Evaluating approaches for on-the-fly machine-learning interatomic potentials for activated mechanisms sampling with ARTn

Methods, such as the Activation-Relaxation Technique Nouveau (ARTn), have proven effective at finding and identifying activated mechanisms pathways in solid-state materials.

The mechanisms discovered are physically accurate within the realm of ab initio calculations. However, due to its computational cost, this method is limited to small systems and a small set of events. However, for most problems of interest, these accurate calculations are out of reach, and empirical potentials must be used, which are notoriously unreliable resulting in qualitative results.

To overcome this limitation, in the last few years, much efforts have gone into developing general machine-learning potentials able to describe interactions for a wide range of structures and phases. Still, 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 on-the-fly machine-learning potentials for the study of activated mechanisms. More specifically, we tests three fitting approaches using the moment-tensor potential to reproduce a reference potential when exploring the energy landscape around a vacancy in silicon crystal and silicon-germanium zincblende structure using ARTn.

We find that a a targeted approach specific and integrated to ARTn generates the highest precision on the energetic and geometry of activated events, while remaining cost-effective. This approach expands the type of problems that can be addressed with high-accuracy ML potentials.