Liu Xinyao, Amini Kasra, Sanchez Aurelien, Belsa Blanca, Steinle Tobias, Biegert Jens
ICFO - Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860, Castelldefels, Barcelona, Spain.
ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain.
Commun Chem. 2021 Nov 9;4(1):154. doi: 10.1038/s42004-021-00594-z.
Ultrafast diffraction imaging is a powerful tool to retrieve the geometric structure of gas-phase molecules with combined picometre spatial and attosecond temporal resolution. However, structural retrieval becomes progressively difficult with increasing structural complexity, given that a global extremum must be found in a multi-dimensional solution space. Worse, pre-calculating many thousands of molecular configurations for all orientations becomes simply intractable. As a remedy, here, we propose a machine learning algorithm with a convolutional neural network which can be trained with a limited set of molecular configurations. We demonstrate structural retrieval of a complex and large molecule, Fenchone (CHO), from laser-induced electron diffraction (LIED) data without fitting algorithms or ab initio calculations. Retrieval of such a large molecular structure is not possible with other variants of LIED or ultrafast electron diffraction. Combining electron diffraction with machine learning presents new opportunities to image complex and larger molecules in static and time-resolved studies.
超快衍射成像是一种强大的工具，可用于以皮米级空间分辨率和阿秒级时间分辨率相结合的方式获取气相分子的几何结构。然而，随着结构复杂性的增加，结构检索变得越来越困难，因为必须在多维解空间中找到全局极值。更糟糕的是，预先计算数千种分子在所有取向下的构型变得根本难以处理。作为一种补救措施，我们在此提出一种带有卷积神经网络的机器学习算法，该算法可以用有限的一组分子构型进行训练。我们展示了从激光诱导电子衍射（LIED）数据中检索复杂大分子葑酮（CHO）的结构，无需拟合算法或从头计算。使用LIED的其他变体或超快电子衍射无法检索如此大的分子结构。将电子衍射与机器学习相结合为在静态和时间分辨研究中对复杂大分子成像提供了新机会。
