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This paper extends the surrogate functional framework from orbital-free DFT to molecular property prediction, using ground-state electron density as input. By enforcing the density as the minimum of the surrogate functional, the model achieves state-of-the-art performance on QM9 extensive properties and improves generalization to QM40. This approach significantly outperforms density-informed models that lack the surrogate functional constraint.
Ground-state electron density, when constrained by a surrogate functional, unlocks superior molecular property prediction and generalization beyond current state-of-the-art.
Machine learning methods are increasingly used to predict chemical properties directly from the molecular structure to bypass the often prohibitive cost of quantum mechanical calculations. Recent work has shown that including the ground-state electron density as input to the machine learning model can improve accuracy in this setting. This work employs the recent surrogate functional framework originally developed for orbital-free density functional theory and extends it to predicting molecular properties. We show that passing the ground-state density as input to the model, while simultaneously enforcing it to be the minimum of the surrogate functional, surpasses state-of-the-art performance for six out of seven extensive properties of the QM9 dataset. Additionally, the approach shows improved generalization to larger molecules taken from the QM40 dataset. On all extensive properties of both datasets, our approach outperforms models that are informed by the ground-state density but do not enforce the surrogate functional constraints.