Repurposing Density Functional Theory to Suit Deep Learning

Density Functional Theory (DFT) accurately pre- dicts the properties of molecules given their atom types and positions, and often serves as ground truth for molecular property prediction tasks. Neu- ral Networks (NN) are popular tools for such tasks and are trained on DFT datasets, with the aim to approximate DFT at a fraction of the com- putational cost. Research in other areas of ma- chine learning has shown that generalisation per- formance of NNs tends to improve with increased dataset size, however, the computational cost of DFT limits the size of DFT datasets. We present PySCFIPU, a DFT library that allows us to iterate on both dataset generation and NN training. We create QM10X, a dataset with 108 conformers, in 13 hours, on which we subsequently train SchNet in 12 hours. We show that the predictions of SchNet improve solely by increasing training data without incorporating further inductive biases.