论文标题
具有深度学习的共同功能化尖端的原子力显微镜模拟
Atomic Force Microscopy Simulations for CO-functionalized tips with Deep Learning
论文作者
论文摘要
在频率调制模式下运行的原子力显微镜（AFM）用CO分子图像功能化的金属尖端具有前所未有的分辨率分子的内部结构。这些图像的解释通常很困难，因此支持理论模拟很重要。当前的仿真方法，尤其是最准确的方法，需要专业知识和资源来对必要输入（即分子的电荷密度和静电电位）进行较低的计算。在这里，我们提出了一种有效而简单的替代方法，以基于条件生成的对抗网络（CGAN）模拟这些AFM图像，该图像避免了所有力量计算，并用作唯一的对分子的输入的输入。使用不同的培训子集优化模型的性能。我们的CGAN准确地重现了准平面分子的模拟图像中观察到的分子内对比度，但由于输入的严格2D特征，具有显着内部扭转的分子具有显着的局限性。
Atomic Force Microscopy (AFM) operating in the frequency modulation mode with a metal tip functionalized with a CO molecule images the internal structure of molecules with an unprecedented resolution. The interpretation of these images is often difficult, making the support of theoretical simulations important. Current simulation methods, particularly the most accurate ones, require expertise and resources to perform ab initio calculations for the necessary inputs (i.e charge density and electrostatic potential of the molecule). Here, we propose an efficient and simple alternative to simulate these AFM images based on a Conditional Generative Adversarial Network (CGAN), that avoids all force calculations, and uses as the only input a 2D ball--and--stick depiction of the molecule. We discuss the performance of the model when optimized using different training subsets. Our CGAN reproduces accurately the intramolecular contrast observed in the simulated images for quasi--planar molecules, but has significant limitations for molecules with a significant internal torsion, due to the strictly 2D character of the input.
