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Second-Order Self-Consistent Field Algorithms: From Classical to Quantum Nuclei.

Robin FeldmannAlberto BaiardiMarkus Reiher
Published in: Journal of chemical theory and computation (2023)
This work presents a general framework for deriving exact and approximate Newton self-consistent field (SCF) orbital optimization algorithms by leveraging concepts borrowed from differential geometry. Within this framework, we extend the augmented Roothaan-Hall (ARH) algorithm to unrestricted electronic and nuclear-electronic calculations. We demonstrate that ARH yields an excellent compromise between stability and computational cost for SCF problems that are hard to converge with conventional first-order optimization strategies. In the electronic case, we show that ARH overcomes the slow convergence of orbitals in strongly correlated molecules with the example of several iron-sulfur clusters. For nuclear-electronic calculations, ARH significantly enhances the convergence already for small molecules, as demonstrated for a series of protonated water clusters.
Keyphrases
  • density functional theory
  • machine learning
  • molecular dynamics
  • deep learning
  • molecular dynamics simulations
  • mental health
  • monte carlo
  • quantum dots