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Area of Science:

  • Quantum Chemistry
  • Computational Chemistry
  • Strongly Correlated Systems

Background:

  • Density matrix renormalization group (DMRG) efficiently handles static correlation in large active spaces.
  • Dynamic correlation is crucial for strongly correlated systems but computationally expensive with traditional methods.
  • Existing DMRG-MRCI methods face limitations with large active spaces and basis sets due to high computational costs.

Purpose of the Study:

  • To develop a more scalable and efficient DMRG-MRCI method for quantum chemistry.
  • To overcome the computational bottlenecks associated with high-order RDMs and MRCI Hamiltonian dimensions.
  • To enable accurate electronic structure calculations for larger and more complex molecular systems.

Main Methods:

  • Implemented a novel DMRG-MRCI approach using reconstructed CASCI-type configurations from DMRG wave functions.
  • Employed the entropy-driving genetic algorithm (EDGA) for configuration reconstruction.
  • Integrated DMRG with MRCI using an external contraction (ec) scheme, bypassing internal contraction limitations.

Main Results:

  • The new DMRG-ec-MRCI method effectively bypasses the computation of high-order RDMs.
  • The number of MRCI configurations is independent of the virtual orbital count, reducing computational cost.
  • Demonstrated the method's capability on benchmark systems like Cr2, acenes, and a lanthanide complex.

Conclusions:

  • The proposed DMRG-ec-MRCI method significantly enhances the applicability of accurate quantum chemical calculations.
  • It allows for larger active spaces (beyond 30 orbitals) and larger basis sets.
  • This advancement is crucial for studying complex, strongly correlated molecules with unprecedented accuracy.