TY - JOUR

T1 - ClusterFinder

T2 - a fast tool to find cluster structures from pair distribution function data

AU - Anker, Andy S.

AU - Friis-jensen, Ulrik

AU - Johansen, Frederik L.

AU - Billinge, Simon J. L

AU - Jensen, Kirsten M. Ø.

PY - 2024

Y1 - 2024

N2 - 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 104 to 105 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.

AB - 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 104 to 105 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.

U2 - 10.1107/S2053273324001116

DO - 10.1107/S2053273324001116

M3 - Journal article

C2 - 38420993

VL - 80

SP - 213

EP - 220

JO - Acta Crystallographica Section A Foundations and Advances

JF - Acta Crystallographica Section A Foundations and Advances

SN - 2053-2733

IS - 2

ER -