JOURNAL ARTICLE

Improved Gaussian basis sets for norm-conserving 4f-in-core pseudopotentials of trivalent lanthanides (Ln = Ce–Lu).

  • Published In: Journal of Chemical Physics, 2024, v. 161, n. 13. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lu, Jun-Bo; Zhang, Yang-Yang; Jiang, Xue-Lian; Ye, Lian-Wei; Li, Jun 3 of 3

Abstract

This article focuses on the development and optimization of atom-centered Gaussian basis sets, named LnBS2*, tailored for condensed-phase simulations of trivalent lanthanide systems using scalar-relativistic 4f-in-core norm-conserving Goedecker–Teter–Hutter (GTH) pseudopotentials (PPs). The authors introduce a unified, self-consistent optimization approach that simultaneously considers total energy, relative energy, condition number of the overlap matrix, and atomic orbital occupation to improve accuracy, transferability, and numerical stability over previous basis sets (LnBS2). Benchmarking against molecular and condensed-phase lanthanide systems—including lanthanide trihalides and aqueous solutions—demonstrates that LnBS2* basis sets yield lower total and relative energies, maintain reasonable condition numbers to avoid linear dependency issues, and reproduce structural and physical properties consistent with experimental and all-electron reference data. These optimized basis sets facilitate large-scale first-principles and ab initio molecular dynamics simulations of structurally complex lanthanide-containing materials.

Additional Information

  • Source:Journal of Chemical Physics. 2024/10, Vol. 161, Issue 13, p1
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2024
  • ISSN:0021-9606
  • DOI:10.1063/5.0228388
  • Accession Number:180155599
  • Copyright Statement:Copyright of Journal of Chemical Physics is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.