  Machine learning driven interatomic potentials, including Gaussian
approximation potential (GAP) models, are emerging tools for atomistic
simulations. Here, we address the methodological question of how one can fit
GAP models that accurately predict vibrational properties in specific regions
of configuration space, whilst retaining flexibility and transferability to
others. We use an adaptive regularization of the GAP fit that scales with the
absolute force magnitude on any given atom, thereby exploring the Bayesian
interpretation of GAP regularization as an "expected error", and its impact on
the prediction of physical properties for a material of interest. The approach
enables excellent predictions of phonon modes (to within 0.1-0.2 THz) for
structurally diverse silicon allotropes, and it can be coupled with existing
fitting databases for high transferability. These findings and workflows are
expected to be useful for GAP-driven materials modeling more generally.
