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

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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.

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

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore

    Tiankai Chen, Jiali Li, Qiaofeng Yao, Yixin Zhu, Saif Khan & Jianping Xie

  2. Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore

    Pengfei Cai

  3. Singapore-MIT Alliance for Research and Technology (SMART), 1 CREATE Way, Singapore, 138602, Singapore

    Zekun Ren

  4. Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China

    Xiaonan Wang

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

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Chen, T., Li, J., Cai, P. et al. Identification of chemical compositions from “featureless” optical absorption spectra: Machine learning predictions and experimental validations. Nano Res. 16, 4188–4196 (2023). https://doi.org/10.1007/s12274-022-5095-7

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