 (2013).
Dobigeon, N. & Brun, N. Spectral mixture analysis of EELS spectrum-images. Ultramicroscopy 120, 25–34 (2012).
Altmann, Y., McLaughlin, S. & Hero, A. Robust linear spectral unmixing using anomaly detection. IEEE Trans. Comput. Imaging 1, 74–85 (2015).
Bosman, M., Watanabe, M., Alexander, D. T. L. & Keast, V. J. Mapping chemical and bonding information using multivariate analysis of electron energy-loss spectrum images. Ultramicroscopy 106, 1024–1032 (2006).
Kalinin, S. V. et al. Separating physically distinct mechanisms in complex infrared plasmonic nanostructures via machine learning enhanced electron energy loss spectroscopy. Adv. Opt. Mater. 9, 2001808 (2021).
Linic, S., Aslam, U., Boerigter, C. & Morabito, M. Photochemical transformations on plasmonic metal nanoparticles. Nat. Mater. 14, 567–576 (2015).
Mukherjee, S. et al. Hot-electron-induced dissociation of H2 on gold nanoparticles supported on SiO2. J. Am. Chem. Soc. 136, 64–67 (2014).
van Schrojenstein Lantman, E. M., Deckert-Gaudig, T., Mank, A. J. G., Deckert, V. & Weckhuysen, B. M. Catalytic processes monitored at the nanoscale with tip-enhanced Raman spectroscopy. Nat. Nanotechnol. 7, 583–586 (2012).
Linic, S., Christopher, P. & Ingram, D. B. Plasmonic-metal nanostructures for efficient conversion of solar to chemical energy. Nat. Mater. 10, 911–921 (2011).
Masood, H., Toe, C. Y., Teoh, W. Y., Sethu, V. & Amal, R. Machine learning for accelerated discovery of solar photocatalysts. ACS Catal. 9, 11774–11787 (2019).
Martirez, J. M. P., Bao, J. L. & Carter, E. A. First-principles insights into plasmon-induced catalysis. Annu. Rev. Phys. Chem. 72, 99–119 (2021).
Rück, M., Garlyyev, B., Mayr, F., Bandarenka, A. S. & Gagliardi, A. Oxygen reduction activities of strained platinum core–shell electrocatalysts predicted by machine learning. J. Phys. Chem. Lett. 11, 1773–1780 (2020).
Chen, C. & Li, S. Z. Valence electron density-dependent pseudopermittivity for nonlocal effects in optical properties of metallic nanoparticles. ACS Photonics 5, 2295–2304 (2018).
Hu, W. et al. Machine learning protocol for surface-enhanced Raman spectroscopy. J. Phys. Chem. Lett. 10, 6026–6031 (2019).
Chu, W., Saidi, W. A. & Prezhdo, O. V. Long-lived hot electron in a metallic particle for plasmonics and catalysis: ab initio nonadiabatic molecular dynamics with machine learning. ACS Nano 14, 10608–10615 (2020).
Sun, B., Fernandez, M. & Barnard, A. S. Machine learning for silver nanoparticle electron transfer property prediction. J. Chem. Inf. Model. 57, 2413–2423 (2017).
Nesfchi, M. M. et al. Fabrication of plasmonic nanoparticles/cobalt doped TiO2 nanosheets for degradation of tetracycline and modeling the process by artificial intelligence techniques. Mater. Sci. Semicond. Process. 122, 105465 (2021).
Mikolajczyk, A. et al. A chemoinformatics approach for the characterization of hybrid nanomaterials: safer and efficient design perspective. Nanoscale 11, 11808–11818 (2019).
Szczerbiński, J., Gyr, L., Kaeslin, J. & Zenobi, R. Plasmon-driven photocatalysis leads to products known from e-beam and x-ray-induced surface chemistry. Nano Lett. 18, 6740–6749 (2018).
Domulevicz, L., Jeong, H., Paul, N. K., Gomez-Diaz, J. S. & Hihath, J. Multidimensional characterization of single-molecule dynamics in a plasmonic nanocavity. Angew. Chem. Int. Ed. 60, 16436–16441 (2021).
Tian, C. et al. Deep learning on image denoising: an overview. Neural Netw. 131, 251–275 (2020).
Adir, O. et al. Integrating artificial intelligence and nanotechnology for precision cancer medicine. Adv. Mater. 32, 1901989 (2020).
Cui, F., Yue, Y., Zhang, Y., Zhang, Z. & Zhou, H. S. Advancing biosensors with machine learning. ACS Sens. 5, 3346–3364 (2020).
Jin, X., Liu, C., Xu, T., Su, L. & Zhang, X. Artificial intelligence biosensors: challenges and prospects. Biosens. Bioelectron. 165, 112412 (2020).
Masson, J. F. Surface plasmon resonance clinical biosensors for medical diagnostics. ACS Sens. 2, 16–30 (2017).
Gomes, J. C. M., Souza, L. C. & Oliveira, L. C. SmartSPR sensor: machine learning approaches to create intelligent surface plasmon based sensors. Biosens. Bioelectron. 172, 112760 (2021).
Thadson, K., Visitsattapongse, S. & Pechprasarn, S. Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application. Sci. Rep. 11, 16289 (2021).
Song, M. K., Chen, S. X., Hu, P. P., Huang, C. Z. & Zhou, J. Automated plasmonic resonance scattering imaging analysis via deep learning. Anal. Chem. 93, 2619–2626 (2021).
Weng, S. Z. et al. Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy. Analyst 145, 4827–4835 (2020).
Erzina, M. et al. Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs. Sens. Actuators B308, 127660 (2020).
Fang, X. L. et al. Fast discrimination of tumor and blood cells by label-free surface-enhanced Raman scattering spectra and deep learning. J. Appl. Phys. 129, 127660 (2021).
Hunter, R. et al. Optofluidic label-free SERS platform for rapid bacteria detection in serum. Sens. Actuators B300, 126907 (2019).
Lussier, F., Thibault, V., Charron, B., Wallace, G. Q. & Masson, J. F. Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering. Trends Anal. Chem. 124, 115796 (2020).
Thrift, W. J. et al. Deep learning analysis of vibrational spectra of bacterial lysate for rapid antimicrobial susceptibility testing. ACS Nano 14, 15336–15348 (2020).
Kajendirarajah, U., Olivia Avilés, M. & Lagugné-Labarthet, F. Deciphering tip-enhanced Raman imaging of carbon nanotubes with deep learning neural networks. Phys. Chem. Chem. Phys. 22, 17857–17866 (2020).
Zivanovic, V. et al. Optical nanosensing of lipid accumulation due to enzyme inhibition in live cells. ACS Nano 13, 9363–9375 (2019).
de Albuquerque, C. D. L., Sobral-Filho, R. G., Poppi, R. J. & Brolo, A. G. Digital protocol for chemical analysis at ultralow concentrations by surface-enhanced Raman scattering. Anal. Chem. 90, 1248–1254 (2018).
Thrift, W. J. & Ragan, R. Quantification of analyte concentration in the single molecule regime using convolutional neural networks. Anal. Chem. 91, 13337–13342 (2019).
Thrift, W. J. et al. Surface-enhanced Raman scattering-based odor compass: locating multiple chemical sources and pathogens. ACS Sens. 4, 2311–2319 (2019).
Smith, J. D. et al. Plasmonic anticounterfeit tags with high encoding capacity rapidly authenticated with deep machine learning. ACS Nano 15, 2901–2910 (2021).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Lashgari, E., Liang, D. & Maoz, U. Data augmentation for deep-learning-based electroencephalography. J. Neurosci. Methods 346, 108885 (2020).
Xie, Y. et al. How to achieve auto-identification in Raman analysis by spectral feature extraction & adaptive hypergraph. Spectrochim. Acta A 222, 117086 (2019).
So, S., Badloe, T., Noh, J., Bravo-Abad, J. & Rho, J. Deep learning enabled inverse design in nanophotonics. Nanophotonics 9, 1041–1057 (2020).
Xu, X., Aggarwal, D. & Shankar, K. Instantaneous property prediction and inverse design of plasmonic nanostructures using machine learning: current applications and future directions. Nanomaterials 12, 633 (2022).
Kabir, H. M. D., Khosravi, A., Hosen, M. A. & Nahavandi, S. Neural network-based uncertainty quantification: a survey of methodologies and applications. IEEE Access 6, 36218–36234 (2018).
Jospin, L. V., Laga, H., Boussaid, F., Buntine, W. & Bennamoun, M. Hands-on bayesian neural networks – a tutorial for deep learning users. IEEE Comput. Intell. Mag. 17, 29–48 (2022).
Deng, C., Ji, X., Rainey, C., Zhang, J. & Lu, W. Integrating machine learning with human knowledge. iScience 23, 101656 (2020).
Nickel, M., Murphy, K., Tresp, V. & Gabrilovich, E. A review of relational machine learning for knowledge graphs. Proc. IEEE 104, 11–33 (2016).
Adadi, A. & Berrada, M. Peeking inside the black-box: a survey on explainable artificial intelligence. IEEE Access 6, 52138–52160 (2018).
Gilpin, L. H. et al. Explaining explanations: an overview of interpretability of machine learning. In Proc. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) 80–89 (IEEE, 2018).
Linardatos, P., Papastefanopoulos, V. & Kotsiantis, S. Explainable AI: a review of machine learning interpretability methods. Entropy 23, 18 (2021).
Lipton, Z. C. The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16, 31–57 (2018).
We acknowledge the financial support of the Natural Science and Engineering Research Council of Canada, The Royal Society, UK, International Exchange Scheme IES\R3\203092 and UKRI Future Leaders Fellowship programme, grant number MR/S017186/1.
The authors declare no competing interests.
Peer review information
Nature Nanotechnology thanks Regina Ragan and Xiaonan Wang for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Masson, JF., Biggins, J.S. & Ringe, E. Machine learning for nanoplasmonics. Nat. Nanotechnol. 18, 111–123 (2023). https://doi.org/10.1038/s41565-022-01284-0