TSNet: predicting transition state structures with tensor field networks and transfer learning
Riley Jackson, Wenyuan Zhang, Jason K. Pearson
Abstract
2 reactions which includes transition state structures - the first of its kind built specifically for machine learning. Finally, transfer learning, a low data remedial technique, is explored to understand the viability of pretraining TSNet on widely available chemical data may provide better starting points during training, faster convergence, and lower loss values. Aspects of the new dataset and model shall be discussed in detail, along with motivations and general outlook on the future of machine learning-based transition state prediction.
Topics & Concepts
Variety (cybernetics)Field (mathematics)Transition stateChemical physicsTransition (genetics)Tensor (intrinsic definition)State (computer science)KineticsFunction (biology)Statistical physicsChemistryCatalysisComputational chemistryMaterials scienceComputer sciencePhysicsMathematicsArtificial intelligenceOrganic chemistryQuantum mechanicsPure mathematicsAlgorithmBiochemistryGeneBiologyEvolutionary biologyMachine Learning in Materials ScienceComputational Drug Discovery MethodsProtein Structure and Dynamics