Inverse Design of Nanoparticles Using Charge Transfer Properties and Multi-Target Machine Learning

Sichao Li, Amanda S. Barnard*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2021 AIChE Annual Meeting
PublisherAmerican Institute of Chemical Engineers
ISBN (Electronic)9781713852834
Publication statusPublished - 2021
Event2021 AIChE Annual Meeting - Boston, Virtual, United States
Duration: 15 Nov 202119 Nov 2021

Publication series

NameAIChE Annual Meeting, Conference Proceedings
Volumenull

Conference

Conference2021 AIChE Annual Meeting
Country/TerritoryUnited States
CityBoston, Virtual
Period15/11/2119/11/21

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