Exploring the Nexus of Many-Body Theories through Neural Network Techniques: the Tangent Model

TL;DR

This paper introduces a neural network-based tangent model that efficiently represents effective interactions in chemical systems, revealing a tangent relationship between bare and effective interactions and connecting to existing theoretical frameworks.

Contribution

The paper develops a physically informed neural network approach to model effective interactions, uncovering a tangent function relationship and linking it to prior theoretical analyses.

Findings

Neural network efficiently evaluates effective interactions across geometries.

Reveals a tangent function relationship between bare and effective interactions.

Connects the tangent model to existing theoretical analyses.

Abstract

In this paper, we present a physically informed neural network representation of the effective interactions associated with coupled-cluster downfolding models to describe chemical systems and processes. The neural network 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.