Skip to content
Preprint

Benchmarking Least-Squares Tensor Hypercontraction Techniques for Molecular Systems

Aug 2026 · 0 citations · 90 references
Physics

Abstract

Finding a low-rank approximation for the two-electron integral (ERI) tensor is a crucial step towards reducing the computational cost of many-body electronic structure methods. In recent years, a range of new tensor factorization techniques, like the so-called tensor-hypercontraction (THC) approach, have been developed to achieve this goal. Unfortunately, a systematic comparison of accuracy and efficiency of different THC construction algorithms is missing. In this work, we address this gap by benchmarking these techniques across a wide range of molecular systems. In addition to offering useful insights into the advantages and disadvantages of the different techniques, we provide reference implementations in the newly developed open-source library, PyTHC.

View source

Similar papers

Preprint Sep 2026

Stochastic Tensor Contraction for Efficient MP2 Exchange

Second-order Moller--Plesset perturbation theory (MP2) is one of the simplest correlated wave-function methods, but its conventional $O(N^5)$ cost limits its application to large systems. Stochastic tensor contraction (STC) has recently appeared as a general technique to evaluate high-order tensor contractions in quant...

Jia-Ce Sun, G. Chan · 1 citation
Preprint Sep 2026

Benchmarking higher-ranking multipoles and polarizability tensors for small molecular systems

Molecular properties govern how molecules interact with one another or external fields. Accurate molecular properties are essential for constructing intermolecular interaction models, and to achieve high accuracy we need to go beyond leading-order (dipolar) terms. This work presents CCSD(T) references for 73 small non-...

Bruno V. von Bruening, A. Misquitta · 0 citations
Preprint Oct 2026

Overcoming the limitations of body-ordered potentials for atomistic machine learning

Machine-learning interatomic potentials have become indispensable tools in atomistic simulations. The most popular models rely heavily on physical priors, such as locality, smoothness and symmetry, that are assumed to improve the accuracy and transferability of the trained models. However, recent evidence suggests that...

Filippo Bigi, M. Domina, Maximilian L. Ach et al. · 0 citations
Open access Sep 2026

Reducing the complexity of sum-of-products potential energy surfaces for efficient MCTDH calculations.

The efficiency of the multi-configuration time-dependent Hartree (MCTDH) method depends critically on the availability of a compact sum-of-products (SOP) representation of the potential energy surface (PES). For medium- and high-dimensional molecular systems, conventional SOP representations often contain thousands of...

Hervé Tajouo Tela, Sosthène Irambona, Ayda Badri et al. · 0 citations
Preprint Sep 2026

Load balancing for adaptive-precision interatomic potentials in materials science

Load balancing a staggered grid with ALL is found to be significantly faster than load balancing an irregular grid with the bisectioning method of LAMMPS, but the communication costs during a regular timestep are lower for the irregular grid of domains, as a lower surface-by-volume ratio of the domains is reached.

David Immel, G. Sutmann · 0 citations
#machine learning Preprint Oct 2026

FlashCart: Fast Cartesian Tensor Products for Equivariant Interatomic Potentials

Machine-learned interatomic potentials extend atomistic simulations beyond the length- and timescales accessible to electronic-structure methods. However, the computational cost of equivariant architectures limits the local correlations they can represent in practice and therefore their achievable accuracy. Here we int...

V. Zaverkin, P. Goodarzi, S. Sukhomlinov et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.