Login / Signup

Excited-State DMRG Made Simple with FEAST.

Alberto BaiardiAnna Klára KelemenMarkus Reiher
Published in: Journal of chemical theory and computation (2021)
We introduce DMRG[FEAST], a new method for optimizing excited-state many-body wave functions with the density matrix renormalization group (DMRG) algorithm. Our approach applies the FEAST algorithm, originally designed for large-scale diagonalization problems, to matrix product state wave functions. We show that DMRG[FEAST] enables the stable optimization of both low- and high-energy eigenstates, therefore overcoming the limitations of state-of-the-art excited-state DMRG algorithms. We demonstrate the reliability of DMRG[FEAST] by calculating anharmonic vibrational excitation energies of molecules with up to 30 fully coupled degrees of freedom.
Keyphrases
  • machine learning
  • deep learning
  • density functional theory
  • mental health
  • energy transfer
  • neural network