THICK2D

THICK2D数据集是由理海大学物理系的C.E. Ekuma开发的，专注于2D材料的厚度预测。该数据集包含超过8000种2D材料的厚度信息，这些数据通过机器学习和大型语言模型自动生成。数据集的创建过程涉及使用结晶数据，并通过数据增强和噪声引入来扩展数据集。THICK2D数据集的应用领域广泛，包括纳米电子学、光电子学和能源存储，旨在解决2D材料厚度预测的复杂问题，提供准确和可靠的厚度预测方法。

THICK2D dataset was developed by C.E. Ekuma from the Department of Physics, Lehigh University, focusing on thickness prediction of 2D materials. This dataset contains thickness information of over 8000 types of 2D materials, which were automatically generated via machine learning and large language models (LLMs). The dataset creation process involves leveraging crystallographic data and expanding the dataset through data augmentation and noise injection. The THICK2D dataset has broad application domains including nanoelectronics, optoelectronics and energy storage, aiming to address the complex problem of 2D material thickness prediction and provide accurate and reliable thickness prediction methods.