A machine learning approach to light-induced order-disorder phase transitions: large-scale long-time simulations with ab initio accuracy

While machine learning has excelled in simulating material thermal properties, its application to light-induced order-disorder non-thermal phase transitions has been limited by challenges in accurately describing the potential energy surface, the forces and the vibrational properties in the presence of a photoexcited electron-hole plasma. Here, we present a novel approach that combines constrained density functional perturbation theory with machine learning techniques, yielding highly reliable interatomic potentials capable of capturing electron-hole plasma effects on structural properties. Applied to photoexcited silicon, our potential is ten times more accurate than previous models. We show that, at low enough temperatures, the non-thermal melting transition is driven by a soft phonon and the formation of a double well potential, at odds with thermal melting being strictly first order. Our method paves the way to large-scale long-time simulations of light-induced order-disorder phase transitions in materials with ab initio accuracy