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.
1. Intelligent Nanotechnology - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. ix 7. Preface - p. xiii 8. Acknowledgments - p. xv 9. Chapter 1 Inverse design meets nanophotonics: From computational optimization to artificial neural network - p. 3 10. Chapter 2 Machine learning for solid mechanics - p. 33 11. Chapter 3 Neural networks in phononics - p. 47 12. Chapter 4 Nanophotonic devices based on optimization algorithms - p. 71 13. Chapter 5 Artificial intelligence (AI) enhanced nanomotors and active matter - p. 113 14. Chapter 6 Applications of convolutional neural networks for spectral analysis - p. 145 15. Chapter 7 Nanoscale electronic synapses for neuromorphic computing - p. 189 16. Chapter 8 Nanowire memristor as artificial synapse in random networks - p. 219 17. Chapter 9 Artificial intelligence accelerator using photonic computing - p. 247 18. Chapter 10 Machine learning in nanomaterial electron microscopy data analysis - p. 279 19. Chapter 11 Deep learning in biomedical informatics - p. 307 20. Chapter 12 Autonomous experimentation in nanotechnology - p. 331 21. Chapter 13 Nanomaterials and artificial intelligence in anti-counterfeiting - p. 361 22. Chapter 14 Machine learning data processing as a bridge between microscopy and the brain - p. 399 23. Index - p. 421
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.
1. Intelligent Nanotechnology - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. ix 7. Preface - p. xiii 8. Acknowledgments - p. xv 9. Chapter 1 Inverse design meets nanophotonics: From computational optimization to artificial neural network - p. 3 10. Chapter 2 Machine learning for solid mechanics - p. 33 11. Chapter 3 Neural networks in phononics - p. 47 12. Chapter 4 Nanophotonic devices based on optimization algorithms - p. 71 13. Chapter 5 Artificial intelligence (AI) enhanced nanomotors and active matter - p. 113 14. Chapter 6 Applications of convolutional neural networks for spectral analysis - p. 145 15. Chapter 7 Nanoscale electronic synapses for neuromorphic computing - p. 189 16. Chapter 8 Nanowire memristor as artificial synapse in random networks - p. 219 17. Chapter 9 Artificial intelligence accelerator using photonic computing - p. 247 18. Chapter 10 Machine learning in nanomaterial electron microscopy data analysis - p. 279 19. Chapter 11 Deep learning in biomedical informatics - p. 307 20. Chapter 12 Autonomous experimentation in nanotechnology - p. 331 21. Chapter 13 Nanomaterials and artificial intelligence in anti-counterfeiting - p. 361 22. Chapter 14 Machine learning data processing as a bridge between microscopy and the brain - p. 399 23. Index - p. 421
