Transfer-learning enhanced adaptive sampling for accelerating ultrafast spectroscopy.

Area of Science:

• Physical Chemistry
• Spectroscopy
• Materials Science

Background:

• Ultrafast transient absorption (TA) spectroscopy is vital for studying photoinduced dynamics in materials.
• Current TA methods often require extensive data collection, leading to long experimental times.
• There is a need for efficient methods to accelerate TA measurements without compromising data quality.

Purpose of the Study:

• To introduce a novel data-driven sampling technique, Transfer-Learning Enhanced Adaptive Sampling (TEAS), for accelerating TA spectroscopy.
• To demonstrate TEAS's ability to reduce measurement time while maintaining comprehensive spectral and temporal data.
• To showcase TEAS as a versatile and model-independent framework for various spectroscopic techniques.

Main Methods:

• TEAS combines transfer learning with adaptive sampling strategies.
• The method intelligently selects measurement points by exploiting cross-wavelength correlations.
• It focuses data acquisition on the most informative spectral and temporal regions.

Main Results:

• TEAS accurately reconstructs TA data using less than 1% of total experimental measurements.
• The method shows superior performance compared to traditional approaches lacking adaptive sampling or transfer learning.
• Effective data reconstruction was achieved across various signal-to-noise ratios.

Conclusions:

• TEAS offers a significant acceleration of ultrafast spectroscopy measurements.
• This approach enables cost-effective, real-time, and data-driven experimental designs.
• TEAS is a broadly applicable solution for enhancing efficiency in spectroscopic studies.