论文标题
具有原子力显微镜和条件生成对抗网络的分子鉴定
Molecular Identification with Atomic Force Microscopy and Conditional Generative Adversarial Networks
论文作者
论文摘要
频率调制（FM）原子力显微镜（AFM），具有在尖端顶点的CO分子功能化的金属尖端，已为具有完全前所未有的分辨率的分子的内部结构提供了访问。我们提出了一个模型，以从这些AFM图像中提取化学信息，以实现成像分子的完整鉴定。我们的条件生成对抗网络（CGAN）将一堆在各种尖端样本距离的AFM图像转换为球形粘结描绘，其中不同颜色和大小的球代表化学物种和棍棒代表键，提供了有关结构和化学组成的完整信息。 CGAN已通过Quam-AFM数据集进行了训练和测试，其中包含模拟的AFM图像，以收集686,000个分子，其中包括所有与有机化学相关的化学物种。具有大量理论图像和几乎没有实验示例的测试证明了我们的分子鉴定方法的准确性和潜力。
Frequency modulation (FM) Atomic Force Microscopy (AFM) with metal tips functionalized with a CO molecule at the tip apex has provided access to the internal structure of molecules with totally unprecedented resolution. We propose a model to extract the chemical information from those AFM images in order to achieve a complete identification of the imaged molecule. Our Conditional Generative Adversarial Network (CGAN) converts a stack of AFM images at various tip-sample distances into a ball-and-stick depiction, where balls of different color and size represent the chemical species and sticks represent the bonds, providing complete information on the structure and chemical composition. The CGAN has been trained and tested with the QUAM-AFM data set, that contains simulated AFM images for a collection of 686,000 molecules that include all the chemical species relevant in organic chemistry. Tests with a large set of theoretical images and few experimental examples demonstrate the accuracy and potential of our approach for molecular identification.
