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Accepting a small error drastically reduces the dimensionality of chemical space for molecular properties. This finding enables more data-efficient and transferable machine learning models in chemistry.

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Area of Science:

  • Computational chemistry
  • Machine learning in chemistry

Background:

  • Chemical space is vast, posing challenges for computational modeling.
  • Machine learning (ML) models benefit from reduced dimensionality in chemical space.
  • The limits of dimensionality reduction for physical properties and the impact of model error are not well understood.

Purpose of the Study:

  • To investigate how much the dimensionality of physical properties can be reduced by accepting a small model error.
  • To determine the dependence of dimensionality reduction on specific physical properties and molecular size.
  • To develop a method for quantifying intrinsic dimensionality bounds for given accuracy thresholds.

Main Methods:

  • Analysis of dimensionality reduction for total energy, frontier orbital energies, and static polarizability.
  • Consideration of neutral molecules up to 20 atoms.
  • Inclusion of all continuous variables in the molecular Hamiltonian, including nuclear charges, to define accuracy thresholds.

Main Results:

  • A modest, nearly negligible error leads to a drastic reduction in independent degrees of freedom for molecular properties.
  • This dimensionality reduction is observed across various properties and molecular sizes.
  • The intrinsic dimensionality is stable across molecules, indicating transferability.

Conclusions:

  • Accepting a small error significantly reduces the effective dimensionality of chemical space.
  • The intrinsic dimensionality is a property of the physical quantity and atom count, not individual configurations.
  • These findings suggest opportunities for compressing molecular representations for more efficient and transferable ML models.