التفاصيل البيبلوغرافية
| العنوان: |
Susceptibility formulation of density matrix perturbation theory. |
| المؤلفون: |
Niklasson, Anders M. N.1 (AUTHOR) amn@lanl.gov, Habib, Adela1 (AUTHOR), Finkelstein, Joshua D.1 (AUTHOR), Rubensson, Emanuel H.2 (AUTHOR) |
| المصدر: |
Journal of Chemical Physics. 12/21/2024, Vol. 161 Issue 23, p1-15. 15p. |
| مصطلحات موضوعية: |
*ARTIFICIAL neural networks, *DENSITY matrices, *MATRICES (Mathematics), *PERTURBATION theory, *GRAPHICS processing units |
| مستخلص: |
Density matrix perturbation theory based on recursive Fermi-operator expansions provides a computationally efficient framework for time-independent response calculations in quantum chemistry and materials science. From a perturbation in the Hamiltonian, we can calculate the first-order perturbation in the density matrix, which then gives us the linear response in the expectation values for some chosen set of observables. We present an alternative, dual formulation, where we instead calculate the static susceptibility of an observable, which then gives us the linear response in the expectation values for any number of different Hamiltonian perturbations. We show how the calculation of the susceptibility can be performed with the same expansion schemes used in recursive density matrix perturbation theory, including generalizations to fractional occupation numbers and self-consistent linear response calculations, i.e., similar to density functional perturbation theory. As with recursive density matrix perturbation theory, the dual susceptibility formulation is well suited for numerically thresholded sparse matrix algebra, which has linear scaling complexity for sufficiently large sparse systems. Similarly, the recursive computation of the susceptibility also seamlessly integrates with the computational framework of deep neural networks used in artificial intelligence (AI) applications. This integration enables the calculation of quantum response properties that can leverage cutting-edge AI-hardware, such as NVIDIA Tensor Cores or Google Tensor Processing Units. We demonstrate performance for recursive susceptibility calculations using NVIDIA Graphics Processing Units and Tensor Cores. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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