Physical machine learning outperforms "human learning" in Quantum Chemistry

Understand

Two types of approaches to modeling molecular systems have demonstrated high practical efficiency.

• Density functional theory (DFT), the most widely used quantum chemical method, is a physical approach predicting energies and electron densities of molecules.
• Recently, numerous papers on machine learning (ML) of molecular properties have also been published.
• ML models greatly outperform DFT in terms of computational costs, and may even reach comparable accuracy, but they are missing physicality - a direct link to Quantum Physics - which limits their applicability.