Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning
Guzhong Chen, Zhen Song, Zhiwen Qi, Kai Sundmacher
Abstract
We are introducing ILTransR, a transfer learning based one-stop framework to predict ionic liquid (IL) properties. High accuracy can be achieved by pre-training the model on millions of unlabeled data and fine-tuning on limited labeled data.
Topics & Concepts
Ionic liquidTransfer of learningProperty (philosophy)Computer scienceTransfer (computing)Training setArtificial intelligenceMachine learningBiological systemChemistryOrganic chemistryParallel computingCatalysisEpistemologyPhilosophyBiologyIonic liquids properties and applicationsElectrochemical Analysis and ApplicationsMachine Learning in Materials Science