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A fragment based approach towards curating, comparing and developing machine learning models applied in

Raúl Pérez-Soto1, Mihai V Popescu1, Sabari Kumar1

  • 1Department of Chemistry, Colorado State University Fort Collins CO 80523 USA s.lopez@northeastern.edu.

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Summary

This study introduces a molecular fragmentation strategy to improve graph neural network predictions of molecular properties, specifically addressing exciton localization errors in photochemistry. This approach enhances model generalizability for sustainable energy applications.

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Area of Science:

  • Computational chemistry
  • Machine learning
  • Photochemistry

Background:

  • Graph neural networks (GNNs) are increasingly used for predicting molecular properties.
  • Photochemistry and photocatalysis are gaining importance as sustainable alternatives.
  • Existing GNN models struggle with exciton localization in molecules, causing prediction errors.

Purpose of the Study:

  • To develop a molecular fragmentation strategy to improve GNN predictions for photophysical properties.
  • To address the limitation of exciton localization in current GNN models.
  • To enable comparison of structural diversity in molecular libraries.

Main Methods:

  • A novel molecular fragmentation strategy was developed.
  • A new database, ALFAST-DB, containing 46,432 adiabatic S0-T1 energy gaps was generated.
  • A fragment-based delta learning approach was employed and compared with traditional message passing GNNs (MPGNNs).

Main Results:

  • The fragmentation strategy effectively overcomes exciton localization limitations.
  • The fragment-based delta learning approach demonstrated improved model generalizability.
  • The new method achieved prediction accuracies comparable to MPGNN architectures.

Conclusions:

  • Molecular fragmentation is a viable strategy to enhance GNN performance in photophysical property prediction.
  • This approach offers a way to assess and compare the diversity of molecular datasets.
  • The developed method holds promise for advancing sustainable chemistry through improved computational modeling.