Inverse Design of Nanoparticles Using Multi-Target Machine Learning

被引:19
作者
Li, Sichao [1 ]
Barnard, Amanda S. [1 ]
机构
[1] Australian Natl Univ, Sch Comp, Acton, ACT 2601, Australia
关键词
inverse design; machine learning; nanoparticles; ABSOLUTE ERROR MAE; STRUCTURE/PROPERTY RELATIONSHIPS; CLASSIFICATION; REGRESSION; MODELS; RMSE;
D O I
10.1002/adts.202100414
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:12
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