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Tensor-Hypercontracted MP2 First Derivatives: Runtime and Memory Efficient Computation of Hyperfine Coupling Constants

[Image: see text] We employ our recently introduced tensor-hypercontracted (THC) second-order Møller–Plesset perturbation theory (MP2) method [Bangerter, F. H., Glasbrenner, M., Ochsenfeld, C. J. Chem. Theory Comput.2021, 17, 211–221] for the computation of hyperfine coupling constants (HFCCs). The...

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Detalles Bibliográficos
Autores principales: Bangerter, Felix H., Glasbrenner, Michael, Ochsenfeld, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9476664/
https://www.ncbi.nlm.nih.gov/pubmed/35943450
http://dx.doi.org/10.1021/acs.jctc.2c00118
Descripción
Sumario:[Image: see text] We employ our recently introduced tensor-hypercontracted (THC) second-order Møller–Plesset perturbation theory (MP2) method [Bangerter, F. H., Glasbrenner, M., Ochsenfeld, C. J. Chem. Theory Comput.2021, 17, 211–221] for the computation of hyperfine coupling constants (HFCCs). The implementation leverages the tensor structure of the THC factorized electron repulsion integrals for an efficient formation of the integral-based intermediates. The computational complexity of the most expensive and formally quintic scaling exchange-like contribution is reduced to effectively subquadratic, by making use of the intrinsic, exponentially decaying coupling between tensor indices through screening based on natural blocking. Overall, this yields an effective subquadratic scaling with a low prefactor for the presented THC-based AO-MP2 method for the computation of isotropic HFCCs on DNA fragments with up to 500 atoms and 5000 basis functions. Furthermore, the implementation achieves considerable speedups with up to a factor of roughly 600–1000 compared to previous implementations [Vogler, S., Ludwig, M., Maurer, M., Ochsenfeld, C. J. Chem. Phys.2017, 147, 024101] for medium-sized organic radicals, while also significantly reducing storage requirements.