## 4 Conclusions and Summary  
In summary, we have demonstrated, for the first time, the classification of transmission eigenchannels of individual disordered nanophotonic structures using supervised machine  
Figure 8: The confusion matrix corresponding to classification performed using a decision tree trained on a dataset with 30-component output vectors. The data correspond to ten transmission eigenchannels of three devices with three different scattering strengths \(N_{s}\). This classification allows for identification of both the eigenchannel as well as the disorder strength. The ports corresponding to the respective three devices are color coded. The maximum value in any cell can be 6000.  
learning techniques. We show that along with classification of transmission eigenchannels it is also possible to classify the photonic device based on its disorder strength for a single configuration utilizing the spatial distribution of the energy density of the transmission eigenchannels. To perform this classification, we used supervised learning methods, namely decision trees and fully connected neural networks. In both cases, we achieved an accuracy of classification greater than 99%. While we performed the classification using disordered photonic structures, our approach is general and should be applicable to other photonic systems, such as metasurfaces. Although in this work we did not perform inverse design, our proof-of-concept results can open new directions for the inverse design of photonic devices. The classification of different structures based on different desired properties can be utilized as a first step in performing optimizations that will lead to the desired photonics devices. This can be especially important for applications in nonlinear optics, where simultaneous optimization of multiple quantities, such as field enhancements, field overlaps, and emission directionality is required [37], and where optimizing one of these quantities does not necessarily optimize the others. In such multi-parameter-based optimizations, performing the classification followed by an optimization on a smaller parameter space can be a more efficient technique for achieving an optimum design. This will be investigated in future studies. Finally, there has recently been a growing interest in two-dimensional disordered silicon-based photonics on-chip devices for numerous applications ranging from spectroscopy and lasing to sensing. Such photonic devices are also excellent platforms for fundamental studies related to wave interference effects, such as probing Anderson localization [23; 24], and mesoscopic correlations [25; 26; 27]. Since it is relatively easy to probe the energy densities of single devices in such systems by measuring the out-of-plane scattering, combining such measurements with machine learning algorithms could provide additional degrees of freedom for optimizing the light-matter interactions. This could be of paramount importance for applications such as random lasing and sensing. Conceptualization, R.S. and J.B.; methodology, R.S and A.P. ; software, R.S. and A.P. ; validation, R.S., A.P., B.S. and J.B.; formal analysis, R.S., A.P. ; writing--original draft preparation, R.S., A.P. ; writing--review and editing, R.S., A.P., B.S., J.B.; supervision, R.S., J.B.; project administration, J.B. All authors have read and agreed to the published version of the manuscript. This work was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Material Sciences and Engineering and performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.