Michael te Vrugt is assistant professor of physics at the Johannes Gutenberg University Mainz in Germany. Previously, he completed PhDs in theoretical physics and philosophy of science, both at the University of Münster in Germany, and worked as a postdoctoral researcher at the University of Cambridge in UK. His main fields of research are nonequilibrium statistical mechanics, theory of soft condensed matter, and philosophy of physics. He is the author of more than 35 scientific articles and has received several awards for his work, including the Infineon PhD Award and the CRC 1459’s Young Researcher Award for Intelligent Matter.

• Preface.- 1. Introduction: Artificial Intelligence and Intelligent Matter.- Part I: Introduction to Artificial Intelligence.- 2. Introduction to Artificial Intelligence.- 3. An Introduction to Reinforcement Learning – In Artificial and Biological Control Systems.- 4. An Introduction to Reservoir Computing.- Part II: Applications of Artificial Intelligence to Nanosystems. 5. Learning Dynamical Systems from Data.- 6. Machine Learning Approaches to Classical Density Functional Theory.- 7. Machine Learning in Quantum Density Functional Theory.- 8. Generative Deep learning for the Inverse Design of Materials.- 9. Machine Learning for Identifying Dynamical Phases Intopologicallasers.- 10. Artificial Intelligence Reshaping the Semiconductor Metrology.- 11. Machine Learning for Active Matter.- Part III: Implementations of Artificial Intelligence in Nanosystems.- 12. A Primer on Neuromorphic Hardware: Underlying Principles and Approaches – From Sensors to Processors.- 13. Towards Intelligent Active Particles.- 14. Intelligent Matter Consisting of Active Particles.- 15. Neural Networks Consisting of DNA.- 16. Intelligence Without a Brain – Perspectives from the Aneural Slime Mold as a Computing Model for Artificial Intelligence.- 17. Integrated Photonics for Neuromorphic Computing.- 18. Time-multiplexed Reservoir Computing with Semiconductor Laser Systems.- 19. Neuromorphic Spintronics.- 20. Machine Learning with Quantum Computers.- Part IV: Philosophical Aspects.- 21. Normativity and the Intelligent Matter Framework.- 22. What Is It Like To Be a Nail? – The Intentions of Matter.- 23. Rethinking Computational Implementation Through Symphoria.- 24. Ethical Aspects of Artificial Intelligence in Nanoscience.

Michael te Vrugt is assistant professor of physics at the Johannes Gutenberg University Mainz in Germany. Previously, he completed PhDs in theoretical physics and philosophy of science, both at the University of Münster in Germany, and worked as a postdoctoral researcher at the University of Cambridge in UK. His main fields of research are nonequilibrium statistical mechanics, theory of soft condensed matter, and philosophy of physics. He is the author of more than 35 scientific articles and has received several awards for his work, including the Infineon PhD Award and the CRC 1459’s Young Researcher Award for Intelligent Matter.

• Preface.- 1. Introduction: Artificial Intelligence and Intelligent Matter.- Part I: Introduction to Artificial Intelligence.- 2. Introduction to Artificial Intelligence.- 3. An Introduction to Reinforcement Learning – In Artificial and Biological Control Systems.- 4. An Introduction to Reservoir Computing.- Part II: Applications of Artificial Intelligence to Nanosystems. 5. Learning Dynamical Systems from Data.- 6. Machine Learning Approaches to Classical Density Functional Theory.- 7. Machine Learning in Quantum Density Functional Theory.- 8. Generative Deep learning for the Inverse Design of Materials.- 9. Machine Learning for Identifying Dynamical Phases Intopologicallasers.- 10. Artificial Intelligence Reshaping the Semiconductor Metrology.- 11. Machine Learning for Active Matter.- Part III: Implementations of Artificial Intelligence in Nanosystems.- 12. A Primer on Neuromorphic Hardware: Underlying Principles and Approaches – From Sensors to Processors.- 13. Towards Intelligent Active Particles.- 14. Intelligent Matter Consisting of Active Particles.- 15. Neural Networks Consisting of DNA.- 16. Intelligence Without a Brain – Perspectives from the Aneural Slime Mold as a Computing Model for Artificial Intelligence.- 17. Integrated Photonics for Neuromorphic Computing.- 18. Time-multiplexed Reservoir Computing with Semiconductor Laser Systems.- 19. Neuromorphic Spintronics.- 20. Machine Learning with Quantum Computers.- Part IV: Philosophical Aspects.- 21. Normativity and the Intelligent Matter Framework.- 22. What Is It Like To Be a Nail? – The Intentions of Matter.- 23. Rethinking Computational Implementation Through Symphoria.- 24. Ethical Aspects of Artificial Intelligence in Nanoscience.

Michael te Vrugt is assistant professor of physics at the Johannes Gutenberg University Mainz in Germany. Previously, he completed PhDs in theoretical physics and philosophy of science, both at the University of Münster in Germany, and worked as a postdoctoral researcher at the University of Cambridge in UK. His main fields of research are nonequilibrium statistical mechanics, theory of soft condensed matter, and philosophy of physics. He is the author of more than 35 scientific articles and has received several awards for his work, including the Infineon PhD Award and the CRC 1459’s Young Researcher Award for Intelligent Matter.

• Preface.- 1. Introduction: Artificial Intelligence and Intelligent Matter.- Part I: Introduction to Artificial Intelligence.- 2. Introduction to Artificial Intelligence.- 3. An Introduction to Reinforcement Learning – In Artificial and Biological Control Systems.- 4. An Introduction to Reservoir Computing.- Part II: Applications of Artificial Intelligence to Nanosystems. 5. Learning Dynamical Systems from Data.- 6. Machine Learning Approaches to Classical Density Functional Theory.- 7. Machine Learning in Quantum Density Functional Theory.- 8. Generative Deep learning for the Inverse Design of Materials.- 9. Machine Learning for Identifying Dynamical Phases Intopologicallasers.- 10. Artificial Intelligence Reshaping the Semiconductor Metrology.- 11. Machine Learning for Active Matter.- Part III: Implementations of Artificial Intelligence in Nanosystems.- 12. A Primer on Neuromorphic Hardware: Underlying Principles and Approaches – From Sensors to Processors.- 13. Towards Intelligent Active Particles.- 14. Intelligent Matter Consisting of Active Particles.- 15. Neural Networks Consisting of DNA.- 16. Intelligence Without a Brain – Perspectives from the Aneural Slime Mold as a Computing Model for Artificial Intelligence.- 17. Integrated Photonics for Neuromorphic Computing.- 18. Time-multiplexed Reservoir Computing with Semiconductor Laser Systems.- 19. Neuromorphic Spintronics.- 20. Machine Learning with Quantum Computers.- Part IV: Philosophical Aspects.- 21. Normativity and the Intelligent Matter Framework.- 22. What Is It Like To Be a Nail? – The Intentions of Matter.- 23. Rethinking Computational Implementation Through Symphoria.- 24. Ethical Aspects of Artificial Intelligence in Nanoscience.