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 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.

Automated summary

This study introduces a new approach to inverse design that utilizes the multi-functionality of nanomaterials to predict unique nanoparticle structures through multi-target regression. The workflow is general, can rapidly predict property/structure relationships, and guide further research and development without the need for additional optimization or high-throughput sampling.