Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films
Siyu Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee‐Fun Lim, Armin G. Aberle, Benjamin P. MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
Abstract
thicknessML predicts film thickness from reflection and transmission spectra. Transfer learning enables thickness prediction of different materials with good performance. Transfer learning also bridges the gap between simulation and experiment.
Topics & Concepts
Reflection (computer programming)Transfer of learningCharacterization (materials science)Perovskite (structure)Spectral lineData transmissionMaterials scienceScarcityThin filmTransfer (computing)Transmission (telecommunications)Computer scienceOpticsChemical engineeringNanotechnologyArtificial intelligencePhysicsEngineeringTelecommunicationsEconomicsAstronomyMicroeconomicsParallel computingProgramming languageComputer networkMachine Learning in Materials SciencePerovskite Materials and ApplicationsElectronic and Structural Properties of Oxides