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We created SHNITSEL, a large dataset of quantum chemical calculations for organic molecules. This resource aids machine learning model development for understanding excited-state properties.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Photochemistry

Background:

  • Theoretical studies of molecular photochemistry and photophysics are crucial for understanding natural processes.
  • Computationally intensive quantum chemical calculations limit direct simulations and machine learning (ML) model development.
  • Existing data limitations hinder progress in modeling excited-state properties.

Purpose of the Study:

  • To introduce SHNITSEL, a comprehensive data repository for ab-initio calculations of organic molecules.
  • To provide a robust benchmark dataset for training and validating machine learning models.
  • To accelerate the development of ML-based approaches for predicting excited-state properties.

Main Methods:

  • Generated a dataset of 418,870 ab-initio data points for nine organic molecules.
  • Included high-accuracy quantum chemical properties for ground and excited states (singlet/triplet).
  • Incorporated properties related to electronic state coupling, such as nonadiabatic couplings, transition dipoles, and spin-orbit couplings.

Main Results:

  • Established SHNITSEL, a large-scale, high-quality dataset for molecular excited-state studies.
  • The dataset contains diverse quantum chemical properties essential for photophysical and photochemical research.
  • SHNITSEL enables rigorous benchmarking of existing and novel ML models.

Conclusions:

  • SHNITSEL significantly lowers the barrier for developing ML models in excited-state chemistry.
  • The repository facilitates advancements in computational modeling of photochemistry and photophysics.
  • This resource is expected to drive innovation in predicting and understanding molecular excited states.