Exploring the nexus of many-body theories through neural network techniques: the tangent model

Liang, Senwei; Kowalski, Karol; Yang, Chao; Bauman, Nicholas P.

doi:10.1088/2632-2153/add78c

Title: Exploring the nexus of many-body theories through neural network techniques: the tangent model

Journal Article · 21 May 2025 · Machine Learning: Science and Technology

DOI: https://doi.org/10.1088/2632-2153/add78c · OSTI ID:2568199

Liang, Senwei ^[1]; Kowalski, Karol ^[2];

^[1];

^[3]

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Univ. of Washington, Seattle, WA (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

In this paper, we present a physically informed neural network (NN) representation of the effective interactions associated with coupled-cluster downfolding models to describe chemical systems and processes. The NN representation not only allows us to evaluate the effective interactions efficiently for various geometrical configurations of chemical systems corresponding to various levels of complexity of the underlying wave functions, but also reveals that the bare and effective interactions are related by a tangent function of some latent variables. We refer to this characterization of the effective interaction as a tangent model. We discuss the connection between this tangent model for the effective interaction with the previously developed theoretical analysis that examines the difference between the bare and effective Hamiltonians in the corresponding active spaces.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: National Energy Research Scientific Computing (NERSC); US Department of Energy; USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22), Scientific User Facilities Division (SC-22.3 ); USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2568199

Report Number(s):: PNNL-SA--208006

Journal Information:: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 2 Vol. 6; ISSN 2632-2153

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Exploring the nexus of many-body theories through neural network techniques: the tangent model

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