Anker Andy S, Friis-Jensen Ulrik, Johansen Frederik L, Billinge Simon J L, Jensen Kirsten M Ø
Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
Department of Applied Physics and Applied Mathematics Science, Columbia University, New York, NY 10027, USA.
Acta Crystallogr A Found Adv. 2024 Mar 1;80(Pt 2):213-220. doi: 10.1107/S2053273324001116. Epub 2024 Feb 29.
A novel automated high-throughput screening approach, ClusterFinder, is reported for finding candidate structures for atomic pair distribution function (PDF) structural refinements. Finding starting models for PDF refinements is notoriously difficult when the PDF originates from nanoclusters or small nanoparticles. The reported ClusterFinder algorithm can screen 10 to 10 candidate structures from structural databases such as the Inorganic Crystal Structure Database (ICSD) in minutes, using the crystal structures as templates in which it looks for atomic clusters that result in a PDF similar to the target measured PDF. The algorithm returns a rank-ordered list of clusters for further assessment by the user. The algorithm has performed well for simulated and measured PDFs of metal-oxido clusters such as Keggin clusters. This is therefore a powerful approach to finding structural cluster candidates in a modelling campaign for PDFs of nanoparticles and nanoclusters.
本文报道了一种新型的自动化高通量筛选方法——ClusterFinder，用于寻找原子对分布函数（PDF）结构精修的候选结构。当PDF源自纳米团簇或小纳米颗粒时，为PDF精修找到起始模型是出了名的困难。所报道的ClusterFinder算法能够在数分钟内从诸如无机晶体结构数据库（ICSD）等结构数据库中筛选出10到10个候选结构，它将晶体结构用作模板，在其中寻找能产生与目标测量PDF相似的PDF的原子团簇。该算法会返回一个按排名顺序排列的团簇列表，供用户进一步评估。对于金属氧化物团簇（如凯吉恩团簇）的模拟和测量PDF，该算法表现良好。因此，这是在纳米颗粒和纳米团簇PDF建模活动中寻找结构团簇候选物的一种强大方法。
