Machine Learning Plasmas and the Neuromorphic Plasma Chemistry
暫譯: 機器學習等離子體與神經形態等離子體化學

等離子體化學是一門基礎科學，推動生物醫學、太空推進和半導體製造工程的進步，目前越來越依賴人工智慧（AI）來診斷和控制等離子體中的化學成分和反應速率。本書介紹了一個開創性的基礎等離子體化學方面，整合了現代機器學習（ML）和神經形態系統，並且是第一本介紹這種獨特理論的書籍。它深入探討這些主題，並引入了革命性的概念，即可以作為基於分子的可編程智能材料運作的化學系統，提出了一種新的人工智慧形式，該形式不依賴數位計算機，而是通過使用化學途徑網絡來運作。

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.

林立是美國喬治華盛頓大學工程與應用科學學院的研究科學家。他專注於低溫等離子體物理，研究重點在於應用於生物醫學和環境科學的化學途徑及自適應等離子體控制。他的工作還結合了機器學習、類神經概念、數值模擬和等離子體診斷。林博士是全國發明家學院（NAI）的名譽會員，並擔任多本知名期刊的編輯委員會成員，包括Scientific Reports和Frontiers in Physics。他因對IOP期刊的貢獻而獲得獎項。

邁克爾·凱達爾是美國喬治華盛頓大學工程與應用科學學院的A. James Clark工程教授。他的專業涵蓋先進航天器推進、基於等離子體的納米技術和等離子體醫學。凱達爾博士已發表超過250篇期刊文章及一本有關等離子體工程的教科書。他於2016年被評選為AIAA國家首都分會工程師，並於2017年獲得等離子體物理的Davidson獎。他是美國物理學會（APS）、美國航空航天學會（AIAA）和全國發明家學院的會士，並在多本領先的學術期刊擔任編輯。