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Nano Research 2023, 16(3): 4188-4196 https://doi.org/10.1007/s12274-022-5095-7
Research Article | Issue | Published: 26 October 2022

Identification of chemical compositions from “featureless” optical absorption spectra: Machine learning predictions and experimental validations

Show Author's Information Tiankai Chen1,§,  Jiali Li1,§,  Pengfei Cai2,  Qiaofeng Yao1,  Zekun Ren3,  Yixin Zhu1,  Saif Khan1,  Jianping Xie1( ),  Xiaonan Wang4( )
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore
Singapore-MIT Alliance for Research and Technology (SMART), 1 CREATE Way, Singapore 138602, Singapore
Department of Chemical Engineering, Tsinghua University, Beijing 100084, China

§ Tiankai Chen and Jiali Li contributed equally to this work.

Keywords:
machine learning, convolutional neural network, gold nanoclusters, optical absorption, composition identification
Topics:
Nanoclusters
Cite this article:
Chen T, Li J, Cai P, et al. Identification of chemical compositions from “featureless” optical absorption spectra: Machine learning predictions and experimental validations. Nano Research, 2023, 16(3): 4188-4196. https://doi.org/10.1007/s12274-022-5095-7
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Abstract Full text Electronic supplementary material About this article

Rapid and accurate chemical composition identification is critically important in chemistry. While it can be achieved with optical absorption spectrometry by comparing the experimental spectra with the reference data when the chemical compositions are simple, such application is limited in more complicated scenarios especially in nano-scale research. This is due to the difficulties in identifying optical absorption peaks (i.e., from “featureless” spectra) arose from the complexity. In this work, using the ultraviolet–visible (UV–Vis) absorption spectra of metal nanoclusters (NCs) as a demonstration, we develop a machine-learning-based method to unravel the compositions of metal NCs behind the “featureless” spectra. By implementing a one-dimensional convolutional neural network, good matches between prediction results and experimental results and low mean absolute error values are achieved on these optical absorption spectra that human cannot interpret. This work opens a door for the identification of nanomaterials at molecular precision from their optical properties, paving the way to rapid and high-throughput characterizations.


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Electronic supplementary material
About this article

Identification of chemical compositions from “featureless” optical absorption spectra: Machine learning predictions and experimental validations

Show Author's information Tiankai Chen1,§, Jiali Li1,§, Pengfei Cai2, Qiaofeng Yao1, Zekun Ren3, Yixin Zhu1, Saif Khan1, Jianping Xie1( ), Xiaonan Wang4( )
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore
Singapore-MIT Alliance for Research and Technology (SMART), 1 CREATE Way, Singapore 138602, Singapore
Department of Chemical Engineering, Tsinghua University, Beijing 100084, China

§ Tiankai Chen and Jiali Li contributed equally to this work.

Abstract

Rapid and accurate chemical composition identification is critically important in chemistry. While it can be achieved with optical absorption spectrometry by comparing the experimental spectra with the reference data when the chemical compositions are simple, such application is limited in more complicated scenarios especially in nano-scale research. This is due to the difficulties in identifying optical absorption peaks (i.e., from “featureless” spectra) arose from the complexity. In this work, using the ultraviolet–visible (UV–Vis) absorption spectra of metal nanoclusters (NCs) as a demonstration, we develop a machine-learning-based method to unravel the compositions of metal NCs behind the “featureless” spectra. By implementing a one-dimensional convolutional neural network, good matches between prediction results and experimental results and low mean absolute error values are achieved on these optical absorption spectra that human cannot interpret. This work opens a door for the identification of nanomaterials at molecular precision from their optical properties, paving the way to rapid and high-throughput characterizations.

Keywords: machine learning, convolutional neural network, gold nanoclusters, optical absorption, composition identification

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Acknowledgements

Publication history

Received: 31 May 2022
Revised: 22 September 2022
Accepted: 25 September 2022
Published: 26 October 2022
Issue date: March 2023

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgement

We acknowledge the Singapore RIE2020 Advanced Manufacturing and Engineering Programmatic grant “Accelerated Materials Development for Manufacturing” by the Agency for Science, Technology and Research under No. A1898b0043.

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