coremof-tools 0.3.1

Python API for CoRE MOF DB

Develop by Guobin Zhao

Installation

This API includes tools developed to collect, curate, and classify Computation-Ready, Experimental MOF database.
a. You need to install the CSD software and python API before downloading the full CoRE MOF database.
b. For using CoREMOF.calculation.Zeopp, you need to input conda install -c conda-forge zeopp-lsmo to install Zeo++.
c. For using CoREMOF.get_mofid, you need to install MOFid following the manual.
d. For using CoREMOF.mof_check, you need to install MOFChecker by input pip install git+https://github.com/sxm13/mofchecker_2.0.git@main.

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. Matter, 8 (2025), 102140.
• 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.
• Chen_Manz: Chen T, Manz T.A. Identifying misbonded atoms in the 2019 CoRE metal–organic framework database. RSC Adv, 10 (2025), 26944-26951.
• MOFChecker: JIN X, Jablonka K, Moubarak E, Li Y, Smit B. MOFChecker: An algorithm for Validating and Correcting Metal-Organic Framework (MOF) Structures. Digital Discovery, 4 (2025), 1560-1569.
• MOSAEC: White A, Gibaldi M, Burner J, Mayo RA, Woo T. High Structural Error Rates in "Computation-Ready" MOF Databases Discovered by Checking Metal Oxidation States. JACS, 147 (2025), 17579-17583.
• MOFClassifier: Zhao G, Zhao P, Chung Y. G. MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks. JACS, 147 (2025), 33343-33349.