Indirect Learning of an Interatomic Potential to Model the Phase-Change Material Ge₂Sb₂Te₅

Chalcogenide phase change materials such as Ge₂Sb₂Te₅ (GST) have received attention due to their ability to transition rapidly between stable solid states, giving them binary data retention properties ideal for computer memory. Thus, there is wide interest in studying this material via atomistic modeling. Recently, a Gaussian Approximation Potential (GAP) was trained to reproduce Density Functional Theory (DFT) energies and forces at a fraction of the computational cost; however, GAP simulations are still far too slow to study the phase change properties that make GST so valuable. Here we present a machine-learned (ML) potential that performs three orders of magnitude faster than GAP. Rather than training the ML potential directly from DFT, we use the concept of indirect learning and train from configurations generated using the GAP potential, allowing us to generate a far more extensive training set than could be considered using DFT. The resulting potential - trained using the Atomic Cluster Expansion (ACE) model - reproduces the structure and thermodynamics of the GAP potential. The tremendous improvement in speed of the ACE potential allows us to study in depth the phase change behavior of GST, as well as explore the possible fragile-to-strong crossover in amorphous phases.