Table_1_Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion.XLSX

Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings.

基于二维材料，如石墨烯的防护涂层在众多应用领域获得了广泛关注。其不可渗透性、惰性、与金属的优异结合能力以及易于功能化的特性，使其成为对抗非生物腐蚀和微生物影响腐蚀（MIC）的潜在涂层。鉴于石墨烯涂层的成功，包括六方氮化硼和二硫化钼在内的整个二维材料家族正在被筛选，以获得其他有潜力的涂层。基于人工智能的数据驱动模型可以加速具有理想物理和化学性质的二维涂层的虚拟筛选。然而，由于缺乏大型实验数据集，分类器的训练变得困难，并常常导致过拟合。生成具有MIC抗性的二维涂层的大型数据集既复杂又费时。深度学习数据增强方法可以通过生成与训练数据类别相似的合成电化学数据来缓解这一问题。在本研究中，我们探讨了两种不同的深度生成模型，即变分自编码器（VAE）和生成对抗网络（GAN），用于生成合成数据以扩展小型实验数据集。我们的模型实验系统包括铜表面的多层石墨烯。使用GAN生成的合成数据在神经网络系统性能（83-85%的准确率）方面优于VAE生成的合成数据（78-80%的准确率）。然而，当使用XGBoost时，VAE数据（90%的准确率）的表现优于GAN数据（84%-85%的准确率）。最终，我们展示了基于VAE和GAN模型的合成数据可以驱动机器学习模型，以开发具有MIC抗性的二维涂层。