Million-Atom Simulation of the Set Process in Phase Change Memories at the Real Device Scale

A key functional property for these applications is the fast crystal nucleation and growth in the supercool liquid phase. Over the last decade, atomistic simulations based on density functional theory (DFT) have provided crucial insights on the early stage of this process.

These simulations are, however, restricted to a few hundred atoms for at most a few ns. More recently, the scope of the DFT simulations has been greatly extended by leveraging on machine learning techniques. In our paper, we show that the exploitation of a recently devised neural network potential for the prototypical phase change compound Ge 2 Sb 2 Te 5 (GST) allows simulating the crystallization process in a multimillion atom model at the length and time scales of the real memory device.