Intelligent Nanotechnology

Merging Nanoscience and Artificial Intelligence

1st Edition - October 26, 2022
Editors: Yuebing Zheng, Zilong Wu
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 7 9 6 - 3
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 1 4 1 - 3

Intelligent Nanotechnology: Merging Nanoscience and Artificial Intelligence provides an overview of advances in science and technology made possible by the convergence of… Read more

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Intelligent Nanotechnology: Merging Nanoscience and Artificial Intelligence provides an overview of advances in science and technology made possible by the convergence of nanotechnology and artificial intelligence (AI). Sections focus on AI-enhanced design, characterization and manufacturing and the use of AI to improve important material properties, with an emphasis on mechanical, photonic, electronic and magnetic properties. Designing benign nanomaterials through the prediction of their impact on biology and the environment is also discussed. Other sections cover the use of AI in the acquisition and analysis of data in experiments and AI technologies that have been enhanced through nanotechnology platforms.

Final sections review advances in applications enabled by the merging of nanotechnology and artificial intelligence, including examples from biomedicine, chemistry and automated research.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Acknowledgments
Section 1: AI-enhanced design, characterization, and manufacturing of nanomaterials, nanodevices and nanotools
Chapter 1: Inverse design meets nanophotonics: From computational optimization to artificial neural network
Abstract
Acknowledgments
1: Computational inverse design
2: Deep learning-based inverse design
3: Deep learning merged with computational optimization
References
Chapter 2: Machine learning for solid mechanics
Abstract
1: Introduction
2: Case studies
3: Future opportunities and considerations
4: Conclusions
References
Chapter 3: Neural networks in phononics
Abstract
Acknowledgment
1: Introduction
2: One-dimensional phononic crystals and transfer matrix method
3: Forward prediction of phononic crystals with neural networks
4: Designing phononic crystals with neural networks
5: Summary
References
Chapter 4: Nanophotonic devices based on optimization algorithms
Abstract
1: Introduction
2: Gradient-based algorithms
3: Heuristic algorithms
4: Conclusion
References
Chapter 5: Artificial intelligence (AI) enhanced nanomotors and active matter
Abstract
1: Introduction
2: Synthetic active particles
3: Control of active particles
4: Reinforcement learning
5: Future directions for MARL
References
Chapter 6: Applications of convolutional neural networks for spectral analysis
Abstract
1: Introduction
2: Fundamentals of CNNs for photonics
3: Predictive models for spectra calculation
4: Generative models for spectra design
5: Dimensionality reduction models for optical property extraction
6: Perspectives and outlooks
References
Section 2: Nanotechnology-enhanced artificial intelligence hardware and algorithm development
Chapter 7: Nanoscale electronic synapses for neuromorphic computing
Abstract
Acknowledgments
Conflict of interest
1: Introduction
2: Realization of artificial synapses
3: Realization of neuromorphic engineering
4: Summary
References
Chapter 8: Nanowire memristor as artificial synapse in random networks
Abstract
1: Introduction
2: Fundaments of NW-based memristive devices
3: Single nanowire memristor as artificial synapse
4: Nanowire random networks as artificial neural networks
5: Computing with nanowire random networks
6: Conclusions
References
Chapter 9: Artificial intelligence accelerator using photonic computing
Abstract
1: Introduction
2: Optical weighted interconnections
3: Optical neuron activation functions
4: Designing photonic neural network architectures
5: Optoelectronic devices and AI systems
6: In-situ optical backpropagation training methods
7: Discussion and outlook
References
Section 3: Scientific advancement and applications enhanced by combining Artificial Intelligence (AI) and Nanotechnology
Chapter 10: Machine learning in nanomaterial electron microscopy data analysis
Abstract
Acknowledgment
1: Introduction
2: ML in 2D microscopy image analysis
3: ML in 3D tomography reconstruction and segmentation
4: ML-assisted analysis of nonimage data
5: Conclusion and outlook
References
Chapter 11: Deep learning in biomedical informatics
Abstract
1: Introduction
2: Deep learning network
3: Drug discovery
4: Medical images
5: Electronic health records
References
Chapter 12: Autonomous experimentation in nanotechnology
Abstract
1: Introduction
2: Development of AE capabilities
3: Case studies of AE in nanotechnology
4: Platform technologies for AE in nanoscience
5: Conclusions and future directions
References
Chapter 13: Nanomaterials and artificial intelligence in anti-counterfeiting
Abstract
1: Introduction
2: Encryption mechanism of optical security labels
3: Advanced optical nanomaterials for anti-counterfeiting applications
4: Advanced optical anti-counterfeiting labels
5: Artificial intelligence-based authentication
6: Summary and outlook
References
Chapter 14: Machine learning data processing as a bridge between microscopy and the brain
Abstract
1: Introduction
2: Machine learning
3: Identifying active neurons
4: Spike inference
5: Discussion
References
Index

Yuebing Zheng

Yuebing Zheng is an Associate Professor of Mechanical Engineering and Materials Science & Engineering at the University of Texas at Austin, USA. He is also affiliated with the Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Texas Materials Institute, Center for Electrochemistry, and Center for Planetary Systems Habitability at the University of Texas at Austin. He received his B.Sc. in Physics from Nankai University, China, in 2001; M.Sc. in Physics from National University of Singapore, Singapore, in 2004; and Ph.D. in Engineering Science and Mechanics from the Pennsylvania State University, USA, in 2010. He was a postdoctoral researcher at the University of California, Los Angeles from 2010 to 2013, and joined the University of Texas at Austin as an Assistant Professor in the Fall of 2013. Zheng Research Group (http://zheng.engr.utexas.edu ) at the University of Texas at Austin explores intelligent nanophotonics, which merges artificial intelligence and photonics at the nanoscale, to advance nanomanufacturing, energy, global health, and life sciences. He received 2020 Texas Health Catalyst Award, 2019 University Co-op Research Excellence Award for Best Paper, 2018 Materials Today Rising Star Award, 2017 NIH Director’s New Innovator Award, 2017 NASA Early Career Faculty Award, 2017 ONR Young Investigator Award, 2015 3M Non-Tenured Faculty Award, and 2014 Beckman Young Investigator Award. He is a fellow of the Institute of Physics, a fellow of the Royal Society of Chemistry, and a senior member of the Optical Society of America.

Affiliations and expertise

Associate Professor, The University of Texas at Austin, Austin, TX, USA

Zilong Wu

Zilong Wu received his Ph.D. in Materials Science and Engineering (with Prof. Yuebing Zheng) from the University of Texas at Austin in 2018. He received his M.Sc. degree in 2014 from the Fudan University and his B.E. degree in 2011 from the Sun Yat-sen University. He is currently a Postdoctoral Fellow in the Texas Materials Institute at the University of Texas at Austin. His research focus is on innovating novel optical materials and techniques at the nanoscale.

Affiliations and expertise

Postdoctoral Fellow, The University of Texas at Austin, Austin, TX, USA

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health