From Neural Networks to Transformers: Achieving High and Fast Precision in Fluorescence Correlation Spectroscopy Analysis.

Fluorescence correlation spectroscopy (FCS), known for its high sensitivity and temporal resolution, is a crucial tool for understanding molecular dynamics. This study develops a neural network-based fitting scheme and introduces the Fast Correlation Fitting Model (FCFM). The neural network fitting scheme accurately fits multiple parameters in FCS data. The FCFM, incorporating the Transformer architecture, a custom CorrelationLoss function, and an optimized process, enhances fitting accuracy and computational efficiency, surpassing traditional methods by 2 orders of magnitude in fitting speed. This provides an effective solution for FCS data analysis. By integrating a pretraining-fine-tuning approach, parameter mapping, and dynamic noise adjustments with CorrelationLoss fine-tuning, FCFM rapidly converges and captures FCS data features for quick and precise fitting. Its efficient parallel computing capabilities, coupled with CUDA acceleration, allow real-time data analysis even on low-specification hardware, making it more accessible to researchers. Our results show that FCFM achieves comparable fitting accuracy and exhibits enhanced performance on certain parameters compared to the classic weighted Levenberg-Marquardt algorithms, enabling rapid simultaneous fitting of thousands of data sets on a personal computer. This provides a tool for real-time acquisition of FCS data parameters in high-throughput FCS methods. The model's flexibility to adapt to various formula-fitting needs demonstrates its broad application potential in analytical chemistry and other fields.