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The universal mathematical form of machine-learning potentials (MLPs) shiftsthe core of development of interatomic potentials to collecting proper trainingdata. Ideally, the training set should encompass diverse local atomicenvironments but the conventional approach is prone to sampling similarconfigurations repeatedly, mainly due to the Boltzmann statistics. As such,practitioners handpick a large pool of distinct configurations manually,stretching the development period significantly. Herein, we suggest a novelsampling method optimized for gathering diverse yet relevant configurationssemi-automatically. This is achieved by applying the metadynamics with thedescriptor for the local atomic environment as a collective variable. As aresult, the simulation is automatically steered toward unvisited localenvironment space such that each atom experiences diverse chemical environmentswithout redundancy. We apply the proposed metadynamics sampling to H:Pt(111),GeTe, and Si systems. Throughout the examples, a small number of metadynamicstrajectories can provide reference structures necessary for traininghigh-fidelity MLPs. By proposing a semi-automatic sampling method tuned forMLPs, the present work paves the way to wider applications of MLPs to manychallenging applications.

Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials