coremof-tools 0.1.1

Python API for CoRE MOF 2024 DB

Installation

This API includes tools developed to collect, curate, and classify Computation-Ready, Experimental MOF database.
You need to install the CSD software and python API before downloading the full CoRE MOF database.
For using CoREMOF.calculation.Zeopp, you need to input conda install -c conda-forge zeopp-lsmo to install Zeo++.

Examples

Available at Github and CoRE MOF Website to view examples.

Citation

• CoRE MOF: Zhao G, Brabson L, Chheda S, Huang J, Kim H, Liu K, et al. CoRE MOF DB: a curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-nvmnr.
• Zeo++: T.F. Willems, C.H. Rycroft, M. Kazi, J.C. Meza, and M. Haranczyk, Algorithms and tools for high-throughput geometry- based analysis of crystalline porous materials, Microporous and Mesoporous Materials, 149 (2012) 134-141.
• Heat capacity: Models from Moosavi, S.M., Novotny, B.A., Ongari, D. et al.A data-science approach to predict the heat capacity of nanoporous materials. Nat. Mater. 21 (2022), 1419-1425.
• Water stability: Terrones G G, Huang S P, Rivera M P, et al. Metal-organic framework stability in water and harsh environments from data-driven models trained on the diverse WS24 data set. Journal of the American Chemical Society, 146 (2024), 20333-20348.
• Activation and thermal stability: Nandy A, Duan C, Kulik H J. Using machine learning and data mining to leverage community knowledge for the engineering of stable metal-organic frameworks. Journal of the American Chemical Society, 143 (2021): 17535-17547.
• MOFid-v1: Bucior B J, Rosen A S, Haranczyk M, et al. Identification schemes for metal-organic frameworks to enable rapid search and cheminformatics analysis. Crystal Growth & Design, 19 (2019), 6682-6697.
• PACMAN-charge: Zhao G, Chung Y G. PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks. Journal of Chemical Theory and Computation, 20(2024), 5368-5380.
• Revised Autocorrelation: Jon Paul Janet and Heather J. Kulik. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships. The Journal of Physical Chemistry A. 121 (2017), 8939-8954.
• Topology: Zoubritzky L, Coudert F X. CrystalNets. jl: identification of crystal topologies. SciPost Chemistry, 1 (2022), 005.