Machine Learning Plasmas and the Neuromorphic Plasma Chemistry

Plasma chemistry, a foundational science driving advancements in the engineering of biomedicine, space propulsion, and semiconductor manufacturing, is currently increasingly relying on AI to diagnose and control chemical compositions and reaction rates in plasmas. This book presents a groundbreaking aspect of fundamental plasma chemistry that integrates modern ML and neuromorphic systems and is the first book to introduce such a unique theory. It explores these topics in depth and introduces the revolutionary concept of chemical systems that can function as molecule-based programmable intelligent materials, proposing a new form of AI that operates without digital computers but by using chemical pathway networks.

Li Lin is a research scientist at the School of Engineering and Applied Science, George Washington University, USA. He specializes in low-temperature plasma physics, and his research focuses on chemical pathways and adaptive plasma control applied to biomedical and environmental sciences. His work also bridges machine learning, neuromorphic concepts, numerical simulations, and plasma diagnostics. Dr Lin is an honorary member of the National Academy of Inventors (NAI) and serves on the editorial boards of various reputed journals, including Scientific Reports and Frontiers in Physics. He has also been awarded for his contributions to IOP journals.

Michael Keidar is an A. James Clark Professor of Engineering at the School of Engineering and Applied Science, George Washington University, USA. His expertise spans advanced spacecraft propulsion, plasma-based nanotechnology, and plasma medicine. Dr Keidar has authored over 250 journal articles and a textbook on plasma engineering. He was named the AIAA National Capital Section Engineer of 2016 and is a recipient of the 2017 Davidson Award in plasma physics. He is a fellow of the APS, AIAA, and the National Academy of Inventors and serves as an editor in leading academic journals.