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Domain-informed graph neural networks: a quantum chemistry case study
We explore different strategies to integrate prior domain knowledge into the design of a deep neural network (DNN).
VIMPNN: A physics informed neural network for estimating potential energies of out-of-equilibrium systems
Our method is extensively evaluated on a augmented version of the QM9 dataset that includes unstable molecules, as well as a new dataset of infinite- and finite-size crystals, and is compared with the Message Passing Neural Network (MPNN).
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