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Automatic State Interaction with Large Localized Active Spaces for Multimetallic Systems.

Valay Agarawal1, Daniel S King1, Matthew R Hermes1

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|May 24, 2024
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Summary
This summary is machine-generated.

This study introduces an automated framework for defining localized active space self-consistent field (LASSCF) fragments and localized active space state interaction (LASSI) model spaces. The new LASSI[r, q] method efficiently converges to the complete active space configuration interaction (CASCI) limit with significantly fewer states.

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Area of Science:

  • Quantum Chemistry
  • Computational Chemistry
  • Electronic Structure Theory

Background:

  • Localized Active Space Self-Consistent Field (LASSCF) method factorizes wave functions into localized fragments.
  • Localized Active Space State Interaction (LASSI) reintroduces correlation via Hamiltonian diagonalization.
  • Optimal procedures for defining LASSCF fragments and LASSI model spaces are currently unknown.

Purpose of the Study:

  • To present an automated framework for exploring systematically convergent model spaces in LASSI.
  • To introduce the LASSI[r, q] method, requiring only user-defined parameters r and q.
  • To demonstrate efficient convergence to the Complete Active Space Configuration Interaction (CASCI) limit.

Main Methods:

  • Developed an automated framework to systematically explore model spaces for LASSI.
  • Introduced the LASSI[r, q] method, controlled by electron hops (r) and fragment basis functions (q).
  • The method converges to CASCI as r and q approach infinity.

Main Results:

  • Numerical tests on trimetal oxo-centered complexes show efficient convergence to the CASCI limit.
  • LASSI[r, q] requires 4-10 orders of magnitude fewer states compared to traditional CASCI.
  • The framework provides a systematic and efficient approach to defining LASSI model spaces.

Conclusions:

  • The LASSI[r, q] method offers a computationally efficient alternative to CASCI for electronic structure calculations.
  • This automated framework simplifies the process of defining model spaces for correlated systems.
  • The approach significantly reduces the number of states needed for accurate calculations.