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Although machine-learning potentials have recently had substantial impact onmolecular simulations, the construction of a robust training set can stillbecome a limiting factor, especially due to the requirement of a reference abinitio simulation that covers all the relevant geometries of the system.Recognizing that this can be prohibitive for certain systems, we develop themethod of transition tube sampling that mitigates the computational cost oftraining set and model generation. In this approach, we generate classical orquantum thermal geometries around a transition path describing a conformationalchange or a chemical reaction using only a sparse set of local normal modeexpansions along this path and select from these geometries by an activelearning protocol. This yields a training set with geometries that characterizethe whole transition without the need for a costly reference trajectory. Theperformance of the method is evaluated on different molecular systems with thecomplexity of the potential energy landscape increasing from a single minimumto a double proton-transfer reaction with high barriers. Our results show thatthe method leads to training sets that give rise to models applicable inclassical and path integral simulations alike that are on par with those baseddirectly on ab initio calculations while providing the computational speed-upwe have come to expect from machine-learning potentials.

Reducing the cost of neural network potential generation for reactive molecular systems