Using computer simulations powered by machine-learning algorithms EPFL scientists have made an important breakthrough in understanding how hydrogen behaves on Saturn and Jupiter.
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The giant planets in our solar system are made mainly of hydrogen, mostly in a liquid state. Near the planets- surface, hydrogen exists in an insulating, molecular form – H 2 – but closer to the center, it takes on a metallic form where individual atoms can move around freely. Professor Michele Ceriotti, who heads the Laboratory of Computational Science and Modelling (COSMO) within EPFL’s School of Engineering, along with colleagues from the University of Cambridge and IBM Zurich, have used computer simulations to understand the nature of this elusive transition. -Hydrogen is the simplest element on the periodic table: it-s made of one proton and one electron. That’s one reason why scientists study it so much. What makes this phenomenon on giant planets fairly unique – and interesting – is that the transition is between two forms of a liquid state, and not from a liquid to a gaseous or solid state,- says Ceriotti.
An extremely complicated system to model
This transition has been the focus of an intense research effort – many research groups have tried to gain a better understanding by replicating it in a laboratory. -But that’s really difficult because you have to create the same conditions here on Earth as those on Saturn and Jupiter, which means achieving pressures that are around a million times greater than that of the Earth’s atmosphere, and then analyzing samples that have been exposed to those pressures. As you can imagine, examining compounds under those conditions isn’t easy. Various studies have been conducted, but their findings are often controversial because they differ substantially from each other,- says Ceriotti. So rather than trying to recreate giant planets- atmospheric conditions in a lab, his team used highly precise computer models to simulate them digitally. Previous attempts to do so involved solving on the fly equations describing the quantum mechanical behaviour of fluid hydrogen. The complexity of such equations limited the scope of such simulations to just a few atoms at a time and the time scale to less than one billionth of a second.
Using their expertise in computer simulation and machine-learning-based models, the team was able to expand the scope and time scale of their simulations and gain insight into the mechanisms at work in the hydrogen transition. That required using very powerful computers. -It was a considerable investment in terms of time and energy. Running our models – which we developed using machine learning – took several weeks on EPFL’s supercomputers. But if we’d tried to run models developed the conventional way, it would-ve taken hundreds of millions of years,- says Ceriotti. […]