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.

Gasification is a sustainable approach for biomass waste treatment with simultaneous combustible H₂-syngas production. However, this thermochemical process was quite complicated with multi-phase products generated. The product distribution and composition also highly depend on the feedstock information and gasification condition. At present, it is still challenging to fully understand and optimize this process. In this context, four data-driven machine learning (ML) methods were applied to model the biomass waste gasification process for product prediction and process interpretation and optimization. The results indicated that the Gradient Boosting Regression (GBR) model showed good performance for predicting three-phase products and syngas compositions with test R² of 0.82–0.96. The GBR model-based interpretation suggested that both feed and gasification condition (including the contents of feedstock ash, carbon, nitrogen, oxygen, and gasification temperature) were important factors influencing the distribution of char, tar, and syngas. Furthermore, it was found that a feedstock with higher carbon (> 48%), lower nitrogen (< 0.5%), and ash (1%–5%) contents under a temperature over 800 ℃ could achieve a higher yield of H₂-rich syngas. It was shown that the optimal conditions suggested by the model could achieve an output containing 60%–62% syngas and achieve an H₂ yield of 44.34 mol/kg. These valuable insights provided from the model-based interpretation could aid the understanding and optimization of biomass gasification to guide the production of H₂-rich syngas.