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arXiv Machine Learning By Manuel V. Klockow, Simon Wagner, Roman Remme, Dominic Plein, Fred A. Hamprecht

Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals

Read the original on arXiv Machine Learning →

The paper introduces a machine‑learning approach that incorporates the ground‑state electron density as input while enforcing it to be the minimum of a surrogate functional derived from orbital‑free density functional theory. This method outperforms state‑of‑the‑art models on six of seven extensive properties in the QM9 dataset and shows better generalization to larger molecules in the QM40 dataset. Models that use the density without enforcing the surrogate functional constraints perform worse on all extensive properties of both datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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