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Local hybrid functionals (LHs) with neural-network local mixing functions (n-LMFs) now balance self-interaction and static-correlation errors effectively. The new LH25nP functional achieves record accuracy for main-group energetics and improves spin-related chemical problems.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Materials Science

Background:

  • Local hybrid functionals (LHs) offer a flexible approach to balancing self-interaction and static-correlation errors in density functional approximations.
  • Recent advancements incorporate strong-correlation factors into local mixing functions (LMFs) for improved performance.
  • Machine learning has been employed to develop neural-network LMFs (n-LMFs), showing promise for main-group energetics.

Purpose of the Study:

  • To develop a novel local hybrid functional incorporating a neural-network local mixing function optimized for strong-correlation effects.
  • To evaluate the performance of the new functional, LH25nP, for main-group energetics and reaction energies.
  • To assess the functional's ability to address spin-related issues in various chemical systems.

Main Methods:

  • Development of the LH25nP functional featuring a neural-network local mixing function (n-LMF) optimized with a fixed strong-correlation factor.
  • Rigorous testing using the GMTKN55 benchmark set for main-group energetics and the W4-11RE set for reaction energies.
  • Evaluation of fractional-spin errors and performance in spin-restricted bond dissociation and spin-contamination problems.

Main Results:

  • LH25nP-D4 achieved a record low self-consistent WTMAD-2 value of 2.47 kcal/mol on the GMTKN55 set, the best for a rung 4 functional.
  • The functional demonstrated the lowest mean absolute deviations (2.4 kcal/mol) on the W4-11RE reaction energy set for rung 4 functionals.
  • Significant improvements were observed in fractional-spin errors, spin-restricted dissociation of covalent bonds, and spin-contamination issues in transition-metal complexes.

Conclusions:

  • The LH25nP functional represents a significant advancement in local hybrid functionals, effectively mitigating self-interaction and static-correlation errors.
  • Its performance on benchmark datasets and for spin-related problems suggests a departure from the typical trade-offs in functional development.
  • Further optimization including transition-metal data is recommended to enhance transferability to organometallic systems.