An explainable machine-learning approach for revealing the complex synthesis path–property relationships of nanomaterials
Kun Jin, Wentao Wang, Guangpei Qi, Xiaohong Peng, Haonan Gao, Hongjiang Zhu, Xin He, Haixia Zou, Lin Yang, Junjie Yuan, Liyuan Zhang, Hong Chen, Xiangmeng Qu
Abstract
prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.
Topics & Concepts
NanomaterialsPath (computing)Property (philosophy)QuantumQuantum yieldNanotechnologyYield (engineering)Phase diagramMaterials scienceComputer scienceFluorescencePhase (matter)ChemistryPhysicsOrganic chemistryEpistemologyProgramming languageMetallurgyPhilosophyQuantum mechanicsMachine Learning in Materials ScienceComputational Drug Discovery MethodsNanocluster Synthesis and Applications