Intelligent Nanotechnology. Merging Nanoscience and Artificial Intelligence

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Cover Front Matter Copyright Contributors Preface Acknowledgments Inverse design meets nanophotonics: From computational optimization to artificial neural network Computational inverse design Gradient-based Adjoint optimization Topology optimization Evolutionary algorithms Genetic algorithm Particle swarm optimization Deep learning-based inverse design Deterministic neural network-based inverse design Multilayer perceptron Convolutional neural networks Generative neural network-based inverse design Autoencoders Generative adversarial networks Deep learning merged with computational optimization Generative neural networks combined with topology optimization Generative neural networks combined with metaheuristic optimization References Machine learning for solid mechanics Introduction Case studies Surrogate modeling for materials Case study: Prediction of material properties of binary composites Examples of surrogate models Inverse material design Physics-informed neural network Graph neural networks Future opportunities and considerations Conclusions References Neural networks in phononics Introduction One-dimensional phononic crystals and transfer matrix method Forward prediction of phononic crystals with neural networks Predicting the dispersion curves of one-dimensional phononic crystals Datasets for the prediction of dispersion curves Neural networks predicting dispersion curves Choosing suitable neural networks Predicted dispersion curves in the testing sets Predicting the energy transmission spectrums of one-dimensional phononic crystals Datasets for the prediction of energy transmission spectrums Neural networks predicting energy transmission spectrums Predicted energy transmission spectrums in the testing sets Designing phononic crystals with neural networks Datasets for the inverse design of phononic crystals One-parameter design Two-parameter design Three-parameter design Summary References Nanophotonic devices based on optimization algorithms Introduction Gradient-based algorithms Topology optimization Variable density method Introduction Applications Level set method Introduction Applications Bi-directional evolutionary structural optimization Introduction Applications Objective-first algorithm Introduction Applications Other gradient-based algorithms Introduction Applications Heuristic algorithms Hill-climbing algorithm Introduction Applications Direct binary search Introduction Applications Simulated annealing algorithm Introduction Applications Tabu search Introduction Applications Genetic algorithm Introduction Applications Differential evolution algorithm Introduction Applications Particle swarm optimization algorithm Introduction Applications Ant colony algorithm Introduction Applications Conclusion References Artificial intelligence (AI) enhanced nanomotors and active matter Introduction Synthetic active particles Control of active particles Light-controllable active particles Feedback control of active particles Experimental realization of feedback control Reinforcement learning Reinforcement learning with a real microswimmer Deep reinforcement learning with a single active particle Multi-agent systems Multi-agent reinforcement learning (MARL) Future directions for MARL References Applications of convolutional neural networks for spectral analysis Introduction Fundamentals of CNNs for photonics Parameterization strategies Mathematical operations Sampling and training Predictive models for spectra calculation Fundamental spectra analysis Ultrafast spectroscopy signal analysis Generative models for spectra design Generative CNNs Applications of GCNN Dimensionality reduction models for optical property extraction Unsupervised learning with dimensionality reduction models Applications of dimensionality reduction models Perspectives and outlooks References Nanoscale electronic synapses for neuromorphic computing Introduction Realization of artificial synapses Ion migration Cation migration Anion migration Electrolyte-gated transistor Electronic migration Electron migration by external field Photogenerated carrier migration Phase transition Ferroelectric Magnetic Realization of neuromorphic engineering Synaptic plasticity Long-term potentiation and depression (LTP/LTD) Short-term potentiation and depression (STP/STD) Spiking-time dependent plasticity (STDP) Other plasticity Integration and cognitive functions Computing with synapses Artificial neuromorphic devices Summary References Nanowire memristor as artificial synapse in random networks Introduction Fundaments of NW-based memristive devices NW synthesis Fabrication strategies of NW-based memristive devices ECM mechanism VCM mechanism Single nanowire memristor as artificial synapse Nanowire random networks as artificial neural networks Nanowire network topology Memristive behavior of single network elements ``Reweighting´´ effect in single NW junctions ``Rewiring´´ effects in single NWs Emergent dynamics Synaptic plasticity in NW networks Short-term and long-term synaptic plasticity Structural plasticity Homosynaptic and heterosynaptic plasticity Criticality and avalanches effects Computing with nanowire random networks Conclusions References Artificial intelligence accelerator using photonic computing Introduction Optical weighted interconnections Free-space optical weighted interconnection Integrated optical weighted interconnection Optical neuron activation functions Photorefractive effect Saturable absorption Optical Kerr effect Structural phase transition Optical resonance Photoelectronic effect Designing photonic neural network architectures Photonic fully connected neural networks Photonic convolutional neural networks Photonic recurrent neural networks Photonic spiking neural networks Optoelectronic devices and AI systems On-chip light source Photonic memory Optoelectronic modulator Optoelectronic AI systems In-situ optical backpropagation training methods Gradient calculation based on optical backpropagation Gradient calculation based on electronic backpropagation Discussion and outlook References Machine learning in nanomaterial electron microscopy data analysis Introduction High-throughput analysis of nanomaterial microscopy data Machine learning models relevant to microscopy data analysis Training datasets in supervised learning of nanomaterial microscopy images ML in 2D microscopy image analysis Classification and regression of nanomaterial microscopy images Segmentation of nanomaterial microscopy images Object detection of nanomaterial microscopy images ML in 3D tomography reconstruction and segmentation ML-assisted analysis of nonimage data Conclusion and outlook References Deep learning in biomedical informatics Introduction Deep learning network Convolutional neural networks Recurrent neural network Long short-term memory Autoencoder Applications Transfer learning Computational biology Transcriptomics Splicing analysis Gene expression Genomics Drug discovery Computer-aided drug design New drug molecule identification Protein engineering Gene expression data analysis Pharmacodynamics modeling Medical images Image segmentation Image registration Computer-aided diagnosis Physical simulation Electronic health records Medical informatics Public health Deep learning in healthcare: Limitations and challenges References Autonomous experimentation in nanotechnology Introduction Development of AE capabilities Automation and robotics Domain knowledge, discovery systems, and artificial intelligence Machine learning, optimization, and data analysis Overview of the development of autonomous experimentation (AE) systems Case studies of AE in nanotechnology Carbon nanotube synthesis Optoelectronic properties of thin films Synthesis and optimization of nanoparticles Enhancement observed due to the introduction of automation and autonomy Platform technologies for AE in nanoscience Platforms for miniaturized liquid handling Scanning probes for patterning and interrogating nanoscale materials Conclusions and future directions References Nanomaterials and artificial intelligence in anti-counterfeiting Introduction Encryption mechanism of optical security labels Advanced optical nanomaterials for anti-counterfeiting applications Photonic crystals Luminescent materials Plasmonic materials Advanced optical anti-counterfeiting labels Conventional optical anti-counterfeiting labels Structural color-based anti-counterfeiting labels Luminescent anti-counterfeiting labels Plasmonic anti-counterfeiting labels Physical unclonable function (PUF)-based optical anti-counterfeiting labels Artificial intelligence-based authentication Summary and outlook References Machine learning data processing as a bridge between microscopy and the brain Introduction Machine learning Identifying active neurons 2D segmentation methods 3D block segmentation methods 3D frame-by-frame segmentation methods Spike inference Discussion References Index