Rare events can influence many physical systems and occur on a large scale. While not strictly physical, I will also provide some brief insights how such events may have impacted my career, from my beginnings as a physics student in Bremen, through my time in Lausanne, to finally settling in Hamburg and Geesthacht. From a scientific perspective, I will discuss the ubiquity of water and its unique structural dynamics. Water plays a pivotal role in shaping the properties and functionality of natural materials as a 'working fluid'. Inspired by these marvels of nature, the core idea is to develop a new class of sustainable, interactive and architected 'blue materials' that derive their functionality from the multiscale structures of hard matter interacting with water. Novel effects achieved through the nanoconfinement of water are increasingly being exploited for this, but unfortunately these often do not exceed the laboratory scale. I will demonstrate how these nanoconfinement effects could be exploited to store energy more efficiently in supercapacitors, harvest energy from temperature differences via the electrolytic Seebeck effect, and utilise capillary-driven imbibition and drying cycles in nanopores to enable energy harvesting on a much larger scale. I will also present recent fluidic insights into the solid–liquid interface, which is often only nanometres thick yet tremendously important for many physical and chemical processes. Along this line, I will explain why machine learning force fields are becoming increasingly important and how they are currently disrupting the field.

Robert Mei?ner is a professor and the head of the Institute for Interface Physics and Engineering at the Hamburg University of Technology. He is also head of the Department of Atomistic Corrosion Informatics at the Helmholtz-Zentrum Hereon. He obtained a diploma in physics in 2010 and a PhD in production engineering in 2015, both from the University of Bremen. Prior to moving to Hamburg, he was a postdoctoral fellow at the ?cole polytechnique fédérale de Lausanne. His research focuses on applying machine learning methods to atomistic simulations, particularly for modelling electrochemical processes which includes using virtual potentiostats in empirical force fields and ab initio methods. He aims to understand the underlying mechanisms of electrochemical processes, energy storage and corrosion at solid-liquid interfaces by combining these methods to improve materials from the atomic scale. He is passionate about science and interpreting the world through algorithms.