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

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.

Automated summary

Rapid and accurate chemical composition identification is crucial in chemistry. Optical absorption spectrometry has limitations in identifying complex chemical compositions, especially in nano-scale research. In this study, a machine-learning-based method is developed to identify the compositions of metal nanoclusters (NCs) from featureless spectra. The method achieves good matches and low error values on optical absorption spectra that are difficult for humans to interpret. This work opens up possibilities for precise identification of nanomaterials based on their optical properties.