Fast and spectrally accurate construction of adaptive diagonal basis sets for electronic structure

• URL: http://arxiv.org/abs/2407.06171v1
• Date: Mon, 8 Jul 2024 17:48:40 GMT
• Title: Fast and spectrally accurate construction of adaptive diagonal basis sets for electronic structure
• Authors: Michael Lindsey, Sandeep Sharma,
• Abstract summary: We combine the periodic sinc basis set with a curvilinear coordinate system for electronic structure calculations. We address two key challenges that arise while using basis sets obtained by such a coordinate transformation. The solution of both of these challenges enables mean-field calculations at a cost that is log-linear in the number of basis functions.
• Score: 2.2489531925874013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this article, we combine the periodic sinc basis set with a curvilinear coordinate system for electronic structure calculations. This extension allows for variable resolution across the computational domain, with higher resolution close to the nuclei and lower resolution in the inter-atomic regions. We address two key challenges that arise while using basis sets obtained by such a coordinate transformation. First, we use pseudospectral methods to evaluate the integrals needed to construct the Hamiltonian in this basis. Second, we demonstrate how to construct an appropriate coordinate transformation by solving the Monge-Amp\`ere equation using a new approach that we call the cyclic Knothe-Rosenblatt flow. The solution of both of these challenges enables mean-field calculations at a cost that is log-linear in the number of basis functions. We demonstrate that our method approaches the complete basis set limit faster than basis sets with uniform resolution. We also emphasize how these basis sets satisfy the diagonal approximation, which is shown to be a consequence of the pseudospectral method. The diagonal approximation is highly desirable for the solution of the electronic structure problem in many frameworks, including mean field theories, tensor network methods, quantum computing, and quantum Monte Carlo.

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