Inverse Design of Nanoparticles Using Multi?Target Machine Learning

In this study a new approach to inverse design is presented that draws on the multi-functionality of nanomaterials and uses sets properties predict unique nanoparticle structure. This involves multi-target regression precursory forward structure/property prediction focus model most important characteristics before inverting problem simultaneously predicting multiple structural features single nanoparticle. The workflow general, as demonstrated two data sets, can rapidly property/structure relationships guide further research development without need for additional optimization or high-throughput sampling.