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Updated: Jun 23, 2025
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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
Published on: May 27, 2020
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Optimal Transport Distances to Characterize Electronic Excitations
Annina Z Lieberherr1, Paola Gori-Giorgi2,3, Klaas J H Giesbertz2
1Department of Chemistry, Physical and Theoretical Chemistry Laboratory, University of Oxford, South Parks Road, Oxford OX1 3QZ, U.K.
Journal of Chemical Theory and Computation
|June 14, 2024
Summary
This study introduces a new optimal transport diagnostic (Θ) for classifying electronic excitations in computational chemistry. While showing promise, Θ has limitations with charge transfer and Rydberg excitations, suggesting combined approaches for better accuracy.
Area of Science:
- Computational Chemistry
- Theoretical Chemistry
- Quantum Mechanics
Background:
- Classifying electronic excitations from simulations is crucial for computational and mechanistic studies.
- Optimal transport distances offer a powerful framework for classification tasks.
Purpose of the Study:
- To propose and evaluate a novel diagnostic (Θ) based on Sinkhorn divergence for electronic excitation characterization.
- To compare the performance of Θ against the established Λ diagnostic and their combination.
Main Methods:
- Utilized Sinkhorn divergence from optimal transport to develop the Θ diagnostic.
- Employed a k-nearest neighbors (k-NN) classification algorithm.
- Assessed performance on labeling electronic excitations, including Rydberg and charge transfer types.
Main Results:
- The new diagnostic (Θ) showed limitations, particularly for charge transfer excitations in small molecules.
- Rydberg excitations were poorly separated across all tested methods.
- A length-scale-normalized version of Θ correlated well with the Λ diagnostic for Gaussian basis sets.
Conclusions:
- The proposed diagnostic (Θ) offers a new perspective but requires further refinement for broad applicability.
- Combined optimal transport and overlap diagnostics with alternative metrics show the most potential for future excitation classification.
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