@inproceedings{1c674cb8511640099444c8ac92d8589b,
title = "Inverse Design of Nanoparticles Using Charge Transfer Properties and Multi-Target Machine Learning",
abstract = "We present a new approach to inverse design that uses sets of properties to predict a unique nanoparticle structure, and is based on established multi-target regression and reliable forward structure/property prediction. Feature selection is used 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 optimisation or high-throughput sampling.",
author = "Sichao Li and Barnard, {Amanda S.}",
note = "Publisher Copyright: {\textcopyright} 2021 American Institute of Chemical Engineers. All rights reserved.; 2021 AIChE Annual Meeting ; Conference date: 15-11-2021 Through 19-11-2021",
year = "2021",
language = "English",
series = "AIChE Annual Meeting, Conference Proceedings",
publisher = "American Institute of Chemical Engineers",
booktitle = "2021 AIChE Annual Meeting",
address = "United States",
}