Physics-Driven Construction of Compact Primitive Gaussian Density Fitting Basis Sets
Kshitijkumar A. Surjuse, Edward F. Valeev

TL;DR
This paper introduces a physics-based algorithm for generating primitive Gaussian density fitting basis sets that are accurate, robust, and applicable across the periodic table, improving electronic structure calculations.
Contribution
The MADF algorithm constructs density fitting basis sets from a contracted basis, using physical principles to minimize parameters and optimize accuracy across various elements and computational methods.
Findings
Achieved microhartree-level accuracy in energy calculations.
Applicable to a wide range of elements and basis set qualities.
Reduced the number of parameters needed for accurate density fitting.
Abstract
We present model-assisted density fitting (MADF) basis set generator, an algorithm for generating primitive atomic Gaussian density fitting (DF) basis sets (DFBSs) from a contracted Gaussian orbital basis set (OBS). The MADF algorithm produces DFBSs suitable for accurate robust DF approximation of 2-particle interactions in mean-field and correlated electronic structure. The algorithm is designed to (a) saturate the OBS product space by a large regularized set of primitive solid-harmonic Gaussian shells with nonuniform distribution of exponents followed by (b) pruning of the shells according to their contributions to the 2-body energy of a correlated atomic ensemble. Building the DFBS generator model almost exclusively on mathematical and physical principles allows one to limit the number of parameters that control the density fitting error to three, with a single set of parameters…
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Taxonomy
TopicsMachine Learning and Data Classification · Face and Expression Recognition · Advanced Image and Video Retrieval Techniques
