Larsen Anders S, Rekis Toms, Madsen Anders Ø
Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark.
Science. 2024 Aug 2;385(6708):522-528. doi: 10.1126/science.adn2777. Epub 2024 Aug 1.
X-ray crystallography provides a distinctive view on the three-dimensional structure of crystals. To reconstruct the electron density map, the complex structure factors [Formula: see text] of a sufficiently large number of diffracted reflections must be known. In a conventional experiment, only the amplitudes [Formula: see text] are obtained, and the phases ϕ are lost. This is the crystallographic phase problem. In this work, we show that a neural network, trained on millions of artificial structure data, can solve the phase problem at a resolution of only 2 angstroms, using only 10 to 20% of the data needed for direct methods. The network works in common space groups and for modest unit-cell dimensions and suggests that neural networks could be used to solve the phase problem in the general case for weakly scattering crystals.
X射线晶体学为晶体的三维结构提供了独特的视角。为了重建电子密度图，必须知道足够数量的衍射反射的复杂结构因子[公式：见正文]。在传统实验中，只能获得振幅[公式：见正文]，而相位ϕ丢失了。这就是晶体学相位问题。在这项工作中，我们表明，一个在数百万个人造结构数据上训练的神经网络，仅使用直接法所需数据的10%到20%，就能在仅2埃的分辨率下解决相位问题。该网络适用于常见的空间群和适度的晶胞尺寸，并表明神经网络可用于在一般情况下解决弱散射晶体的相位问题。
