Discovering nonlinear resonances through physics-informed machine learning

TL;DR

This paper introduces a physics-informed machine learning method to efficiently identify system configurations that optimize transfer properties in nonlinear systems, including molecules and photonic systems.

Contribution

The paper presents a novel PIML approach that finds optimal parameters for targeted energy transfer in nonlinear systems without requiring data training.

Findings

Successfully recovers known TET results

Discovers resonant paths in a trimer system

Applicable to molecular and photonic system design

Abstract

For an ensemble of nonlinear systems that model, for instance, molecules or photonic systems, we propose a method that finds efficiently the configuration that has prescribed transfer properties. Specifically, we use physics-informed machine-learning (PIML) techniques to find the parameters for the efficient transfer of an electron (or photon) to a targeted state in a non-linear dimer. We create a machine learning model containing two variables, $\chi_D$, and $\chi_A$, representing the non-linear terms in the donor and acceptor target system states. We then introduce a data-free physics-informed loss function as $1.0 - P_j$, where $P_j$ is the probability, the electron being in the targeted state, $j$. By minimizing the loss function, we maximize the occupation probability to the targeted state. The method recovers known results in the Targeted Energy Transfer (TET) model, and it is then applied to a more complex system with an additional intermediate state. In this trimer configuration, the PIML approach discovers desired resonant paths from the donor to acceptor units. The proposed PIML method is general and may be used in the chemical design of molecular complexes or engineering design of quantum or photonic systems.