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Jun, 2023;19 (25): e2205893.

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders.

Muammer Y Yaman, Sergei V Kalinin, Kathryn N Guye, David S Ginger, Maxim Ziatdinov
Author Information
  1. Muammer Y Yaman: Department of Chemistry, University of Washington, Seattle, WA, 98195, USA. ORCID
  2. Sergei V Kalinin: Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37996, USA. ORCID
  3. Kathryn N Guye: Department of Chemistry, University of Washington, Seattle, WA, 98195, USA. ORCID
  4. David S Ginger: Department of Chemistry, University of Washington, Seattle, WA, 98195, USA. ORCID
  5. Maxim Ziatdinov: Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA. ORCID
PMID: 36942857 DOI: 10.1002/smll.202205893

Abstract

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

Keywords

darkfield scattering spectra machine learning plasmonic gold particles scanning electron microscopy structure-property prediction variational autoencoder

References

  1. N. Karker, G. Dharmalingam, M. A. Carpenter, ACS Nano 2014, 8, 10953.
  2. J. G. Smith, J. A. Faucheaux, P. K. Jain, Nano Today 2015, 10, 67.
  3. M. Du, G. H. Tang, Sol. Energy 2016, 137, 393.
  4. M. S. G. Hamed, J. N. Ike, G. T. Mola, J Phys D Appl Phys 2021, 55, 015102.
  5. A. P. Kulkarni, K. M. Noone, K. Munechika, S. R. Guyer, D. S. Ginger, Nano Lett. 2010, 10, 1501.
  6. K. Yao, M. Salvador, C.-C. Chueh, X.-K. Xin, Y.-X. Xu, D. W. deQuilettes, T. Hu, Y. Chen, D. S. Ginger, A. K.-Y. Jen, Adv. Energy Mater. 2014, 4, 1400206.
  7. X. Huang, I. H. El-Sayed, W. Qian, M. A. El-Sayed, J. Am. Chem. Soc. 2006, 128, 2115.
  8. A. M. Gobin, M. Ho Lee, N. J. Halas, W. D. James, R. A. Drezek, J. L. West, Nano Lett. 2007, 7, 1929.
  9. M.-R. Choi, K. J. Stanton-Maxey, J. K. Stanley, C. S. Levin, R. Bardhan, D. Akin, S. Badve, J. Sturgis, J. Paul Robinson, R. Bashir, N. J. Halas, S. E. Clare, Nano Lett. 2007, 7, 3759.
  10. S. A. Maier, P. G. Kik, H. A. Atwater, S. Meltzer, E. Harel, B. E. Koel, A. A. G. Requicha, Nat. Mater. 2003, 2, 229.
  11. Jiang, K. Bosnick, M. Maillard, L. Brus, J. Phys. Chem. B 2003, 107, 9964.
  12. N. L. Rosi, C. A. Mirkin, Chem. Rev. 2005, 105, 1547.
  13. B. Nikoobakht, M. A. El-Sayed, J. Phys. Chem. A 2003, 107, 3372.
  14. J. I. L. Chen, H. Durkee, B. Traxler, D. S. Ginger, Small 2011, 7, 1993.
  15. S. A. Maier, M. L. Brongersma, P. G. Kik, S. Meltzer, A. A. G. Requicha, H. A. Atwater, Adv. Mater. 2001, 13, 1501.
  16. W. Y. Huang, W. Qian, M. A. El-Sayed, Adv. Mater. 2008, 20, 733.
  17. K. N. Guye, H. Shen, M. Y. Yaman, G. Y. Liao, D. Baker, D. S. Ginger, Langmuir 2021, 37, 9111.
  18. M. Y. Yaman, K. N. Guye, M. Ziatdinov, H. Shen, D. Baker, S. v Kalinin, D. S. Ginger, Soft Matter 2021, 17, 6109.
  19. A. Kuzyk, R. Schreiber, Z. Fan, G. Pardatscher, E.-M. Roller, A. Högele, F. C. Simmel, A. O. Govorov, T. Liedl, Nature 2012, 483, 311.
  20. X. Shen, C. Song, J. Wang, D. Shi, Z. Wang, N. Liu, B. Ding, J. Am. Chem. Soc. 2011, 134, 146.
  21. A. M. Funston, C. Novo, T. J. Davis, P. Mulvaney, Nano Lett. 2009, 9, 1651.
  22. N. Gandra, A. Abbas, L. Tian, S. Singamaneni, Nano Lett. 2012, 12, 2645.
  23. J.-Y. Kim, J. Yeom, G. Zhao, H. Calcaterra, J. Munn, P. Zhang, N. Kotov, J. Am. Chem. Soc. 2019, 141, 11739.
  24. L. Yao, Z. Ou, B. Luo, C. Xu, Q. Chen, ACS Cent. Sci. 2020, 6, 1421.
  25. J. I. L. Chen, Y. Chen, D. S. Ginger, J. Am. Chem. Soc. 2010, 132, 9600.
  26. S. Samai, Z. Qian, J. Ling, K. N. Guye, D. S. Ginger, ACS Applied Materials & Interfaces 2018, 10, 8976.
  27. A. L. Koh, K. Bao, I. Khan, W. E. Smith, G. Kothleitner, P. Nordlander, S. A. Maier, D. W. McComb, ACS Nano 2009, 3, 3015.
  28. J. Szczerbiński, L. Gyr, J. Kaeslin, R. Zenobi, Nano Lett. 2018, 18, 6740.
  29. P. K. Jain, W. Huang, M. A. El-Sayed, Nano Lett. 2007, 7, 2080.
  30. R. T. Hill, J. J. Mock, A. Hucknall, S. D. Wolter, N. M. Jokerst, D. R. Smith, A. Chilkoti, ACS Nano 2012, 6, 9237.
  31. L. Na, H. Mario, W. Thomas, A. A. Paul, G. Harald, Science (1979) 2011, 332, 1407.
  32. I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, H. Suchowski, Light Sci Appl 2018, 7, 60.
  33. P. R. Wiecha, A. Lecestre, N. Mallet, G. Larrieu, Nature Nanotechnology 2019, 14, 237.
  34. J. Zhou, B. Huang, Z. Yan, J.-C. G. Bünzli, Light Sci Appl 2019, 8, 84.
  35. E. X. Tan, Y. Chen, Y. H. Lee, Y. X. Leong, S. X. Leong, C. V. Stanley, C. S. Pun, X. Y. Ling, Nanoscale Horiz. 2022, 7, 626.
  36. T. Zhang, J. Wang, Q. Liu, J. Zhou, J. Dai, X. Han, Y. Zhou, K. Xu, Photonics Res 2019, 7, 368.
  37. K. Kojima, T. Koike-Akino, Y. Tang, Y. Wang, in OSA Advanced Photonics Congress 2021, Optica Publishing Group, Beijing, China 2021, p. IF3A.6.
  38. O. Avci, C. Yurdakul, M. Selim Ünlü, Appl. Opt. 2017, 56, 4238.
  39. M. Sun, F. Liu, Y. Zhu, W. Wang, J. Hu, J. Liu, Z. Dai, K. Wang, Y. Wei, J. Bai, W. Gao, Nanoscale 2016, 8, 4452.
  40. K. Lance Kelly, E. Coronado, L. Lin Zhao, G. C. Schatz, J. Phys. Chem. B 2002, 107, 668.
  41. C. Tabor, R. Murali, M. Mahmoud, M. A. El-Sayed, J. Phys. Chem. A 2008, 113, 1946.
  42. J. Hee Yoon, F. Selbach, L. Schumacher, J. Jose, S. Schlücker, ACS Photonics 2019, 6, 642.
  43. Q. Zhang, G.-C. Li, T. W. Lo, D. Y. Lei, J Opt 2018, 20, 024010.
  44. C. J. Murphy, T. K. Sau, A. M. Gole, C. J. Orendorff, J. X. Gao, L. Gou, S. E. Hunyadi, T. Li, J. Phys. Chem. B 2005, 109, 13857.
  45. Y. Liu, R. K. Vasudevan, K. K. Kelley, D. Kim, Y. Sharma, M. Ahmadi, S. v Kalinin, M. Ziatdinov, Mach Learn Sci Technol 2021, 2, 045028.
  46. K. M. Roccapriore, M. Ziatdinov, S. H. Cho, J. A. Hachtel, S. v Kalinin, Small 2021, 17, 2100181.
  47. M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, S. v Kalinin, ACS Nano 2017, 11, 12742.
  48. S. v. Kalinin, S. Zhang, M. Valleti, H. Pyles, D. Baker, J. J. de Yoreo, M. Ziatdinov, ACS Nano 2021, 15, 6471.
  49. S. V. Kalinin, D. Ondrej, J. Stephen, Z. Maxim, Sci. Adv. 2021, 7, eabd5084.
  50. M. P. Oxley, M. Ziatdinov, O. Dyck, A. R. Lupini, R. Vasudevan, S. v Kalinin, npj Comput. Mater. 2021, 7, 65.
  51. Y. Liu, R. Proksch, C. Y. Wong, M. Ziatdinov, S. v Kalinin, Adv. Mater. 2021, 33, 2103680.
  52. S. v. Kalinin, M. P. Oxley, M. Valleti, J. Zhang, R. P. Hermann, H. Zheng, W. Zhang, G. Eres, R. K. Vasudevan, M. Ziatdinov, npj Comput. Mater. 2021, 7, 181.
  53. M. Ziatdinov, C. Y. Wong, S. v Kalinin, arXiv:2106.12472 [cond-mat] 2021.
  54. S. v. Kalinin, K. Kelley, R. K. Vasudevan, M. Ziatdinov, ACS Appl. Mater. Interfaces 2021, 13, 1693.
  55. K. M. Roccapriore, M. Ziatdinov, S. H. Cho, J. A. Hachtel, S. V. Kalinin, Small 2021, 17, 2100181.
  56. M. Ziatdinov, M. Y. Yaman, Y. Liu, D. Ginger, S. V. Kalinin, arXiv:2105.11475 2021.
  57. M. Ziatdinov, S. V. Kalinin, arXiv:2104.10180 2021.
  58. Amit Singhal, Bulletin Of The Ieee Computer Society Technical Committee On Data Engineering 2001, 24.
  59. J. R. Hershey, P. A. Olsen, in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07, IEEE,  2007, pp. IV-317.
  60. D. P. Kingma, J. Ba, arXiv preprint arXiv:1412.6980 2014.
  61. Y. Yan, J. I. L. Chen, D. S. Ginger, Nano Lett. 2012, 12, 2530.
  62. J. H. Yoon, F. Selbach, L. Schumacher, J. Jose, S. Schlücker, ACS Photonics 2019, 6, 642.
  63. S. Samai, T. L. Y. Choi, K. N. Guye, Y. Yan, D. S. Ginger, J. Phys. Chem. C 2018, 122, 13363.
  64. A. Crut, P. Maioli, N. del Fatti, F. Vallée, Chem. Soc. Rev. 2014, 43, 3921.
  65. C. Schopf, E. Noonan, A. J. Quinn, D. Iacopino, R. Comparelli, L. Curri, M. Striccoli, Crystals 2016, 6, 117.
  66. S. K. Pal, H. Chatterjee, S. K. Ghosh, RSC Adv. 2019, 9, 42145.
  67. R. Dosselmann, X. D. Yang, Signal Image Video Process 2011, 5, 81.
  68. P. B. Johnson, R. W. Christy, Phys. Rev. B 1972, 6, 4370.
  69. T. A. F. König, P. A. Ledin, J. Kerszulis, Mahmoud. A. Mahmoud, M. A. El-Sayed, J. R. Reynolds, V. v Tsukruk, ACS Nano 2014, 8, 6182.

Grants

  1. /Energy Frontier Research Centers
  2. DE-SC0019288/Science of Synthesis Across Scales
  3. /University of Washington
  4. /Oak Ridge National Laboratory's Center for Nanophase Materials Sciences
  5. /U.S. Department of Energy
  6. CNMS2021-B-00847/Office of Science
  7. /Basic Energy Sciences
  8. /University of Washington Molecular Analysis Facility
  9. /National Nanotechnology Coordinated Infrastructure
  10. /Clean Energy Institute
Journal Article

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