Pyglotaran: a lego-like Python framework for global and target analysis of time-resolved spectra.

Area of Science:

• Chemical Physics
• Molecular Dynamics
• Spectroscopy

Background:

• Understanding molecular dynamics is crucial for various scientific fields.
• Time-resolved spectroscopy provides insights into ultrafast molecular processes.
• Model-based analysis is essential for interpreting complex spectral data.

Purpose of the Study:

• Introduce a novel Python framework, pyglotaran, for global and target analysis of time-resolved spectra.
• Demonstrate the framework's utility across diverse molecular systems and spectroscopic techniques.
• Provide a reliable tool for researchers to study complex molecular dynamics.

Main Methods:

• Developed a Python framework (pyglotaran) for global and target analysis of time-resolved spectral data.
• Applied the framework to three case studies: 4-thiothymidine, perylene bisimide systems, and photosynthetic cells.
• Utilized a modular, 'lego-like' approach for kinetic model composition and integration with the Python ecosystem.

Main Results:

• Resolved vibrational wavepackets with ≈10 fs resolution in 4-thiothymidine, highlighting the importance of 'coherent artifact' description.
• Identified a light-harvesting efficiency loss due to radical pair formation in multichromophoric perylene bisimide systems.
• Successfully modeled a large photosynthetic megacomplex (≈500 chromophores) by combining kinetic models of its components.

Conclusions:

• The pyglotaran framework offers a practical 'middle ground' between theoretical simulation and experimental data fitting for complex molecular systems.
• Its modular design and integration capabilities make it accessible and reliable for researchers.
• Pyglotaran is an invaluable tool for advancing the understanding of molecular dynamics through time-resolved spectroscopy.