Universal neural network potentials as descriptors: towards scalable chemical property prediction using quantum and classical computers
Tomoya Shiota, Kenji Ishihara, Wataru Mizukami
Abstract
Using outputs from a pre-trained universal neural network potential's graph layer as descriptors enables efficient and accurate predictions of molecular properties. These descriptors are compact yet perform as well as the best current descriptors.
Topics & Concepts
ScalabilityArtificial neural networkComputer scienceProperty (philosophy)GraphArtificial intelligenceQuantum chemicalTheoretical computer sciencePattern recognition (psychology)Machine learningQuantum mechanicsPhysicsPhilosophyDatabaseMoleculeEpistemologyMachine Learning in Materials ScienceComputational Drug Discovery MethodsProtein Structure and Dynamics