Optimizing the performance of phase-change azobenzene: from trial and error to machine learning
Kai Wang, Huitao Yu, Jing-Li Gao, Yiyu Feng, Wei Feng
Abstract
Machine learning can predict the properties of phase change azobenzene derivatives and guide molecular design to further improve their photothermal conversion performance.
Topics & Concepts
AzobenzeneMaterials sciencePhotothermal therapyPhase (matter)Phase changeNanotechnologyEngineering physicsPolymerComposite materialOrganic chemistryEngineeringChemistryPhotochromic and Fluorescence ChemistryMachine Learning in Materials ScienceAdvanced Memory and Neural Computing