Intelligently optimized global analysis of time resolved spectra with particle swarm optimization

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Intelligently optimized global analysis of time resolved spectra with particle swarm optimization
Title:
Intelligently optimized global analysis of time resolved spectra with particle swarm optimization
Journal Title:
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
Publication Date:
27 November 2023
Citation:
Ma, L., & Jiang, L. (2024). Intelligently optimized global analysis of time resolved spectra with particle swarm optimization. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 308, 123685. https://doi.org/10.1016/j.saa.2023.123685
Abstract:
Time-resolved spectroscopy, especially transient absorption spectroscopy (TAS), provides valuable insights to excited state dynamics. Analyzing TAS data involves fitting complex kinetic traces at various probe wavelengths using different rate equations. Conventional TAS global fitting methods require domain experts to establish physically valid models and provide good initial guesses to generate converged solutions. This poses challenges for non-experts who seek to utilize TAS, thus limiting its broader application and impact. To address this problem, we propose an intelligent optimization framework based on the particle swarm optimization (PSO) algorithm. In the proposed method, the PSO algorithm acts as the global fitting method to find the optimal values of the target variables or unknown parameters in the kinetics models. The target solution is optimized by iteratively updating candidate solutions with respect to an objective feedback signal. We demonstrated the effectiveness of the proposed PSO-based global fitting method with both synthetic and experimental datasets. The results show that our proposed method can successfully find the optimal target values in the global fitting process automatically, thus eliminating the iterative manual labor traditionally required. The proposed intelligent optimization framework provides a novel approach for automatic global fitting of TAS data, which significantly enhances the accessibility and utilization of the TAS methodology.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This work was financially supported by the Guangdong Basic and Applied Basic Research Foundation (2022A1515011951), and the National Natural Science Foundation of China (62375056).
Description:
ISSN:
1386-1425
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