  In the last few years, much efforts have gone into developing universal
machine-learning potentials able to describe interactions for a wide range of
structures and phases. Yet, as attention turns to more complex materials
including alloys, disordered and heterogeneous systems, the challenge of
providing reliable description for all possible environment become ever more
costly. In this work, we evaluate the benefits of using specific versus general
potentials for the study of activated mechanisms in solid-state materials. More
specifically, we tests three machine-learning fitting approaches using the
moment-tensor potential to reproduce a reference potential when exploring the
energy landscape around a vacancy in Stillinger-Weber silicon crystal and
silicon-germanium zincblende structure using the activation-relaxation
technique nouveau (ARTn). We find that a a targeted on-the-fly approach
specific and integrated to ARTn generates the highest precision on the
energetic and geometry of activated barriers, while remaining cost-effective.
This approach expands the type of problems that can be addressed with
high-accuracy ML potentials.
