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Solution of Schrödinger’s equation for large systems
Abstract
Iterative diagonalization of the Hamiltonian matrix is required to solve very large electronic-structure problems. Present algorithms are limited in their convergence rates at low wave numbers by stability problems associated with large changes in the Hartree potential, and at high wave numbers with large changes in the kinetic energy. A new method is described which includes the effect of density changes on the potentials and properly scales the changes in kinetic energy. The use of this method has increased the rate of convergence by over an order of magnitude for large problems.
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Citations
Cited by
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Barnett, R. N., & Landman, U. (1993) · Physical Review B · doi:10.1103/physrevb.48.2081 · Cited by 629
Metadata
- Authors
- Teter, M. P., Payne, M. C., & Allan, D. C.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 1989-12-15
- Volume
- 40
- Issue
- 18
- Pages
- 12255-12263
- ISSN
- 0163-1829 (ISSN-L)
- OpenAlex
- W1995469798
- Cited by
- 1,115
- References
- 0
- FWCI
- 14.81
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Solution of Schrödinger’s equation for large systems. https://app.cochat.ai/papers/10.1103/physrevb.40.12255
- The paper
- Teter, M. P., Payne, M. C., & Allan, D. C. (1989). Solution of Schrödinger’s equation for large systems. Physical Review B. https://doi.org/10.1103/physrevb.40.12255
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
