Login / Signup

Automatic Differentiation for the Direct Minimization Approach to the Hartree-Fock Method.

Naruki YoshikawaMasato Sumita
Published in: The journal of physical chemistry. A (2022)
Automatic differentiation (AD) has become an important tool for optimization problems in computational science, and it has been applied to the Hartree-Fock method. Although the reverse-mode AD is more efficient than the forward-mode, eigenvalue calculation in the self-consistent field (SCF) method has impeded the use of the reverse-mode AD. Here, we propose a method to directly minimize Hartree-Fock energy under the orthonormality constraint of the molecular orbitals using reverse-mode AD by avoiding eigenvalue calculation. According to our validation, the proposed method was more stable than the conventional SCF method and achieved comparable accuracy.
Keyphrases
  • mental health
  • deep learning
  • machine learning
  • clinical evaluation