Predicting glass transition temperature and melting point of organic compounds <i>via</i> machine learning and molecular embeddings
Tommaso Galeazzo, Manabu Shiraiwa
Abstract
We developed tgBoost a machine learning model to predict glass transition temperature ( T g) of organic species considering their molecular structure and functionality for better predictions of the phase state of secondary organic aerosols.
Topics & Concepts
Melting pointGlass transitionPhase transitionMelting temperatureTransition (genetics)Point (geometry)ThermodynamicsMaterials scienceChemical physicsStatistical physicsComputer scienceChemistryPhysicsMathematicsComposite materialPolymerGeometryGeneBiochemistryAtmospheric chemistry and aerosolsGlass properties and applicationsOptical properties and cooling technologies in crystalline materials