Intelligent Nanotechnology
Merging Nanoscience and Artificial Intelligence
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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.
Key Features
- Includes recent advances on AI-enhanced design, characterization and the manufacturing of nanomaterials
- Reviews AI technologies that have been enabled by nanotechnology
- Discusses potentially world-changing applications that could ensue as a result of merging these two fields
Readership
Materials scientists and engineers. Computer Scientists
Table of Contents
- Part 1: AI-enhanced design, characterization, and manufacturing of nanomaterials, nanodevices and nanotools
1. Inverse design of nanophotonic structures
2. Artificial Intelligence (AI)-enhanced design/additive of nanomaterials
3. Neural networks for prediction in nanophononics
4. Neural networks for prediction in nanophotonic properties
5. Neural networks for prediction in device performance
6. Artificial Intelligence (AI) enhanced nanomotors and active matters
7. Application of convolution neural network in spectral analysis
8. Artificial Intelligence in nanotoxicology
Part 2: Nanotechnology-enhanced Artificial Intelligence hardware and algorithm development
9. Merging of machine learning and nanomaterials
10. Nanoscale Electronic Synapses for Neuromorphic Computing
11. Nanowire memristor as artificial synapse in random networks
12. Nanomemories for neuromorphic computing
13. Photonic artificial neural networks
14. Artificial intelligent accelerator using optoelectronic computing
Part 3: Scientific advancement and applications enhanced by combining Artificial Intelligence (AI) and Nanotechnology
15. Merging Artificial Intelligence (AI) and nanotechnology for single molecule science
16. Artificial Intelligence (AI) and nanotechnology in biomedicine
17. Automated lab/research
18. Nanomaterials and artificial intelligence in anti-counterfeiting
19. AI in chemistry studies
Part 4: Conclusion and perspective
20. Prospects and challenges in the merging of nanotechnology and Artificial Intelligence (AI)
Product details
- No. of pages: 440
- Language: English
- Copyright: © Elsevier 2022
- Published: October 26, 2022
- Imprint: Elsevier
- eBook ISBN: 9780323901413
- Paperback ISBN: 9780323857963
About the Editors
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