Prof. Patrick Rinke, TUM Chair of AI-based Materials Science, agrees that the impact of machine learning and deep learning has been huge: “No other development has had quite the same effects because machine learning can be and is used in all of physics and all of chemistry.” Rinke continues, “In every sub-discipline, machine learning has led to new insights, facilitated acceleration of data generation and understanding, and has enabled research that simply wasn't possible in this form before.” In his own research, ML has allowed Rinke to tackle a much wider range of scientific problems. “My previous expertise was in atomistic modeling. With machine learning, we can go beyond the atomic scale and address challenges with societal impact, like sustainable materials development (such as organic electronics).”
School of Natural Sciences, Department of Physics
https://www.nat.tum.de/en/nat/latest/physics/article/angetrieben-durch-maschinelles-lernen/