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Alexander Yu Tolbin1, Mikhail S Savelyev1,2,3, Pavel N Vasilevsky1,2

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This study introduces the CORRELATO algorithm to predict optical limiter efficiency using limited data, enabling precise quantitative forecasting and targeted material design for advanced optical protection.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Nonlinear Optics

Background:

  • Optical limiters (OLs) are crucial for protecting optical components from high-intensity lasers.
  • Existing prediction methods (quantum-chemical calculations, machine learning) have limitations like high cost and data scarcity.
  • Developing efficient and interpretable methods for predicting OL performance is essential.

Purpose of the Study:

  • To introduce and validate the CORRELATO algorithm for predicting optical limiter efficiency.
  • To establish analytical structure-property relationships for designing high-performance OLs.
  • To overcome limitations of traditional methods, especially with small datasets.

Main Methods:

  • Utilized the CORRELATO algorithm, a hybrid approach combining nonlinear regression, symbolic regression, and factor analysis.
  • Synthesized 24 low-symmetry phthalocyanine dyes (monomers and dimers) and characterized their nonlinear optical (NLO) response via Z-scan measurements.
  • Calculated electronic structure descriptors (HOMO-LUMO gap, dipole moment, polarizability, first hyperpolarizability) using DFT/M06-2X.
  • Implemented an iterative optimization procedure and a clustering strategy based on local nonlinear response density.

Main Results:

  • Derived explicit analytical expressions for predicting integral OL activation speed.
  • Achieved significant refinement of predictive models and reduced mean absolute percentage error (MAPE) through iterative optimization.
  • Developed highly accurate cluster-specific models with MAPE <5% for one cluster.
  • Identified polarizability, dipole moment, and charge-transfer integral as key parameters governing OL performance.

Conclusions:

  • The CORRELATO algorithm provides a powerful and interpretable framework for predicting OL efficiency, especially with limited data.
  • This methodology enables a transition from qualitative classification to precise quantitative forecasting of OL performance.
  • Established a comprehensive approach for the targeted design and accelerated optimization of high-performance optical limiters.