A team of researchers from MIPT, Skoltech, and Dukhov Research Institute of Automatics, led by Artem Oganov, used a machine learning technique to model the behavior of aluminum and uranium in the liquid and crystalline phases at various temperatures and pressures. Such simulations of chemical systems can predict their properties under a range of conditions before experiments are performed, enabling further work with only the most promising materials. The research findings were published in the journal Scientific Reports.