Phase discovery with active learning: Application to structural phase
  transitions in equiatomic NiTi
Jonathan Vandermause Anders Johansson Yucong Miao Joost J. Vlassak Boris Kozinsky
Nickel titanium (NiTi) is a protypical shape-memory alloy used in a range of
biomedical and engineering devices, but direct molecular dynamics simulations
of the martensitic B19' -> B2 phase transition driving its shape-memory
behavior are rare and have relied on classical force fields with limited
accuracy. Here, we train four machine-learned force fields for equiatomic NiTi
based on the LDA, PBE, PBEsol, and SCAN DFT functionals. The models are trained
on the fly during NPT molecular dynamics, with DFT calculations and model
updates performed automatically whenever the uncertainty of a local energy
prediction exceeds a chosen threshold. The models achieve accuracies of 1-2
meV/atom during training and are shown to closely track DFT predictions of B2
and B19' elastic constants and phonon frequencies. Surprisingly, in large-scale
molecular dynamics simulations, only the SCAN model predicts a reversible B19'
-> B2 phase transition, with the LDA, PBE, and PBEsol models predicting a
reversible transition to a previously uncharacterized low-volume phase, which
we hypothesize to be a new stable high-pressure phase. We examine the structure
of the new phase and estimate its stability on the temperature-pressure phase
diagram. This work establishes an automated active learning protocol for
studying displacive transformations, reveals important differences between DFT
functionals that can only be detected in large-scale simulations, provides an
accurate force field for NiTi, and identifies a new phase.