Structure and properties of amorphous silicon from machine learning
— A combination of machine learning models predicting the stability as well as the electronic properties of materials with the accuracy of quantum mechanics allowed to understand the structural transitions that silicon undergoes when compressed to tens of GPa. The study, performed by an international team from Oxford, Cambridge, the US Naval Research Laboratory and Ohio University, as well as EPFL's Laboratory of Computational Science and Modeling, has been published in Nature.