Iterative Implementation of the Dipole Interaction Model for Atomic Polarizabilities.
Raphael F Ligorio1, Leonardo H R Dos Santos2, Anna Krawczuk1
1Institute of Inorganic Chemistry, University of Goettingen, Goettingen, Germany.
Journal of Computational Chemistry
|June 27, 2025
Summary
This study introduces a direct iterative method for calculating polarizabilities using the dipole interaction model (DIM), overcoming limitations of traditional matrix inversion. This approach enables precise, large-scale atomic system calculations efficiently.
Area of Science:
- Computational chemistry
- Materials science
- Quantum mechanics
Background:
- The dipole interaction model (DIM) is used for calculating atomic dipole moments and polarizabilities.
- Traditional DIM polarizability calculations involve matrix inversion, which is computationally expensive and memory-intensive.
- Existing iterative methods improve speed but often compromise accuracy.
Purpose of the Study:
- To develop a direct iterative approach for computing polarizabilities within the DIM framework.
- To overcome the computational and memory constraints of traditional matrix inversion methods.
- To enable accurate polarizability calculations for large-scale atomic systems.
Main Methods:
- A novel direct iterative method for polarizability computation was developed, avoiding matrix inversion.
- The method addresses memory limitations by optimizing the storage of large arrays.
- The approach allows for scaling to systems with hundreds of thousands of atoms.
Main Results:
- The new method achieves accurate polarizability calculations without matrix inversion.
- It significantly improves computational efficiency and reduces memory requirements.
- The approach scales effectively to very large atomic systems without precision loss.
Conclusions:
- The developed direct iterative method offers an efficient and precise alternative for DIM polarizability calculations.
- This advancement facilitates the study of polarizabilities in large and complex systems.
- The method has broad applicability in computational chemistry and materials science.
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