AI for Materials Science

Topic: AI for Materials Science

Abstract:

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. 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. Our implementation is based on the open-source mlr and mlrMBO frameworks and can be applied in many other contexts, some of which we briefly outline at the end of the talk.

Speaker(s):

Dr. Lars Kotthoff is an assistant professor of Computer Science at the University of Wyoming, and has held postdoctoral appointments at the University of St Andrews (where he also completed his PhD), University College Cork, and the University of British Columbia. He has made contributions to solving hard AI problems, automated machine learning, and applications of AI in Materials Science. His publications have been cited more than 4,800 times.

Google Scholar: https://scholar.google.de/citations?user=P37OkUUAAAAJ