Jason Meziere, Abigail Hardy Carpenter, Anastasios Pateras, Ross Harder, Richard L. Sandberg
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引用次数: 0
摘要
来自同步加速器等加速器源的相干 X 射线成像和散射将继续影响生物学、医学、技术和材料科学。目前,全球许多同步加速器正在进行重大升级,以将其可用的相干 X 射线通量提高约两个数量级。同步加速器的改进可实现原子尺度的材料成像,从而彻底改变电池和催化技术。相干 X 射线成像中目前使用的相位检索算法基于投影到集合的方法。这些传统的迭代相位检索方法随着原子分辨率的提高,计算成本会越来越高,而且可能难以收敛。此外,这些方法没有结合物理信息,而物理信息可能会额外限制求解。在这项工作中,我们提出了一种将分子动力学纳入布拉格相干衍射成像(BCDI)的算法。我们称这种算法为 PRAMMol(原子建模与分子动力学相位检索),它将统计技术与分子动力学相结合,解决了相位检索问题。我们举了几个例子,将我们的算法应用于三维晶体的模拟相干衍射,结果显示在原子尺度上收敛到了正确的解决方案。
Atomic resolution coherent x-ray imaging with physics-based phase retrieval
Coherent x-ray imaging and scattering from accelerator based sources such as synchrotrons continue to impact biology, medicine, technology, and materials science. Many synchrotrons around the world are currently undergoing major upgrades to increase their available coherent x-ray flux by approximately two orders of magnitude. The improvement of synchrotrons may enable imaging of materials in operando at the atomic scale which may revolutionize battery and catalysis technologies. Current algorithms used for phase retrieval in coherent x-ray imaging are based on the projection onto sets method. These traditional iterative phase retrieval methods will become more computationally expensive as they push towards atomic resolution and may struggle to converge. Additionally, these methods do not incorporate physical information that may additionally constrain the solution. In this work, we present an algorithm which incorporates molecular dynamics into Bragg coherent diffraction imaging (BCDI). This algorithm, which we call PRAMMol (Phase Retrieval with Atomic Modeling and Molecular Dynamics) combines statistical techniques with molecular dynamics to solve the phase retrieval problem. We present several examples where our algorithm is applied to simulated coherent diffraction from 3D crystals and show convergence to the correct solution at the atomic scale.
期刊介绍:
npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings.
Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.