Rahman Tanzim, Tahmid Ahnaf, Arman Shifat E, Ahmed Tanvir, Rakhy Zarin Tasnim, Das Harinarayan, Rahman Mahmudur, Azad Abul Kalam, Wahadoszamen Md, Habib Ahsan
Department of Electrical and Electronic Engineering, University of Dhaka Dhaka-1000 Bangladesh
Department of Robotics and Mechatronics Engineering, University of Dhaka Dhaka-1000 Bangladesh.
Nanoscale Adv. 2024 Dec 9;7(2):634-642. doi: 10.1039/d4na00859f. eCollection 2025 Jan 14.
Tandem neural networks for inverse design can only make single predictions, which limits the diversity of predicted structures. Here, we use conditional variational autoencoder (cVAE) for the inverse design of core-shell nanoparticles. cVAE is a type of generative neural network that generates multiple valid solutions for the same input condition. We generate a dataset from Mie theory simulations, including ten commonly used materials in plasmonic core-shell nanoparticle synthesis. We compare the performance of cVAE with that of the tandem model. Our cVAE model shows higher accuracy with a lower mean absolute error (MAE) of 0.013 compared to 0.046 for the tandem model. Robustness analysis with 100 test spectra confirms the improved reliability and diversity of cVAE. To validate the effectiveness of the cVAE model, we synthesize Au@Ag core-shell nanoparticles. cVAE model offers high accuracy in predicting material composition and spectral features. Our study shows the potential of cVAEs as generative neural networks in producing accurate, diverse, and robust nanoparticle designs.
用于逆向设计的串联神经网络只能进行单一预测，这限制了预测结构的多样性。在这里，我们使用条件变分自编码器（cVAE）进行核壳纳米粒子的逆向设计。cVAE是一种生成神经网络，它能针对相同的输入条件生成多个有效解决方案。我们从米氏理论模拟中生成了一个数据集，包括等离子体核壳纳米粒子合成中十种常用材料。我们将cVAE的性能与串联模型的性能进行了比较。我们的cVAE模型显示出更高的准确性，平均绝对误差（MAE）为0.013，而串联模型为0.046。对100个测试光谱进行的稳健性分析证实了cVAE的可靠性和多样性得到了提高。为了验证cVAE模型的有效性，我们合成了Au@Ag核壳纳米粒子。cVAE模型在预测材料组成和光谱特征方面具有很高的准确性。我们的研究表明，cVAE作为生成神经网络在生成准确、多样和稳健的纳米粒子设计方面具有潜力。
