Compression of the electron repulsion integral tensor in tensor hypercontraction format with cubic scaling cost

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2015-12-01

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© 2015 Elsevier Inc.Electron repulsion integral tensor has ubiquitous applications in electronic structure computations. In this work, we propose an algorithm which compresses the electron repulsion tensor into the tensor hypercontraction format with O(nN2logN) computational cost, where N is the number of orbital functions and n is the number of spatial grid points that the discretization of each orbital function has. The algorithm is based on a novel strategy of density fitting using a selection of a subset of spatial grid points to approximate the pair products of orbital functions on the whole domain.

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Published Version (Please cite this version)

10.1016/j.jcp.2015.09.014

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Lu, J, and L Ying (2015). Compression of the electron repulsion integral tensor in tensor hypercontraction format with cubic scaling cost. Journal of Computational Physics, 302. pp. 329–335. 10.1016/j.jcp.2015.09.014 Retrieved from https://hdl.handle.net/10161/14102.

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Lu

Jianfeng Lu

James B. Duke Distinguished Professor of Mathematics

Jianfeng Lu is an applied mathematician interested in mathematical analysis and algorithm development for problems from computational physics, theoretical chemistry, materials science, machine learning, and other related fields.

More specifically, his current research focuses include:
High dimensional PDEs; generative models and sampling methods; control and reinforcement learning; electronic structure and many body problems; quantum molecular dynamics; multiscale modeling and analysis.


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