Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

Generalizable Metamaterials Design Techniques Inspire Efficient Mycelial Materials Inverse Design

Fungal mycelial materials can mimic numerous nonrenewable materials; they are even capable of outperforming certain materials at their own applications. Fungi's versatility makes mock leather, bricks, wood, foam, meats, and many other products possible. That said, there is currently a critical need to develop efficient mycelial materials design techniques. In mycelial materials, and the wider field of biomaterials, design is primarily limited to costly forward techniques. New mycelial materials could be developed faster and cheaper with robust inverse design techniques, which are not currently used within the field. However, computational inverse design techniques will not be tractable unless clear and concrete design parameters are defined for fungi, derived from genotype and bulk phenotype characteristics. Through mycelial materials case studies and a comprehensive review of metamaterials design techniques, we identify three critical needs that must be addressed to implement computational inverse design in mycelial materials. These critical needs are the following: 1) heuristic search/optimization algorithms, 2) efficient mathematical modeling, and 3) dimensionality reduction techniques. Metamaterials researchers already use many of these computational techniques that can be adapted for mycelial materials inverse design. Then, we suggest mycelium-specific parameters as well as how to measure and use them. Ultimately, based on a review of metamaterials research and the current state of mycelial materials design, we synthesize a generalizable inverse design paradigm that can be applied to mycelial materials or related design fields.

Unidirectional Transmission Metasurfaces with Topological Continuity Generated from High-dimensional Design Space

With the growing demand for nanodevices, there is a concerted effort to improve the design flexibility of nanostructures, thereby expanding the capabilities of nanophotonic devices. In this work, a Laplacian-weighted binary search (LBS) algorithm is proposed to generate a unidirectional transmission metasurface from a high-dimensional design space, offering an increased degree of design freedom. The LBS algorithm incorporates topological continuity based on the Laplacian, effectively circumventing the common issue of high structural complexity in designing high-dimensional nanostructures. As a result, metasurfaces developed using the LBS algorithm in a high-dimensional design space exhibit reduced complexity, which is advantageous for experimental fabrication. An all-dielectric metasurface with unidirectional transmission, designed from the high-dimensional space using the LBS method, demonstrated the successful application of these design principles in experiments. The metasurface exhibits high optical performance on unidirectional transmission in measurements by a high-resolution angle-resolved micro-spectra system, achieving forward transmissivity above 90% (400-700 nm) and back transmissivity below 20% (400-500 nm) within the targeted wavelength range. This work provides a feasible approach for advancing high-dimensional metasurface applications, as the LBS design method takes into account topological continuity during experimental processing. Compared to traditional direct binary search (DBS) methods, the LBS method not only improves information processing efficiency but also maintains the topological continuity of structures. Beyond unidirectional transmission, the LBS-based design method has generality and flexibility to accommodate almost all physical scenarios in metasurface design, enabling a multitude of complex functions and applications.

A guidance to intelligent metamaterials and metamaterials intelligence

The bidirectional interactions between metamaterials and artificial intelligence have recently attracted immense interest to motivate scientists to revisit respective communities, giving rise to the proliferation of intelligent metamaterials and metamaterials intelligence. Owning to the strong nonlinear fitting and generalization ability, artificial intelligence is poised to serve as a materials-savvy surrogate electromagnetic simulator and a high-speed computing nucleus that drives numerous self-driving metamaterial applications, such as invisibility cloak, imaging, detection, and wireless communication. In turn, metamaterials create a versatile electromagnetic manipulator for wave-based analogue computing to be complementary with conventional electronic computing. In this Review, we stand from a unified perspective to review the recent advancements in these two nascent fields. For intelligent metamaterials, we discuss how artificial intelligence, exemplified by deep learning, streamline the photonic design, foster independent working manner, and unearth latent physics. For metamaterials intelligence, we particularly unfold three canonical categories, i.e., wave-based neural network, mathematical operation, and logic operation, all of which directly execute computation, detection, and inference task in physical space. Finally, future challenges and perspectives are pinpointed, including data curation, knowledge migration, and imminent practice-oriented issues, with a great vision of ushering in the free management of entire electromagnetic space.

Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon film. By embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon (a-Si) layer, we achieve a significant enhancement of light absorption. This enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes, requiring precise tuning of plasmon resonances to match the absorption region of the silicon active layer. To facilitate the device design and improve light absorption without increasing the thickness of the active layer, we develop a deep learning framework, which learns to map from the absorption spectra to the design space. This inverse design strategy helps to tune the absorption for selective spectral functionalities. Our optimized design surpasses the bare silicon planar device, exhibiting a remarkable enhancement of over 100%. Experimental validation confirms the broadband enhancement of light absorption in the proposed configuration. The proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells, photodetectors, and optical filters.

Development of deep learning software to improve HPLC and GC predictions using a new crown-ether based mesogenic stationary phase and beyond

The application of AI to analytical and separative sciences is a recent challenge that offers new perspectives in terms of data prediction. In this work, we report an AI-based software, named Chrompredict 1.0, which based on chromatographic data of a novel mesogenic crown ether stationary phase (CESP). Its molecular design represents a significant advancement due to the unique combination of properties and binding capabilities, including the formation of a cavity, mesogenic behavior via mobile chains, and a range of polar and non-polar interactions (aromatic rings, N=N and C=O double bonds, alkyl chains, π-π interactions, and hydrogen bonding). The mesogenic phase is effective in both normal and reversed-phase chromatography, enhancing the software's adaptability across diverse datasets. Here we introduce for the first time an unprecedented scientific approach, integrating deep learning techniques with the novel CESP, which demonstrates exceptional thermal and analytical performance in both liquid chromatography modes, especially in the separation of complex hydrocarbon isomers. This ability enables the results obtained with CESP to extend across various types of stationary phases. Leveraging these insights, a comprehensive chromatographic dataset on a series of aromatic and polyaromatic molecules interacting with our CESP was used to train a Deep Learning Model (DLM). This model is embedded within a user-friendly software, Chrompredict 1.0, designed for predicting chromatographic parameters (MAE = 0.042, R² = 0.95) by selecting chemical descriptors directly from SMILES notation. It offers a deeper understanding of molecular structure and interactions through exploratory data analysis, identifying key factors affecting model accuracy and chromatographic behavior. Users can configure hyperparameters, choose from six machine learning models, and compare their performance with DLM. Chrompredict 1.0 excels in retention behavior prediction for compounds with known structures, and it accurately predicts chromatographic retention and thermal characteristics for different temperatures in HPLC and GC. The model has been successfully tested with METLIN database of 1,023 small molecules of diverse structures and polarities (R² > 0.75, error range ±7.8 s). Overall, the CESP, combined with Chrompredict 1.0, offers a robust tool for intelligent chromatographic analysis, encompassing chemo-informatics, statistical analysis, and graphical capabilities across a broad range of compounds and stationary phases.

Deep-Learning Empowered Customized Chiral Metasurface for Calibration-Free Biosensing

As a 2D metamaterial, metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. The current "one-by-one design" method is hindered by time-consuming, repeated testing within a confined space. However, intelligent design strategies for metasurfaces, limited by data-driven properties, have rarely been explored. To address this gap, a data iterative strategy based on deep learning, coupled with a global optimization network is proposed, to achieve the customized design of chiral metasurfaces. This methodology is applied to precisely identify different chiral molecules in a label-free manner. Fundamentally different from the traditional approach of collecting data purely through simulation, the proposed data generation strategy encompasses the entire design space, which is inaccessible by conventional methods. The dataset quality is significantly improved, with a 21-fold increase in the number of chiral structures exhibiting the desired circular dichroism (CD) response (>0.6). The method's efficacy is validated by a monolayer structure that is easily prepared, demonstrating advanced sensing abilities for enantiomer-specific analysis of bio-samples. These results demonstrate the superior capability of data-driven schemes in photonic design and the potential of chiral metasurface-based platforms for calibration-free biosensing applications. The proposed approach will accelerate the development of complex systems for rapid molecular detection, spectroscopic imaging, and other applications.

Enhancing computational efficiency in topology-optimized mode converters via dynamic update rate strategies

In the big data era, mode division multiplexing, as a technology for extended channel capacity, demonstrates potential in enhancing parallel data processing capability. Consequently, developing a compact, high-performance mode converter through efficient design methods is an urgent requirement. However, traditional design methodologies for these converters face significant computational complexities and inefficiencies. Addressing this challenge, this paper introduces a novel topology optimization design method for mode converters employing a Dynamic Adjustment of Update Rate (DAUR). This approach markedly reduces computational overhead, accelerating the design process while ensuring high performance and compactness. As a proof-of-concept, an ultra-compact dual-mode converter was designed. The DAUR method demonstrated an 80% reduction in computational time compared to traditional methods, while maintaining a compact design (only 1.4 μm × 1.4 μm) and an insertion loss under 0.68 dB across a wavelength range of 1525 nm to 1575 nm. Meanwhile, simulated inter-mode crosstalk remained below - 24 dB across a 40 nm bandwidth. A comprehensive comparison with traditional inverse design algorithms is presented, demonstrating our method's superior efficiency and effectiveness. Our findings suggest that DAUR not only streamlines the design process but also facilitates exploration into more complex micro-nano photonic structures with reduced resource investment.

Integrated optical probing scheme enabled by localized-interference metasurface for chip-scale atomic magnetometer

Emerging miniaturized atomic sensors such as optically pumped magnetometers (OPMs) have attracted widespread interest due to their application in high-spatial-resolution biomagnetism imaging. While optical probing systems in conventional OPMs require bulk optical devices including linear polarizers and lenses for polarization conversion and wavefront shaping, which are challenging for chip-scale integration. In this study, an integrated optical probing scheme based on localized-interference metasurface for chip-scale OPM is developed. Our monolithic metasurface allows tailorable linear polarization conversion and wavefront manipulation. Two silicon-based metasurfaces namely meta-polarizer and meta-polarizer-lens are fabricated and characterized, with maximum transmission efficiency and extinction ratio (ER) of 86.29 % and 14.2 dB for the meta-polarizer as well as focusing efficiency and ER of 72.79 % and 6.4 dB for the meta-polarizer-lens, respectively. A miniaturized vapor cell with 4 × 4 × 4 mm3 dimension containing 87Rb and N2 is combined with the meta-polarizer to construct a compact zero-field resonance OPM for proof of concept. The sensitivity of this sensor reaches approximately 9 fT/Hz1/2 with a dynamic range near zero magnetic field of about ±2.3 nT. This study provides a promising solution for chip-scale optical probing, which holds potential for the development of chip-integrated OPMs as well as other advanced atomic devices where the integration of optical probing system is expected.

Meta-Attention Deep Learning for Smart Development of Metasurface Sensors

Optical metasurfaces with pronounced spectral characteristics are promising for sensor applications. Currently, deep learning (DL) offers a rapid manner to design various metasurfaces. However, conventional DL models are usually assumed as black boxes, which is difficult to explain how a DL model learns physical features, and they usually predict optical responses of metasurfaces in a fuzzy way. This makes them incapable of capturing critical spectral features precisely, such as high quality (Q) resonances, and hinders their use in designing metasurface sensors. Here, a transformer-based explainable DL model named Metaformer for the high-intelligence design, which adopts a spectrum-splitting scheme to elevate 99% prediction accuracy through reducing 99% training parameters, is established. Based on the Metaformer, all-dielectric metasurfaces based on quasi-bound states in the continuum (Q-BIC) for high-performance metasensing are designed, and fabrication experiments are guided potently. The explainable learning relies on spectral position encoding and multi-head attention of meta-optics features, which overwhelms traditional black-box models dramatically. The meta-attention mechanism provides deep physics insights on metasurface sensors, and will inspire more powerful DL design applications on other optical devices.

Spatial localisation and sensing in two dimensions via metasurfaces

In this study, we introduce a two-dimensional metasurface sensor designed to detect, locate and distinguish between different objects placed in its near field. When an object is placed on the metasurface, local changes can be detected in one or more of the structure's meta-atoms. This interaction generally modifies the inductance of the meta-atom, resulting in changes to the overall input impedance of the surface. We derive the properties of the structure and its behaviour in terms of superposition and demonstrate that observing the meta-surface from a single point is sufficient for unambiguous localisation and identification. To model these changes effectively and identify the position of an object, we employ a neural network machine learning algorithm. Our approach enables accurate localisation of all studied objects, with a precision exceeding 98 % . Additionally, the distinct signatures of the objects allow for separation between them with an accuracy of over 97 % . The potential applications of this platform extend to foreign object detection on metasurfaces for wireless power transfer, providing proximity detection for many surfaces such as clothing, car bodies and robotic carapaces. Furthermore, our research suggests the feasibility of implementing a touchscreen type interface requiring only a single waveguide connection.

Thermal Metamaterials with Configurable Mechanical Properties

Thermal metamaterials are typically achieved by mixing different natural materials to realize effective thermal conductivities (ETCs) that conventional materials do not possess. However, the necessity for multifunctional design of metamaterials, encompassing both thermal and mechanical functionalities, is somewhat overlooked, resulting in the fixation of mechanical properties in thermal metamaterials designed within current research endeavors. Thus far, conventional methods have faced challenges in designing thermal metamaterials with configurable mechanical properties because of intricate inherent relationships among the structural configuration, thermal and mechanical properties in metamaterials. Here, a data-driven approach is proposed to design a thermal metamaterial capable of seamlessly achieving thermal functionalities and harnessing the advantages of microstructural diversity to configure its mechanical properties. The designed metamaterial possesses thermal cloaking functionality while exhibiting exceptional mechanical properties, such as load-bearing capacity, shearing strength, and tensile resistance, thereby affording mechanical protection for the thermal metadevice. The proposed approach can generate numerous distinct inverse design candidate topological functional cells (TFCs), designing thermal metamaterials with dramatic improvements in mechanical properties compared to traditional ones, which sets up a novel paradigm for discovering thermal metamaterials with extraordinary mechanical structures. Furthermore, this approach also paves the way for investigating thermal metamaterials with additional physical properties.

NIR-II Surface-Enhanced Raman Scattering Nanoprobes in Biomedicine: Current Impact and Future Directions

The field of second near-infrared (NIR-II) surface-enhanced Raman scattering (SERS) nanoprobes has made commendable progress in biomedicine. This article reviews recent advances and future development of NIR-II SERS nanoprobes. It introduces the fundamental principles of SERS nanoprobes and highlights key advances in the NIR-II window, including reduced tissue attenuation, deep penetration, maximized allowable exposure, and improved photostability. The discussion of future directions includes the refinement of nanoprobe substrates, emphasizing the tailoring of optical properties of metallic SERS-active nanoprobes, and exploring non-metallic alternatives. The intricacies of designing Raman reporters for the NIR-II resonance and the potential of these reporters to advance the field are also discussed. The integration of artificial intelligence (AI) into nanoprobe design represents a cutting-edge approach to overcome current challenges. This article also examines the emergence of deep Raman techniques for through-tissue SERS detection, toward NIR-II SERS tomography. It acknowledges instrumental advancements like improved charge-coupled device sensitivity and accelerated imaging speeds. The article concludes by addressing the critical aspects of biosafety, ease of functionalization, compatibility, and the path to clinical translation. With a comprehensive overview of current achievements and future prospects, this review aims to illuminate the path for NIR-II SERS nanoprobes to innovate diagnostic and therapeutic approaches in biomedicine.

Negative Conductivity Induced Reconfigurable Gain Metasurfaces and Their Nonlinearity

The past decades have witnessed the rapid development of metamaterials and metasurfaces. However, loss is still a challenging problem limiting numerous practical applications, including long-range wireless communications, superscattering, and non-Hermitian physics. Recently, great effort has been made to minimize the loss, however, they are too complicated for practical implementation and still restricted by the theoretical limit. Here, we propose and experimentally realize a tunable gain metasurface induced by negative conductivity, with deep theoretical analysis from scattering theory and equivalent circuits. In the experiment, we create metasurface samples embedded with tunable negative (or positive) conductivity to achieve adjustable gain (or loss). By varying the control bias voltages, the metasurfaces can reflect incident waves with additional controllable gain. Interestingly, we find the gain metasurfaces inherently pose nonlinearities, which are beneficial for nonlinear optics and microwave applications, particularly for the nonlinear activation of wave-based neural networks.

Deep learning-driven forward and inverse design of nanophotonic nanohole arrays: streamlining design for tailored optical functionalities and enhancing accessibility

In nanophotonics, nanohole arrays (NHAs) are periodic arrangements of nanoscale apertures in thin films that provide diverse optical functionalities essential for various applications. Fully studying NHAs' optical properties and optimizing performance demands understanding both materials and geometric parameters, which presents a computational challenge due to numerous potential combinations. Efficient computational modeling is critical for overcoming this challenge and optimizing NHA-based device performance. Traditional approaches rely on time-consuming numerical simulation processes for device design and optimization. However, using a deep learning approach offers an efficient solution for NHAs design. In this work, a deep neural network within the forward modeling framework accurately predicts the optical properties of NHAs by using device structure data such as periodicity and hole radius as model inputs. We also compare three deep learning-based inverse modeling approaches-fully connected neural network, convolutional neural network, and tandem neural network-to provide approximate solutions for NHA structures based on their optical responses. Once trained, the DNN accurately predicts the desired result in milliseconds, enabling repeated use without wasting computational resources. The models are trained using over 6000 samples from a dataset obtained by finite-difference time-domain (FDTD) simulations. The forward model accurately predicts transmission spectra, while the inverse model reliably infers material attributes, lattice geometries, and structural parameters from the spectra. The forward model accurately predicts transmission spectra, with an average Mean Squared Error (MSE) of 2.44 × 10-4. In most cases, the inverse design demonstrates high accuracy with deviations of less than 1.5 nm for critical geometrical parameters. For experimental verification, gold nanohole arrays are fabricated using deep UV lithography. Validation against experimental data demonstrates the models' robustness and precision. These findings show that the trained DNN models offer accurate predictions about the optical behavior of NHAs.

Enhanced DBR mirror design via D3QN: A reinforcement learning approach

Modern optical systems are important components of contemporary electronics and communication technologies, and the design of new systems has led to many innovative breakthroughs. This paper introduces a novel application based on deep reinforcement learning, D3QN, which is a combination of the Dueling Architecture and Double Q-Network methods, to design distributed Bragg reflectors (DBRs). Traditional design methods are based on time-consuming iterative simulations, whereas D3QN is designed to optimize the multilayer structure of DBRs. This approach enabled the reflectance performance and compactness of the DBRs to be improved. The reflectance of the DBRs designed using D3QN is 20.5% higher compared to designs derived from the transfer matrix method (TMM), and these DBRs are 61.2% smaller in terms of their size. These advancements suggest that deep reinforcement learning, specifically the D3QN methodology, is a promising new method for optical design and is more efficient than traditional techniques. Future research possibilities include expansion to 2D and 3D design structures, where increased design complexities could likely be addressed using D3QN or similar innovative solutions.

Biomimetic Mechanical Robust Cement-Resin Composites with Machine Learning-Assisted Gradient Hierarchical Structures

Biological materials relying on hierarchically ordered architectures inspire the emergence of advanced composites with mutually exclusive mechanical properties, but the efficient topology optimization and large-scale manufacturing remain challenging. Herein, this work proposes a scalable bottom-up approach to fabricate a novel nacre-like cement-resin composite with gradient brick-and-mortar (BM) structure, and demonstrates a machine learning-assisted method to optimize the gradient structure. The fabricated gradient composite exhibits an extraordinary combination of high flexural strength, toughness, and impact resistance. Particularly, the toughness and impact resistance of such composite attractively surpass the cement counterparts by factors of approximately 700 and 600 times, and even outperform natural rocks, fiber-reinforced cement-based materials and even some alloys. The strengthening and toughening mechanisms are clarified as the regional-matrix densifying and crack-tip shielding effects caused by the gradient BM structure. The developed gradient composite not only endows a promising structural material for protective applications in harsh scenarios, but also paves a new way for biomimetic metamaterials designing.

Specific wavelength peak emulation with amorphous metastructures

The conventional design process for metasurfaces is time-consuming and computationally expensive. To address this challenge, we utilize a deep convolutional generative adversarial network (DCGAN) to generate new nanohole metastructure designs that match a desired transmittance spectrum in the visible range. The trained DCGAN model demonstrates an exceptional performance in generating diverse and manufacturable metastructure designs that closely resemble the target optical properties. The proposed method provides several advantages over existing approaches. These include its capability to generate new designs without prior knowledge or assumptions regarding the relationship between metastructure geometries and optical properties, its high efficiency, and its generalizability to other types of metamaterials. The successful fabrication and experimental characterization of the predicted metastructures further validate the accuracy and effectiveness of our proposed method.

Recent advances in the metamaterial and metasurface-based biosensor in the gigahertz, terahertz, and optical frequency domains

Recently, metamaterials and metasurface have gained rapidly increasing attention from researchers due to their extraordinary optical and electrical properties. Metamaterials are described as artificially defined periodic structures exhibiting negative permittivity and permeability simultaneously. Whereas metasurfaces are the 2D analogue of metamaterials in the sense that they have a small but not insignificant depth. Because of their high optical confinement and adjustable optical resonances, these artificially engineered materials appear as a viable photonic platform for biosensing applications. This review paper discusses the recent development of metamaterial and metasurface in biosensing applications based on the gigahertz, terahertz, and optical frequency domains encompassing the whole electromagnetic spectrum. Overlapping features such as material selection, structure, and physical mechanisms were considered during the classification of our biosensing applications. Metamaterials and metasurfaces working in the GHz range provide prospects for better sensing of biological samples, THz frequencies, falling between GHz and optical frequencies, provide unique characteristics for biosensing permitting the exact characterization of molecular vibrations, with an emphasis on molecular identification, label-free analysis, and imaging of biological materials. Optical frequencies on the other hand cover the visible and near-infrared regions, allowing fine regulation of light-matter interactions enabling metamaterials and metasurfaces to offer excellent sensitivity and specificity in biosensing. The outcome of the sensor's sensitivity to an electric or magnetic field and the resonance frequency are, in theory, determined by the frequency domain and features. Finally, the challenges and possible future perspectives in biosensing application areas have been presented that use metamaterials and metasurfaces across diverse frequency domains to improve sensitivity, specificity, and selectivity in biosensing applications.

Bimodal Absorber Frequencies Shift Induced by the Coupling of Bright and Dark Modes

In this paper, we demonstrate that the absorption frequencies of the bimodal absorber shift with the coupling strength of the bright and dark modes. The coupling between the bright mode and the dark mode can acquire electromagnetically induced transparency, we obtain the analytical relationship between the absorbing frequencies, the resonant frequencies, losses of the bright mode and dark mode, and the coupling strength between two modes by combining the coupled mode theory with the interference theory. As the coupling strength between the bright mode and the dark mode decreases, the two absorption peaks gradually move closer to each other, inversely, they will move away from each other. The simulation employs three distinct metasurface structures with coupling of the bright and dark modes, thereby verifying the generality of the theoretical findings.

Adaptive meshing strategies for nanophotonics using a posteriori error estimation

As nanophotonic devices become increasingly complex, computer simulations of such devices are becoming ever more important. Unfortunately, computer simulations of nanophotonic devices are computationally expensive, especially if many simulations are necessary, e.g., when optimizing or inverse designing a device. Here we study adaptive mesh refinement for finite-element method simulations using an a posteriori error estimation method. We demonstrate that the use of adaptive meshing leads to faster convergence with lower memory footprint for complex three-dimensional nanophotonic structures. Nevertheless, one needs to be careful to avoid a mesh propagation effect for adaptive mesh refinement to be a successful strategy.

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one-to-many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root-mean-squared-error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser-matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

AI-Assisted Insole Sensing System for Multifunctional Plantar-Healthcare Applications

The pervasive global issue of population aging has led to a growing demand for health monitoring, while the advent of electronic wearable devices has greatly alleviated the strain on the industry. However, these devices come with inherent limitations, such as electromagnetic radiation, complex structures, and high prices. Herein, a Solaris silicone rubber-integrated PMMA polymer optical fiber (S-POF) intelligent insole sensing system has been developed for remote, portable, cost-effective, and real-time gait monitoring. The system is capable of sensitively converting the pressure of key points on the sole into changes in light intensity with correlation coefficients of 0.995, 0.952, and 0.910. The S-POF sensing structure demonstrates excellent durability with a 4.8% variation in output after 10,000 cycles and provides stable feedback for bending angles. It also exhibits water resistance and temperature resistance within a certain range. Its multichannel multiplexing framework allows a smartphone to monitor multiple S-POF channels simultaneously, meeting the requirements of convenience for daily care. Also, the system can efficiently and accurately provide parameters such as pressure, step cadence, and pressure distribution, enabling the analysis of gait phases and patterns with errors of only 4.16% and 6.25% for the stance phase (STP) and the swing phase (SWP), respectively. Likewise, after comparing various AI models, an S-POF channel-based gait pattern recognition technique has been proposed with a high accuracy of up to 96.87%. Such experimental results demonstrate that the system is promising to further promote the development of rehabilitation and healthcare.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Transfer learning for metamaterial design and simulation

We demonstrate transfer learning as a tool to improve the efficacy of training deep learning models based on residual neural networks (ResNets). Specifically, we examine its use for study of multi-scale electrically large metasurface arrays under open boundary conditions in electromagnetic metamaterials. Our aim is to assess the efficiency of transfer learning across a range of problem domains that vary in their resemblance to the original base problem for which the ResNet model was initially trained. We use a quasi-analytical discrete dipole approximation (DDA) method to simulate electrically large metasurface arrays to obtain ground truth data for training and testing of our deep neural network. Our approach can save significant time for examining novel metasurface designs by harnessing the power of transfer learning, as it effectively mitigates the pervasive data bottleneck issue commonly encountered in deep learning. We demonstrate that for the best case when the transfer task is sufficiently similar to the target task, a new task can be effectively trained using only a few data points yet still achieve a test mean absolute relative error of 3 % with a pre-trained neural network, realizing data reduction by a factor of 1000.

Anisotropy Reversal of Thermal Conductivity in Silicon Nanowire Networks Driven by Quasi-Ballistic Phonon Transport

Nanostructured semiconductors promise functional thermal management for microelectronics and thermoelectrics through a rich design capability. However, experimental studies on anisotropic in-plane thermal conduction remain limited, despite the demand for directional heat dissipation. Here, inspired by an oriental wave pattern, a periodic network of bent wires, we investigate anisotropic in-plane thermal conduction in nanoscale silicon phononic crystals with the thermally dead volume. We observed the anisotropy reversal of the material thermal conductivity from 1.2 at 300 K to 0.8 at 4 K, with the reversal temperature of 80 K mediated by the transition from a diffusive to a quasi-ballistic regime. Our Monte Carlo simulations revealed that the backflow of the directional phonons induces the anisotropy reversal, showing that the quasi-ballistic phonon transport introduces preferential thermal conduction channels with anomalous temperature dependence. Accordingly, the anisotropy of the effective thermal conductivity varied from 2.7 to 5.0 in the range of 4-300 K, indicating an anisotropic heat manipulation capability. Our findings demonstrate that the design of nanowire networks enables the directional thermal management of electronic devices.

Machine Learning Aided Design and Optimization of Thermal Metamaterials

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.

Flexible design of chiroptical response of planar chiral metamaterials using deep learning

Optical chirality is highly demanded for biochemical sensing, spectral detection, and advanced imaging, however, conventional design schemes for chiral metamaterials require highly computational cost due to the trial-and-error strategy, and it is crucial to accelerate the design process particularly in comparably simple planar chiral metamaterials. Herein, we construct a bidirectional deep learning (BDL) network consists of spectra predicting network (SPN) and design predicting network (DPN) to accelerate the prediction of spectra and inverse design of chiroptical response of planar chiral metamaterials. It is shown that the proposed BDL network can accelerate the design process and exhibit high prediction accuracy. The average process of prediction only takes ∼15 ms, which is 1 in 40000 compared to finite-difference time-domain (FDTD). The mean-square error (MSE) loss of forward and inverse prediction reaches 0.0085 after 100 epochs. Over 95.2% of training samples have MSE ≤ 0.0042 and MSE ≤ 0.0044 for SPN and DPN, respectively; indicating that the BDL network is robust in the inverse deign without underfitting or overfitting for both SPN and DPN. Our founding shows great potentials in accelerating the on-demand design of planar chiral metamaterials.

Optimizing PCF-SPR sensor design through Taguchi approach, machine learning, and genetic algorithms

Designing Photonic Crystal Fibers incorporating the Surface Plasmon Resonance Phenomenon (PCF-SPR) has led to numerous interesting applications. This investigation presents an exceptionally responsive surface plasmon resonance sensor, seamlessly integrated into a dual-core photonic crystal fiber, specifically designed for low refractive index (RI) detection. The integration of a plasmonic material, namely silver (Ag), externally deposited on the fiber structure, facilitates real-time monitoring of variations in the refractive index of the surrounding medium. To ensure long-term functionality and prevent oxidation, a thin layer of titanium dioxide (TiO2) covers the silver coating. To optimize the sensor, five key design parameters, including pitch, air hole diameter, and silver thickness, are fine-tuned using the Taguchi L8(25) orthogonal array. The optimal results obtained present spectral and amplitude sensitivities that reach remarkable values of 10,000 nm/RIU and 235,882 RIU-1, respectively. In addition, Artificial Neural Network (ANN) optimization techniques, specifically Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO), are used to predict a critical optical property of the sensor confinement loss (αloss). These predictions are derived from the same input structure parameters that are present in the full L32(25) design experiment. A genetic algorithm (GA) is then applied for optimization with the goal of maximizing the confinement loss. Our results highlight the effectiveness of training PSO artificial neural networks and demonstrate their ability to quickly and accurately predict results for unknown geometric dimensions, demonstrating their significant potential in this innovative context. The proposed sensor design can be used for various applications including pharmaceutical inspection and detection of low refractive index analytes.

On-demand Doppler-offset beamforming with intelligent spatiotemporal metasurfaces

Recently, significant efforts have been devoted to guaranteeing high-quality communication services in fast-moving scenes, such as high-speed trains. The challenges lie in the Doppler effect that shifts the frequency of the transmitted signal. To this end, the recent emergence of spatiotemporal metasurfaces offers a promising solution, which can manipulate electromagnetic waves in time and space domain while being lightweight and cost-effective. Here we introduce deep learning-assisted spatiotemporal metasurfaces to automatically and adaptively neutralize Doppler effect in fast-moving situations. A tandem neural network is used to establish a rapid connection between on-site targets and time-varying series of spatiotemporal metasurfaces, endowing the capability of on-demand beamforming with Doppler effects offset. Moreover, oblique incidence problems are also studied in practice, which can be used for relieving multipath effect. In the microwave experiment, we fabricate the intelligent spatiotemporal metasurfaces and demonstrate the potential to fulfill Doppler-offset beamforming under oblique incidence.

Inverse design in photonic crystals

Photonic crystals are periodic dielectric structures that possess a wealth of physical characteristics. Owing to the unique way they interact with the light, they provide new degrees of freedom to precisely modulate the electromagnetic fields, and have received extensive research in both academia and industry. At the same time, fueled by the advances in computer science, inverse design strategies are gradually being used to efficiently produce on-demand devices in various domains. As a result, the interdisciplinary area combining photonic crystals and inverse design emerges and flourishes. Here, we review the recent progress for the application of inverse design in photonic crystals. We start with a brief introduction of the background, then mainly discuss the optimizations of various physical properties of photonic crystals, from eigenproperties to response-based properties, and end up with an outlook for the future directions. Throughout the paper, we emphasize some insightful works and their design algorithms, and aim to give a guidance for readers in this emerging field.

Unleashing the potential: AI empowered advanced metasurface research

In recent years, metasurface, as a representative of micro- and nano-optics, have demonstrated a powerful ability to manipulate light, which can modulate a variety of physical parameters, such as wavelength, phase, and amplitude, to achieve various functions and substantially improve the performance of conventional optical components and systems. Artificial Intelligence (AI) is an emerging strong and effective computational tool that has been rapidly integrated into the study of physical sciences over the decades and has played an important role in the study of metasurface. This review starts with a brief introduction to the basics and then describes cases where AI and metasurface research have converged: from AI-assisted design of metasurface elements up to advanced optical systems based on metasurface. We demonstrate the advanced computational power of AI, as well as its ability to extract and analyze a wide range of optical information, and analyze the limitations of the available research resources. Finally conclude by presenting the challenges posed by the convergence of disciplines.

Optimized wideband and compact multifunctional photonic device based on Sb2S3 phase change material

In this paper, a 1 × 2 photonic switch is designed based on a silicon-on-insulator (SOI) platform combined with the phase change material (PCM), Sb2S3, assisted by the direct binary search (DBS) algorithm. The designed photonic switch exhibits an impressive operating bandwidth ranging from 1450 to 1650 nm. The device has an insertion loss (IL) from 0.44 dB to 0.70 dB (of less than 0.7 dB) and cross talk (CT) from -26 dB to -20 dB (of less than -20 dB) over an operating bandwidth of 200 nm, especially an IL of 0.52 dB and CT of -24 dB at 1550 nm. Notably, the device is highly compact, with footprints of merely 3 × 4 µm2. Furthermore, we have extended the device's functionality for multifunctional operation in the C-band that can serve as both a 1 × 2 photonic switch and a 3 dB photonic power splitter. In the photonic switch mode, the device demonstrates an IL of 0.7 dB and a CT of -13.5 dB. In addition, when operating as a 3 dB photonic power splitter, the IL is less than 0.5 dB.

Chiral nanomaterials in tissue engineering

Addressing significant medical challenges arising from tissue damage and organ failure, the field of tissue engineering has evolved to provide revolutionary approaches for regenerating functional tissues and organs. This involves employing various techniques, including the development and application of novel nanomaterials. Among them, chiral nanomaterials comprising non-superimposable nanostructures with their mirror images have recently emerged as innovative biomaterial candidates to guide tissue regeneration due to their unique characteristics. Chiral nanomaterials including chiral fibre supramolecular hydrogels, polymer-based chiral materials, self-assembling peptides, chiral-patterned surfaces, and the recently developed intrinsically chiroptical nanoparticles have demonstrated remarkable ability to regulate biological processes through routes such as enantioselective catalysis and enhanced antibacterial activity. Despite several recent reviews on chiral nanomaterials, limited attention has been given to the specific potential of these materials in facilitating tissue regeneration processes. Thus, this timely review aims to fill this gap by exploring the fundamental characteristics of chiral nanomaterials, including their chiroptical activities and analytical techniques. Also, the recent advancements in incorporating these materials in tissue engineering applications are highlighted. The review concludes by critically discussing the outlook of utilizing chiral nanomaterials in guiding future strategies for tissue engineering design.

Super resolution label-free dark-field microscopy by deep learning

Dark-field microscopy (DFM) is a powerful label-free and high-contrast imaging technique due to its ability to reveal features of transparent specimens with inhomogeneities. However, owing to the Abbe's diffraction limit, fine structures at sub-wavelength scale are difficult to resolve. In this work, we report a single image super resolution DFM scheme using a convolutional neural network (CNN). A U-net based CNN is trained with a dataset which is numerically simulated based on the forward physical model of the DFM. The forward physical model described by the parameters of the imaging setup connects the object ground truths and dark field images. With the trained network, we demonstrate super resolution dark field imaging of various test samples with twice resolution improvement. Our technique illustrates a promising deep learning approach to double the resolution of DFM without any hardware modification.

Pure optical twist with zero net torque

In photonic systems, bilayer or multilayer systems exhibit numerous exciting phenomena induced by twisting. Thus, it is highly desired to explore the twisting effect by engineering the light-matter interactions. Optical torque, an important means in optical micromanipulation, can rotate micro-objects in various ways, enabling a wide range of promising applications. In this study, we present an interesting phenomenon called "pure optical twist" (POT), which emerges when a bilayer structure with specific symmetry is illuminated by counter-propagating lights with opposite spin and/or orbital angular momentum. Remarkably, this leads to zero net optical torque but yet possesses an interesting mechanical effect of bilayer system twisting. The crucial determinant of this phenomenon is the rotational symmetries of each layer, which govern the allowed azimuthal channels of the scattered wave. When the rotational symmetries do not allow these channels to overlap, no resultant torque is observed. Our work will encourage further exploration of the twisting effect through engineered light-matter interactions. This opens up the possibility of creating twisted bilayer systems using optical means, and constructing a stable bilayer optical motor that maintains identical rotation frequencies for both layers.

Chiral plasmonic sensing: From the perspective of light-matter interaction

Molecular chirality is represented as broken mirror symmetry in the structural orientation of constituent atoms and plays a pivotal role at every scale of nature. Since the discovery of the chiroptic property of chiral molecules, the characterization of molecular chirality is important in the fields of biology, physics, and chemistry. Over the centuries, the field of optical chiral sensing was based on chiral light-matter interactions between chiral molecules and polarized light. Starting from simple optics-based sensing, the utilization of plasmonic materials that could control local chiral light-matter interactions by squeezing light into molecules successfully facilitated chiral sensing into noninvasive, ultrasensitive, and accurate detection. In this Review, the importance of plasmonic materials and their engineering in chiral sensing are discussed based on the principle of chiral light-matter interactions and the theory of optical chirality and chiral perturbation; thus, this Review can serve as a milestone for the proper design and utilization of plasmonic nanostructures for improved chiral sensing.

Data-Driven Design for Metamaterials and Multiscale Systems: A Review

Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. This review provides a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. Existing research is organized into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. The approaches are further categorized within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Neuromorphic computing, which mimics the structure and principles of the human brain, has the potential to facilitate the hardware implementation of next-generation artificial intelligence systems and process large amounts of data with very low power consumption. Among them, the XNOR synapse-based Binary Neural Network (BNN) has been attracting attention due to its compact neural network parameter size and low hardware cost. The previous XNOR synapse has drawbacks, such as a trade-off between cell density and accuracy. In this work, we show nonvolatile XNOR synapses with high density and accuracy using a monolithically stacked complementary ferroelectric field-effect transistor (C-FeFET) composed of a p-type Si MFMIS-FeFET at the bottom and a 3D stackable n-type Al:IZTO MFS-FeTFT, achieving 60F2 per cell (2C-FeFET). For adjusting the threshold voltage and improving the switching speed (100 ns) of n-type ferroelectric TFT, we employed a dual-gate configuration and a unique operation scheme, making it comparable to those of Si-based FeFETs. We performed array-level simulation with a 512 × 512 subarray size and a 3-bit flash ADC, demonstrating that the image recognition accuracies using the MNIST and CIFAR-10 data sets were increased by 3.17 and 14.07%, respectively, in comparison to other nonvolatile XNOR synapses. In addition, we performed system-level analysis on a 512 × 512 XNOR C-FeFET, exhibiting an outstanding throughput of 717.37 GOPS and an energy efficiency of 196.7 TOPS/W. We expect that our approach would contribute to the high-density memory systems, logic-in-memory technology, and hardware implementation of neural networks.

Real-data-driven real-time reconfigurable microwave reflective surface

Manipulating the electromagnetic (EM) scattering behavior from an arbitrary surface dynamically on arbitrary design goals is an ultimate ambition for many EM stealth and communication problems, yet it is nearly impossible to accomplish with conventional analysis and optimization techniques. Here we present a reconfigurable conformal metasurface prototype as well as a workflow that enables it to respond to multiple design targets on the reflection pattern with extremely low on-site computing power and time. The metasurface is driven by a sequential tandem neural network which is pre-trained using actual experimental data, avoiding any possible errors that may arise from calculation, simulation, or manufacturing tolerances. This platform empowers the surface to operate accurately in a complex environment including varying incident angle and operating frequency, or even with other scatterers present close to the surface. The proposed data-driven approach requires minimum amount of prior knowledge and human effort yet provides maximized versatility on the reflection control, stepping towards the end form of intelligent tunable EM surfaces.

Metamaterial absorber optimization method based on an artificial neural network surrogate

Finding the optimal design parameters for the target EM response of a metamaterial absorber is still a challenging task even if the layout of the absorber has been determined. To effectively address this issue, we introduce the idea of surrogate-based optimization into the area of metamaterial absorber design. This paper proposes a surrogate based optimization method combining artificial neural network (ANN) and trust region algorithm for metamaterial absorbers. Each optimization iteration utilizes the optimal solution from the previous iteration and the sample points surrounding it as the training dataset to build an effective ANN surrogate model. To improve the convergence of the optimization method for metamaterial absorbers based on ANN surrogate model, we incorporate a trust region algorithm. The proposed method employs a simple forward neural network architecture and requires less training data, leading to a quick convergence towards the target solution after only a few iterations. Compared to the three commonly used alternative methods, the proposed method can optimize geometric and material parameters more efficiently in the same time. The validity of the proposed method is demonstrated by two examples of electromagnetic optimizations of metamaterial absorbers.

Utilizing Mixed Training and Multi-Head Attention to Address Data Shift in AI-Based Electromagnetic Solvers for Nano-Structured Metamaterials

When designing nano-structured metamaterials with an iterative optimization method, a fast deep learning solver is desirable to replace a time-consuming numerical solver, and the related issue of data shift is a subtle yet easily overlooked challenge. In this work, we explore the data shift challenge in an AI-based electromagnetic solver and present innovative solutions. Using a one-dimensional grating coupler as a case study, we demonstrate the presence of data shift through the probability density method and principal component analysis, and show the degradation of neural network performance through experiments dealing with data affected by data shift. We propose three effective strategies to mitigate the effects of data shift: mixed training, adding multi-head attention, and a comprehensive approach that combines both. The experimental results validate the efficacy of these approaches in addressing data shift. Specifically, the combination of mixed training and multi-head attention significantly reduces the mean absolute error, by approximately 36%, when applied to data affected by data shift. Our work provides crucial insights and guidance for AI-based electromagnetic solvers in the optimal design of nano-structured metamaterials.

Chiral Metasurface Multifocal Lens in the Terahertz Band Based on Deep Learning

Chiral metasurfaces have garnered significant interest as an emerging field of metamaterials, primarily due to their exceptional capability to manipulate phase distributions at interfaces. However, the on-demand design of chiral metasurface structures remains a challenging task. To address this challenge, this paper introduces a deep learning-based network model for rapid calculation of chiral metasurface structure parameters. The network achieves a mean absolute error (MAE) of 0.025 and enables the design of chiral metasurface structures with a circular dichroism (CD) of 0.41 at a frequency of 1.169 THz. By changing the phase of the chiral metasurface, it is possible to produce not only a monofocal lens but also a multifocal lens. Well-designed chiral metasurface lenses allow us to control the number and position of focal points of the light field. This chiral metasurface, designed using deep learning, demonstrates great multifocal focus characteristics and holds great potential for a wide range of applications in sensing and holography.

Menezes L, Maier SA. Two-Dimensional Chiral Metasurfaces Obtained by Geometrically Simple Meta-atom Rotations

Two-dimensional chiral metasurfaces seem to contradict Lord Kelvin's geometric definition of chirality since they can be made to coincide by performing rotational operations. Nevertheless, most planar chiral metasurface designs often use complex meta-atom shapes to create flat versions of three-dimensional helices, although the visual appearance does not improve their chiroptical response but complicates their optimization and fabrication due to the resulting large parameter space. Here we present one of the geometrically simplest two-dimensional chiral metasurface platforms consisting of achiral dielectric rods arranged in a square lattice. Chirality is created by rotating the individual meta-atoms, making their arrangement chiral and leading to chiroptical responses that are stronger or comparable to more complex designs. We show that resonances depending on the arrangement are robust against geometric variations and behave similarly in experiments and simulations. Finally, we explain the origin of chirality and behavior of our platform by simple considerations of the geometric asymmetry and gap size.

Inverse design of chiral functional films by a robotic AI-guided system

Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out the entire process, including chiral film construction, characterization, and testing. A machine learned reverse design model using spectrum embedded descriptors is developed to predict optimal structure/process parameters for any targeted chiroptical property. A series of chiral films with a dissymmetry factor as high as 1.9 (gabs ~ 1.9) are identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable circularly polarized (CP) luminescence is demonstrated. Our findings not only provide chiral films with the highest reported chiroptical activity, but also have great fundamental value for the inverse design of chiroptical materials.

Diverse ranking metamaterial inverse design based on contrastive and transfer learning

Metamaterials, thoughtfully designed, have demonstrated remarkable success in the manipulation of electromagnetic waves. More recently, deep learning can advance the performance in the field of metamaterial inverse design. However, existing inverse design methods based on deep learning often overlook potential trade-offs of optimal design and outcome diversity. To address this issue, in this work we introduce contrastive learning to implement a simple but effective global ranking inverse design framework. Viewing inverse design as spectrum-guided ranking of the candidate structures, our method creates a resemblance relationship of the optical response and metamaterials, enabling the prediction of diverse structures of metamaterials based on the global ranking. Furthermore, we have combined transfer learning to enrich our framework, not limited in prediction of single metamaterial representation. Our work can offer inverse design evaluation and diverse outcomes. The proposed method may shrink the gap between flexibility and accuracy of on-demand design.

Generalized binary spiral zone plates with a single focus obtained by feedforward neural network

Traditional spiral zone plates (SZPs) have been widely used to generate optical vortices, but this structure suffers from multiple focuses. To eliminate high-order foci, the current method is to design a binary structure that has a sinusoidal transmittance function along the radial direction. With the rapid development of artificial neural networks, they can provide alternative methods to design novel SZPs with a single focus. In this paper, we first propose the concept of generalized binary spiral zone plates (GBSZPs), and train a feedforward neural network (FNN) to obtain the mapping relationship between the relative intensity of each focus and the structural parameters of GBSZPs. Then the structural parameters of GBSZPs with a single focus were predicted by the trained FNN. It is found by simulations and experiments that the intensities of high-order foci can be as low as 0.2% of the required first order. By analyzing the radial transmittance function, it is found that this structure has a different distribution function from the previous radial sinusoidal function, which reveals that the imperfect radial sinusoidal form also can guide the design of binary zone plates to eliminate high-order foci diffraction. These findings are expected to direct new avenue towards improving the performance of optical image processing and quantum computation.

Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm

Introduction: Triply periodic minimal surface (TPMS) is widely used in the design of bone scaffolds due to its structural advantages. However, the current approach to designing bone scaffolds using TPMS structures is limited to a forward process from microstructure to mechanical properties. Developing an inverse bone scaffold design method based on the mechanical properties of bone structures is crucial. Methods: Using the machine learning and genetic algorithm, a new inverse design model was proposed in this research. The anisotropy of bone was matched by changing the number of cells in different directions. The finite element (FE) method was used to calculate the TPMS configuration and generate a back propagation neural network (BPNN) data set. Neural networks were used to establish the relationship between microstructural parameters and the elastic matrix of bone. This relationship was then used with regenerative genetic algorithm (RGA) in inverse design. Results: The accuracy of the BPNN-RGA model was confirmed by comparing the elasticity matrix of the inverse-designed structure with that of the actual bone. The results indicated that the average error was below 3.00% for three mechanical performance parameters as design targets, and approximately 5.00% for six design targets. Discussion: The present study demonstrated the potential of combining machine learning with traditional optimization method to inversely design anisotropic TPMS bone scaffolds with target mechanical properties. The BPNN-RGA model achieves higher design efficiency, compared to traditional optimization methods. The entire design process is easily controlled.

Design of a High-Performance Titanium Nitride Metastructure-Based Solar Absorber Using Quantum Computing-Assisted Optimization

Metastructures of titanium nitride (TiN), a plasmonic refractory material, can potentially achieve high solar absorptance while operating at elevated temperatures, but the design has been driven by expert intuition. Here, we design a high-performance solar absorber based on TiN metastructures using quantum computing-assisted optimization. The optimization scheme includes machine learning, quantum annealing, and optical simulation in an iterative cycle. It designs an optimal structure with solar absorptance > 95% within 40 h, much faster than an exhaustive search. Analysis of electric field distributions demonstrates that combined effects of Fabry-Perot interferences and surface plasmonic resonances contribute to the broadband high absorption efficiency of the optimally designed metastructure. The designed absorber may exhibit great potential for solar energy harvesting applications, and the optimization scheme can be applied to the design of other complex functional materials.

Chirality in Light-Matter Interaction

The scientific effort to control the interaction between light and matter has grown exponentially in the last 2 decades. This growth has been aided by the development of scientific and technological tools enabling the manipulation of light at deeply sub-wavelength scales, unlocking a large variety of novel phenomena spanning traditionally distant research areas. Here, the role of chirality in light-matter interactions is reviewed by providing a broad overview of its properties, materials, and applications. A perspective on future developments is highlighted, including the growing role of machine learning in designing advanced chiroptical materials to enhance and control light-matter interactions across several scales.

Dual-band optical collimator based on deep-learning designed, fabrication-friendly metasurfaces

Metasurfaces, which consist of arrays of ultrathin planar nanostructures (also known as "meta-atoms"), offer immense potential for use in high-performance optical devices through the precise manipulation of electromagnetic waves with subwavelength spatial resolution. However, designing meta-atom structures that simultaneously meet multiple functional requirements (e.g., for multiband or multiangle operation) is an arduous task that poses a significant design burden. Therefore, it is essential to establish a robust method for producing intricate meta-atom structures as functional devices. To address this issue, we developed a rapid construction method for a multifunctional and fabrication-friendly meta-atom library using deep neural networks coupled with a meta-atom selector that accounts for realistic fabrication constraints. To validate the proposed method, we successfully applied the approach to experimentally demonstrate a dual-band metasurface collimator based on complex free-form meta-atoms. Our results qualify the proposed method as an efficient and reliable solution for designing complex meta-atom structures in high-performance optical device implementations.