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Deciphering nonlinear optical properties in functionalized hexaphyrins via explainable machine learning.

Eline Desmedt1, Michiel Jacobs1, Mercedes Alonso1

  • 1Department of General Chemistry: Algemene Chemie (ALGC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium. Mercedes.Alonso.Giner@vub.be.

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Machine learning reveals key factors driving nonlinear optical (NLO) properties in hexaphyrins. Explainable AI identifies specific molecular features, like charge transfer and transition dipole moments, crucial for designing advanced NLO switches.

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Area of Science:

  • * Molecular Photonics
  • * Computational Chemistry
  • * Materials Science

Background:

  • * Understanding molecular nonlinear optical (NLO) properties is crucial for designing advanced NLO switches.
  • * Previous research identified orbital contributions, aromaticity, planarity, and intramolecular charge transfer as key factors.
  • * Hexaphyrin-based redox switches have shown promise for tunable NLO behavior.

Purpose of the Study:

  • * To identify the driving forces behind the first hyperpolarizability (βHRS) in meso-substituted and/or core-modified [26]- and [30]hexaphyrins.
  • * To utilize explainable machine learning (ML) for elucidating these structure-property relationships.
  • * To develop a predictive model for βHRS applicable to diverse hexaphyrin systems.

Main Methods:

  • * Kernel ridge regression (KRR) model with 6-fold cross-validation.
  • * Application of Shapley additive explanations (SHAP) for feature importance analysis.
  • * Investigation of various hexaphyrin derivatives, including redox states, substitution patterns, and topologies.

Main Results:

  • * A strong correlation between βHRS and the HOMO-LUMO energy gap was observed.
  • * Incorporating additional orbital information and charge-transfer features significantly improved the KRR model's accuracy.
  • * SHAP analysis revealed that charge transfer excitation length is critical for 30R systems, while transition dipole moment is key for 26R systems.
  • * The ML model effectively predicted βHRS for various hexaphyrin structures beyond the training set.

Conclusions:

  • * Explainable ML provides deep insights into the molecular design principles for enhanced NLO properties.
  • * Specific molecular descriptors, such as charge transfer and transition dipole moments, are critical for tuning βHRS in hexaphyrins.
  • * The developed ML model offers a powerful tool for accelerating the discovery of novel NLO materials.