AI and machine learning have advanced the state of the art in many application domains. We present an application to materials science; in particular, we use surrogate models with Bayesian optimization for automated parameter tuning to optimize the fabrication of laser-induced graphene. This process allows to create thin conductive lines in thin layers of insulating material, enabling the development of next-generation nano-circuits. This is of interest for example for in-space manufacturing. We are able to achieve improvements of up to a factor of two compared to existing approaches in the literature and to what human experts are able to achieve, in a reproducible manner. Our implementation is based on the open-source mlr and mlrMBO frameworks and generalizes to other applications.
23 April at 13:00 in the JSI E-lecture room (Old Technological Park, Teslova 30, 1st Floor, Room 38/39)
AI for Materials Science: Tuning Laser-Induced Graphene Production
Lars Kotthoff
23 April at 13:00 in the JSI E-lecture room (Old Technological Park, Teslova 30, 1st Floor, Room 38/39)
AI for Materials Science: Tuning Laser-Induced Graphene Production
prof. dr. Lars Kotthoff, St Andrews University
Lars Kotthoff is a professor of Computer Science at the University of St Andrews, where he holds the Johann and Gaynor Rupert chair in Artificial Intelligence. He is a visiting professor at Sorbonne Université and an adjunct professor at the University of Wyoming, where he previously held a faculty appointment. Lars has held postdoctoral appointments at the University of British Columbia and University College Cork, after obtaining a PhD from the University of St Andrews. He has made contributions to automated machine learning, algorithm selection, and applications of AI in materials science. Lars is one of the core contributors to the mlr3 machine learning framework
Lars Kotthoff
prof. dr. Lars Kotthoff, St Andrews University
