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Accurate Prediction of ECD Spectra of Silver-Based Chiral Complexes Using Femtosecond-Informed Machine Learning from

Zhixuan Li¹, Yanyan Bu², Jiaren Yuan³

¹College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

The Journal of Physical Chemistry Letters

|May 8, 2026

Summary

A new physics-inspired machine learning framework accurately predicts electronic circular dichroism (ECD) spectra for silver complexes. This approach significantly accelerates computational speed and enhances spectral prediction accuracy, aiding chiral material design.

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Area of Science:

Computational chemistry
Spectroscopy
Machine learning

Background:

Electronic circular dichroism (ECD) is crucial for analyzing chiral systems.
Traditional machine learning faces challenges in predicting ECD spectra due to complex factors like coupled transitions and limited data.
Accurate ECD prediction is vital for molecular configuration, metal-ligand interactions, and chiral optical material design.

Purpose of the Study:

To develop a physics-inspired machine learning framework for accurate and efficient ECD spectra prediction of metal complexes.
To address the limitations of traditional methods in handling complex electronic transitions and data scarcity.
To provide an interpretable and computationally efficient tool for chiral analysis.

Main Methods:

Integration of femtosecond charge dynamics, ECD spectra, and electronic transitions within a machine learning framework.
Development of the MC-HG model for computational speed enhancement and the PSF-ECD model for spectral accuracy improvement.
Utilizing transition contribution maps (TCM) and Mulliken charge flow for interpretability.

Main Results:

The proposed framework achieves quantitatively close ECD spectra predictions compared to TDDFT, with a 470x increase in computational speed.
The PSF-ECD model improved spectral shape similarity by 3x and reduced error indices by 45% over traditional ML models.
Established an interpretable link between spectral shapes, orbital transitions, and charge transfer channels.

Conclusions:

The physics-inspired machine learning framework offers an efficient and interpretable solution for predicting ECD spectra of silver-based complexes.
This approach accelerates tasks like absolute configuration determination, chiral catalysis screening, and optical material design.
Enables rapid evaluation of chiral optical responses without extensive real-time TDDFT calculations.