Dataset Information

Optimization of metamaterials and metamaterial-microcavity based on deep neural networks.

ABSTRACT: Computational inverse-design and forward prediction approaches provide promising pathways for on-demand nanophotonics. Here, we use a deep-learning method to optimize the design of split-ring metamaterials and metamaterial-microcavities. Once the deep neural network is trained, it can predict the optical response of the split-ring metamaterial in a second which is much faster than conventional simulation methods. The pretrained neural network can also be used for the inverse design of split-ring metamaterials and metamaterial-microcavities. We use this method for the design of the metamaterial-microcavity with the absorptance peak at 1310 nm. Experimental results verified that the deep-learning method is a fast, robust, and accurate method for designing metamaterials with complex nanostructures.

SUBMITTER: Lan G

PROVIDER: S-EPMC9680957 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Optimization of metamaterials and metamaterial-microcavity based on deep neural networks.

Lan Guoqiang G Wang Yu Y Ou Jun-Yu JY

Nanoscale advances 20221028 23

Computational inverse-design and forward prediction approaches provide promising pathways for on-demand nanophotonics. Here, we use a deep-learning method to optimize the design of split-ring metamaterials and metamaterial-microcavities. Once the deep neural network is trained, it can predict the optical response of the split-ring metamaterial in a second which is much faster than conventional simulation methods. The pretrained neural network can also be used for the inverse design of split-ring ...[more]

PMID: 36504733

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Project description:The recent trend in using network and graph structures to represent a variety of different data types has renewed interest in the graph partitioning (GP) problem. This interest stems from the need for general methods that can both efficiently identify network communities and reduce the dimensionality of large graphs while satisfying various application-specific criteria. Traditional clustering algorithms often struggle to capture the complex relationships within graphs and generalize to arbitrary clustering criteria. The emergence of graph neural networks (GNNs) as a powerful framework for learning representations of graph data provides new approaches to solving the problem. Previous work has shown GNNs to be capable of proposing partitionings using a variety of criteria. However, these approaches have not yet been extended to Markov chains or kinetic networks. These arise frequently in the study of molecular systems and are of particular interest to the biomolecular modeling community. In this work, we propose several GNN-based architectures to tackle the GP problem for Markov Chains described as kinetic networks. This approach aims to maximize the Kemeny constant, which is a variational quantity and it represents the sum of time scales of the system. We propose using an encoder-decoder architecture and show how simple GraphSAGE-based GNNs with linear layers can outperform much larger and more expressive attention-based models in this context. As a proof of concept, we first demonstrate the method's ability to cluster randomly connected graphs. We also use a linear chain architecture corresponding to a 1D free energy profile as our kinetic network. Subsequently, we demonstrate the effectiveness of our method through experiments on a data set derived from molecular dynamics. We compare the performance of our method to other partitioning techniques, such as PCCA+. We explore the importance of feature and hyperparameter selection and propose a general strategy for large-scale parallel training of GNNs for discovering optimal graph partitionings.

Dataset Information

Optimization of metamaterials and metamaterial-microcavity based on deep neural networks.

Publications

Optimization of metamaterials and metamaterial-microcavity based on deep neural networks.

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