Neural Network-based Orientational Monte-Carlo of MOF-5-OH, SNU-70 and UiO66(Zr)-NH2 -- Python Routines and Data

Python routines and Orientational Monte-Carlo Data to investigate orientational isomerism in the metal-organic framework materials MOF-5-OH, SNU-70 and UiO66(Zr)-NH2 associated to the publication in the Journal of Computational Chemistry: "Tackling Orientational Isomerism in Metal-Organic Frameworks Comprising Low-Symmetry Linker Molecules via Simulated Annealing Featuring a Neural Network Potential"; Benedikt E. Hörfarter, Stefan Seiwald, Clemens Hofstötter, Armin Penz, Josef M. Gallmetzer, Thomas S. Hofer; Journal of Computational Chemistry 2026 47(9) e70349 DOI: 10.1002/jcc.70349 This data includes: Simulated annealing orientational Monte-Carlo python routines utilizing the MACE-MP0a neural network potential for MOF-5-OH, SNU-70 and UiO66(Zr)-NH2 A list of required python packages to execute the simulations Input structures for MOF-5-OH, SNU-70 and UiO66(Zr)-NH2 Simulation results for MOF-5-OH, SNU-70 and UiO66(Zr)-NH2 Images found in the publication This research was funded in whole or in part by the Austrian Science Fund (FWF) grant DOI . For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. The computational results presented have been achieved (in part) using the HPC infrastructure LEO of the University of Innsbruck.