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Abstract Machine learned potentials based on artificial neural networks are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees of freedom at a coarse-grained resolution.
1. Introduction Click to copy section linkSection link copied! Methods for modeling atomistic systems range between computationally cheap but less precise (e.g., classical force fields), to computationally expensive but more precise [e.g., first-principles calculations based on density functional theory (DFT), coupled-cluster method with single, double and triple excitations (CCSD(T)), or quantum Monte Carlo techniques (1,2)].
Automatic Identification of Chemical Moieties 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.
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