Data-driven Surrogate Modeling of Matter under Extreme Conditions

The successful diagnostics of phenomena in matter under extreme conditions relies on a strong interplay between experiment and simulation. Understanding these phenomena is key to advancing our fundamental knowledge of astrophysical objects and has the potential to unlock future energy technologies that have great societal impact.
A great challenge for an accurate numerical modeling is the persistence of electron correlation and has hitherto impeded our ability to model these phenomena across multiple length and time scales at sufficient accuracy.
In this talk, I will summarize our recent efforts on devising a data-driven workflow to tackle this challenge. Based on first-principles data we generate machine-learning surrogate models that replace traditional electronic-structure algorithms. Our surrogates both predict the electronic structure and yield thermo-magneto-elastic materials properties of matter under extreme conditions highly efficiently while maintaining their accuracy. This opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.