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In recent years, the prediction of quantum mechanical observables with
machine learning methods has become increasingly popular. Message-passing
neural networks (MPNNs) solve this task by constructing atomic representations,
from which the properties of interest are predicted. Here, we introduce a
method to automatically identify chemical moieties (molecular building blocks)
from such representations, enabling a variety of applications beyond property
prediction, which otherwise rely on expert knowledge. The required
representation can either be provided by a pretrained MPNN, or learned from
scratch using only structural information. Beyond the data-driven design of
molecular fingerprints, the versatility of our approach is demonstrated by
enabling the selection of representative entries in chemical databases, the
automatic construction of coarse-grained force fields, as well as the
identification of reaction coordinates.
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