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A posteriori error estimation for the non-self-consistent Kohn-Sham equations.
Michael F Herbst1, Antoine Levitt, Eric Cancès
1CERMICS, Ecole des Ponts and Inria Paris, 6 & 8 Avenue Blaise Pascal, 77455 Marne-la-Vallée, France. michael.herbst@inria.fr antoine.levitt@inria.fr eric.cances@enpc.fr.
This study provides guaranteed error bounds for numerical solutions of Kohn-Sham equations. The findings ensure accuracy in electronic structure calculations, crucial for materials science.
Area of Science:
- Computational Physics
- Quantum Chemistry
- Materials Science
Background:
- Numerical solutions of Kohn-Sham equations are essential for electronic structure calculations.
- Errors arise from basis set limitations, iterative convergence, and floating-point arithmetic.
- Rigorous error bounding is critical for reliable computational results.
Purpose of the Study:
- To develop and apply a methodology for rigorously bounding numerical errors in Kohn-Sham equation solutions.
- To quantify errors stemming from basis set finiteness, convergence thresholds, and rounding errors.
- To demonstrate the practical application of error bounding in electronic structure analysis.
Main Methods:
- Development of a rigorous mathematical framework for error analysis.
- Computation of fully-guaranteed bounds for non-self-consistent equations.
- Utilizing pseudopotential approximation within a plane-wave basis set.
- Application to calculate silicon band structures with annotated error bars.
Main Results:
- Successfully computed fully-guaranteed error bounds for the numerical solution.
- Quantified combined errors from basis set, convergence, and rounding.
- Presented silicon band structure diagrams with precise error annotations.
- Validated the methodology for reliable electronic structure predictions.
Conclusions:
- The developed methodology provides reliable error bounds for Kohn-Sham equation solutions.
- Accurate electronic structure calculations are achievable with quantified error control.
- This approach enhances the trustworthiness of computational materials science predictions.
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