Structure of MgSO4 hydrates on Mars and icy moons

Magnesium sulfate hydrates are minerals that form large salt deposits on Earth, as well as on Mars and on moons and celestial bodies in the outer solar system. There, they influence fundamental geological processes, such as the water cycle on Mars or tectonic plate movements on celestial bodies like Jupiters moon Europa, which is considered one of the most promising candidates in the search for extraterrestrial life. Despite decades of research, little is known about how these mineral salts are structured at the atomic level, how stable they are under extreme conditions, and what properties they exhibit under the temperatures and pressures found inside such celestial bodies. One reason for this is that many of these minerals change or decompose under extreme environmental conditions, pushing conventional analytical methods to their limits. New developments in the field of machine learning now open entirely new possibilities, however. AI-based methods developed at the University of Cambridge enable to precisely predict the structure and properties of these minerals in extraterrestrial extreme environments. During my research stay at the University of Cambridge, I am developing methods to directly compare these theoretical predictions with experimental data. This allows me to reinterpret both experimental data acquired I worked on during my postdoctoral time at NASA which was by the Curiosity rover as well as experimental data on magnesium sulfate hydrates under simulated Europa conditions. Reinterpreting these data using an innovative AI-based approach may make a critical contribution towards closing existing knowledge gaps and significantly expanding our understanding of both the interiors of icy moons and the water cycle on Mars. In addition, these methods will be further refined and applied during my return phase at the Institute of Mineralogy and Petrography at the University of Innsbruck. The institute has excellent laboratory facilities for simulating extreme pressure and temperature conditions, relevant to the surfaces and interiors of planets and moons, thus providing great potential for the application of the AI-based methods developed in this project. In this way, this Schrödinger project makes an important contribution to knowledge transfer to Austria.