Stabilized quasi-Newton optimization of noisy potential energy surfaces
- Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel (Switzerland)
- Institute for Advanced Studies in Basic Sciences, P.O. Box 45195-1159, IR-Zanjan (Iran, Islamic Republic of)
- Computational and Systems Biology, Biozentrum, University of Basel, CH-4056 Basel (Switzerland)
Optimizations of atomic positions belong to the most commonly performed tasks in electronic structure calculations. Many simulations like global minimum searches or characterizations of chemical reactions require performing hundreds or thousands of minimizations or saddle computations. To automatize these tasks, optimization algorithms must not only be efficient but also very reliable. Unfortunately, computational noise in forces and energies is inherent to electronic structure codes. This computational noise poses a severe problem to the stability of efficient optimization methods like the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm. We here present a technique that allows obtaining significant curvature information of noisy potential energy surfaces. We use this technique to construct both, a stabilized quasi-Newton minimization method and a stabilized quasi-Newton saddle finding approach. We demonstrate with the help of benchmarks that both the minimizer and the saddle finding approach are superior to comparable existing methods.
- OSTI ID:
- 22416002
- Journal Information:
- Journal of Chemical Physics, Vol. 142, Issue 3; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); ISSN 0021-9606
- Country of Publication:
- United States
- Language:
- English
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