OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Understand

We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations.

• The model is a message-passing neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule.
• OrbNet Denali is trained on a vast dataset of 2.3 million DFT calculations on molecules and geometries.
• This dataset covers the most common elements in bio- and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, I) as well as charged molecules.