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Leveraging generative neural networks for accurate, diverse, and robust nanoparticle design
Tanzim Rahman1, Ahnaf Tahmid1, Shifat E Arman2
1Department of Electrical and Electronic Engineering, University of Dhaka Dhaka-1000 Bangladesh mahabib@du.ac.bd.
Nanoscale Advances
|December 11, 2024
Summary
Conditional variational autoencoders (cVAE) enable diverse predictions for nanoparticle inverse design. This generative neural network approach offers higher accuracy and robustness compared to tandem models for core-shell nanoparticle synthesis.
Area of Science:
- Materials Science
- Computational Chemistry
- Nanotechnology
Background:
- Traditional inverse design methods using tandem neural networks are limited to single structure predictions, hindering the exploration of diverse nanoparticle designs.
- The synthesis of plasmonic core-shell nanoparticles requires accurate prediction of material composition and spectral properties.
Purpose of the Study:
- To introduce and evaluate a conditional variational autoencoder (cVAE) for the inverse design of core-shell nanoparticles.
- To demonstrate the cVAE's capability in generating multiple valid structural solutions for a given input condition.
- To compare the performance of cVAE against existing tandem models in terms of accuracy, diversity, and robustness.
Main Methods:
- A dataset was generated using Mie theory simulations for ten common materials used in plasmonic core-shell nanoparticle synthesis.
- A conditional variational autoencoder (cVAE) was developed and trained on the simulated dataset.
- The performance of the cVAE model was benchmarked against a tandem neural network model.
- Robustness was assessed using 100 test spectra.
- Experimental synthesis of Au@Ag core-shell nanoparticles was performed to validate the model's predictions.
Main Results:
- The cVAE model achieved higher accuracy, with a mean absolute error (MAE) of 0.013, significantly lower than the tandem model's MAE of 0.046.
- Robustness analysis confirmed the improved reliability and diversity of predictions generated by the cVAE.
- The cVAE model accurately predicted material composition and spectral features, validated by the successful synthesis of Au@Ag core-shell nanoparticles.
Conclusions:
- Conditional variational autoencoders (cVAEs) represent a powerful generative neural network approach for nanoparticle inverse design.
- cVAEs overcome the limitations of single-prediction models, offering accurate, diverse, and robust solutions for designing core-shell nanoparticles.
- This study highlights the potential of cVAEs in accelerating materials discovery and optimizing nanoparticle synthesis for plasmonic applications.
