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Found 2 papers, 2 papers with code
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
In recent years, neural network (NN)-based potentials trained on quantum mechanical (DFT-labeled) data have emerged as a more accurate alternative to conventional EIPs.
BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules
Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge.
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