Inverse Design of Nanoparticles Using Multi‐Target Machine Learning. Issue 2 (7th December 2021)

• Record Type: Journal Article
• Authors: Li, Sichao; Barnard, Amanda S.
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-07
• Subjects: inverse design -- machine learning -- nanoparticles; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100414
• Languages: English
• ISSNs: 2513-0390

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. Abstract : Simultaneously predicting structural characteristics of nanoparticles based on a desired set of functional properties with multi‐target regression can accelerate the design of new nanomaterials.