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Size Consistent Excited States via Algorithmic Transformations between Variational Principles.

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Excited state variational principles often lack size consistency. Our new method transforms principles to ensure state selectivity and size consistency, improving quantum Monte Carlo calculations for excited states.

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Area of Science:

  • Quantum Chemistry
  • Computational Physics

Background:

  • Variational principles are fundamental in quantum mechanics.
  • Excited state calculations present unique challenges, including size consistency issues.

Purpose of the Study:

  • To address the lack of size consistency in existing excited state variational principles.
  • To develop a novel, size-consistent approach for excited state optimization.
  • To ensure compatibility with quantum Monte Carlo methods.

Main Methods:

  • Transformation between different variational principles.
  • Development of a state-selective optimization technique.
  • Integration with quantum Monte Carlo simulations.

Main Results:

  • Demonstrated that many excited state variational principles are not size consistent.
  • Developed and tested a novel approach that achieves state selectivity and size consistency.
  • Confirmed compatibility with quantum Monte Carlo methods through numerical examples.

Conclusions:

  • The proposed method offers a more robust and "black box" approach to calculating excited states.
  • This work overcomes a significant limitation in applying variational principles to excited states.
  • The approach enhances the reliability of quantum Monte Carlo for excited state investigations.