Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers

Accurate prediction of diverse chemical properties is crucial for advancing molecular design and materials discovery. Here we present a versatile approach that uses the intermediate information of a universal neural network potential as a general-purpose descriptor for chemical property prediction. Our method is based on the insight that by training a sophisticated neural network architecture for universal force fields, it learns transferable representations of atomic environments. We show that transfer learning with a graph neural network potential M3GNet achieves accuracy comparable to state-of-the-art methods for predicting the NMR chemical shifts of¹H, ¹³C, ¹⁵N, ¹⁷O, and ¹⁹F using quantum machine learning as well as a standard classical regression model, despite the compactness of its descriptors. This work provides an efficient way to accurately predict properties, potentially accelerating the discovery of new molecules and materials.