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Abstract In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century. 

The IceCube Neutrino Observatory at the South Pole has measured the diffuse astrophysical neutrino flux up to ~PeV energies and is starting to identify first point source candidates. The next generation facility, IceCube-Gen2, aims at extending the accessible energy range to EeV in order to measure the continuation of the astrophysical spectrum, to identify neutrino sources, and to search for a cosmogenic neutrino flux. As part of IceCube-Gen2, a radio array is foreseen that is sensitive to detect Askaryan emission of neutrinos beyond ~30 PeV. Surface and deep antenna stations have different benefits in terms of effective area, resolution, and the capability to reject backgrounds from cosmic-ray air showers and may be combined to reach the best sensitivity. The optimal detector configuration is still to be identified. This contribution presents the full-array simulation efforts for a combination of deep and surface antennas, and compares different design options with respect to their sensitivity to fulfill the science goals of IceCube-Gen2. 

The IceCube Neutrino Observatory opened the window on high-energy neutrino astronomy by confirming the existence of PeV astrophysical neutrinos and identifying the first compelling astrophysical neutrino source in the blazar TXS0506+056. Planning is underway to build an enlarged detector, IceCube-Gen2, which will extend measurements to higher energies, increase the rate of observed cosmic neutrinos and provide improved prospects for detecting fainter sources. IceCube-Gen2 is planned to have an extended in-ice optical array, a radio array at shallower depths for detecting ultra-high-energy (>100 PeV) neutrinos, and a surface component studying cosmic rays. In this contribution, we will discuss the simulation of the in-ice optical component of the baseline design of the IceCube-Gen2 detector, which foresees the deployment of an additional ~120 new detection strings to the existing 86 in IceCube over ~7 Antarctic summer seasons. Motivated by the phased construction plan for IceCube-Gen2, we discuss how the reconstruction capabilities and sensitivities of the instrument are expected to progress throughout its deployment.