Inverse Design of Nanoparticles Using Multi‐Target Machine Learning

Abstract

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

Topics & Concepts