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Found 3 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.
Language Models as Recommender Systems: Evaluations and Limitations
Pre-trained language models (PLMs) such as BERT and GPT learn general text representations and encode extensive world knowledge; thus, they can be efficiently and accurately adapted to various downstream tasks.
Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules.
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