Issue 8, 2024

Economical quasi-Newton unitary optimization of electronic orbitals

Abstract

We present an efficient quasi-Newton orbital solver optimized to reduce the number of gradient evaluations and other computational steps of comparable cost. The solver optimizes orthogonal orbitals by sequences of unitary rotations generated by the (preconditioned) limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm equipped with trust-region step restriction. The low-rank structure of the L-BFGS inverse Hessian is exploited when solving the trust-region problem. The efficiency of the proposed “Quasi-Newton Unitary Optimization with Trust-Region” (QUOTR) solver is compared to that of the standard Roothaan–Hall approach accelerated by the Direct Inversion of Iterative Subspace (DIIS), and other exact and approximate Newton solvers for mean-field (Hartree–Fock and Kohn–Sham) problems.

Graphical abstract: Economical quasi-Newton unitary optimization of electronic orbitals

Supplementary files

Article information

Article type
Paper
Submitted
15 Huk 2023
Accepted
19 N’w 2023
First published
19 N’w 2023

Phys. Chem. Chem. Phys., 2024,26, 6557-6573

Economical quasi-Newton unitary optimization of electronic orbitals

S. A. Slattery, K. A. Surjuse, C. C. Peterson, D. A. Penchoff and E. F. Valeev, Phys. Chem. Chem. Phys., 2024, 26, 6557 DOI: 10.1039/D3CP05557D

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements