A T-matrix database to promote information-driven research in nanophotonics

TL;DR

This paper introduces a standardized, web-based T-matrix database for nanophotonics, facilitating data-driven research by providing accessible, well-annotated scatterer response data for machine learning and inverse design applications.

Contribution

The authors present the Daphona T-matrix portal, a novel infrastructure for sharing and standardizing T-matrices, enabling efficient data reuse and accelerating research in nanophotonics.

Findings

Demonstrated use of the database for surrogate forward modeling.

Showcased inverse design applications using T-matrix data.

Enabled resource-efficient exploration of photonic systems.

Abstract

Information-driven methods from machine learning and artificial intelligence for exploring the optical response of metasurfaces and, more generally, photonic systems rely on well-annotated datasets for training. For metasurfaces made from a periodic or aperiodic arrangement of scatterers, the primary information encoding their response is the optical properties of these individual scatterers. In the linear regime, that response is entirely contained in the transition or T-matrix of the individual scatterer. However, despite the widespread use of these T-matrices in exploring advanced photonic materials within the larger community, there is no common infrastructure for distributing them with consistent metadata and a standard representation. That would be important to avoid the repetitive, resource-intensive computation of these T-matrices by researchers worldwide and to enable data-driven research. To overcome this limitation, we introduce the Daphona T-matrix portal at https://tmatrix.scc.kit.edu/, a web-based platform for interactive searching, filtering, and exporting standardized data containing structure-property relations for a wide range of scatterers, as expressed by their T-matrices. Besides introducing this infrastructure, we demonstrate how the available data enables addressing scientific questions in the broader context of information-driven research. The multiple illustrative examples in our contribution cover both surrogate forward models and inverse design models, and operate either directly on the T-matrix or alternatively on optical observables of metasurfaces made from these scatterers.