Training sets for initial GAPs including those that used GP variance as a selection criteria were constructed using the gap-train code (v. 1.0.0b). ACE potentials were trained using the ACE.jl code 35 (v 0.8.4, Julia v. 1.6.3) and wrapped by pyjulip (v. 0.1, using PyCall). All MD simulations were propagated using the Langevin (0.02 friction coefficient) integrator in ASE v. 3.22.0 with initial velocities sampled from a Maxwell-Boltzmann distribution then propagated using a 0.5 fs timestep. NequIP 14 potentials used the nequip 36 v. 0.3.3 Python package (pytorch v. 1.9.1, pytorch_geometric v. 1.7.2, e2nn 37 v.0.3.5) with a maximum 1000 epochs and a 90:10 training to validation data split; all other hyperparameters were retained as their defaults (4.0 Å cut off radius, see nequip module in mlp-train, commit 0ba027c). Gas phase (vacuum) simulations in periodic potentials used a large cubic box (l = 10 nm). Combinations of harmonic restraining potentials were added and incorporated into the dynamics using ASE.