Abstract:<p></p><p>The development of
efficient models for predicting specific properties through machine learning is
of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like frontier
molecular orbital HOMO and LUMO energy levels and their HOMO-LUMO gaps from the
small-sized molecule data to larger molecules remains a challenge. Here we develop a multi-level attention neural
network, named DeepMoleNet, to enable chemical
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