The launch of a freely accessible MOF CIF collection from the CSD

Li, Aurelia; Perez, Rocio Bueno; Wiggin, Seth B.; Ward, Suzanna C.; Wood, Peter A.; Fairen‐Jimenez, David

doi:10.1016/j.matt.2021.03.006

Cited by 23 publications

(26 citation statements)

References 5 publications

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“…As listed in Table , both experimental and hypothetical MOF databases are considered in this study. Two experimental databases, CoRE MOF 2019 and CSD MOF, have been curated to be computation-ready. For C 3 H 8 -selective C 3 separation, these two databases were further curated by three steps (Figure S1).…”

Section: Methodsmentioning

confidence: 99%

“…③ The accuracy of ML models is evaluated and their interpretability is analyzed. ④ The ML models are used to rapidly screen other MOF data sets (e.g., experimental Cambridge Structural Database (CSD) MOFs 25 and hypothetical MOFs 13 and CSD MOF, 25 have been curated to be computation-ready. For C 3 H 8 -selective C 3 separation, these two databases were further curated by three steps (Figure S1 3) Geometric analysis: MOFs with a porelimiting diameter (PLD) < 3.8 Å were discarded as being unlikely accessible to C 3 H 8 and C 3 H 6 .…”

Section: Introductionmentioning

confidence: 99%

“…③ The accuracy of ML models is evaluated and their interpretability is analyzed. ④ The ML models are used to rapidly screen other MOF data sets (e.g., experimental Cambridge Structural Database (CSD) MOFs and hypothetical MOFs) for C 3 H 8 -selective C 3 separation, and the best MOFs are identified. The technical details are elaborated below.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

Tang

Wang

et al. 2021

ACS Appl. Mater. Interfaces

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At present, 100 000+ metal–organic frameworks (MOFs) have been synthesized, and it is challenging to identity the best candidate for a specific application. In this study, MOFs are rapidly screened via a hierarchical approach for propane/propylene (C3H8/C3H6) separation. First, the adsorption capacity and selectivity of C3H8/C3H6 mixture in “Computation-Ready, Experimental” (CoRE) MOFs are predicted via a molecular simulation (MS) method. The relationships between separation metrics and structural factors are established, and top-performing CoRE MOFs are identified. Then, machine learning (ML) models are trained and developed upon the CoRE MOFs using pore size, pore geometry, and framework chemistry as feature descriptors. By introducing binned pore size distributions and geometric descriptors, the accuracy of ML models is substantially improved. The feature importance of the descriptors is physically interpreted by the Gini impurities and Shapley Additive Explanations. Subsequently, the ML models are used to rapidly screen experimental “Cambridge Structural Database” (CSD) MOFs and hypothetical MOFs for C3H8/C3H6 separation. In the CSD MOFs, the out-of-sample predictions are found to agree well with simulation results, demonstrating the excellent transferability of the ML models from the CoRE to CSD MOFs. Moreover, nine CSD MOFs are identified to possess separation performance superior to top-performing CoRE MOFs. Finally, the similarity and diversity among experimental and hypothetical MOFs are visualized and compared by the t-Distributed Stochastic Neighbor Embedding (t-SNE) feature projections. Remarkably, the CoRE and CSD MOFs are revealed to share a close similarity in both chemical and geometric feature spaces. By synergizing MS and ML, the hierarchical approach developed in this study would advance the rapid screening of MOFs across different databases toward industrially important separation processes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…③ The accuracy of ML models is evaluated and their interpretability is analyzed. ④ The ML models are used to rapidly screen other MOF data sets (e.g., experimental Cambridge Structural Database (CSD) MOFs and hypothetical MOFs) for C 3 H 8 -selective C 3 separation, and the best MOFs are identified. The technical details are elaborated below.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

Tang

Wang

et al. 2021

ACS Appl. Mater. Interfaces

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“…[1,10] In March 2021, the Cambridge Crystallographic Data Centre (CDCC) announced the existence of more than 100,000 registered entries under the MOF subset. [11] The highly flexible MIL-88B framework has attracted numerous research groups' attention for its distinctive capability of expressing crystal transformation depending on the surrounding environment. MIL-88 refers to the formation of a 3D structure, whereby the trigonal prismatic metal cluster is formed from three trivalent metal centers and is connected to a ditopic linker, preserving the same parent topology.…”

Section: Introductionmentioning

confidence: 99%

Microwave-Assisted Synthesis of the Flexible Iron-Based MIL-88B Metal-Organic Framework for Advanced Energetic Systems

Zorainy

Kaliaguine

Gobara

et al. 2021

Preprint

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The 3D metal-organic framework (MOF), MIL-88B, built from the trivalent metal ions and the ditopic 1,4-Benzene dicarboxylic acid linker (H2BDC), distinguishes itself from the other MOFs for its flexibility and high thermal stability. MIL-88B was synthesized by a rapid microwave-assisted solvothermal method at high power (850 W). The iron-based MIL-88B [Fe3.O.Cl.(O2C-C6H4 -CO2)3] exposed oxygen and iron content of 29% and 24%, respectively, which offers unique properties as an oxygen-rich catalyst for energetic systems. Upon dispersion in an organic solvent and integration into ammonium perchlorate (AP) (the universal oxidizer for energetic systems), the dispersion of the MOF particles into the AP energetic matrix was uniform (investigated via elemental mapping using an EDX detector). Therefore, MIL-88B(Fe) could probe AP decomposition with the exclusive formation of mono-dispersed Fe2O3 nanocatalyst during the AP decomposition. The evolved nanocatalyst can offer superior combustion characteristics. XRD pattern for the MIL-88B(Fe) framework TGA residuals confirmed the formation of α-Fe2O3 nanocatalyst as a final product. The catalytic efficiency of MIL-88B(Fe) on AP thermal behavior was assessed via DSC and TGA. AP solely demonstrated a decomposition enthalpy of 733 J g-1 , while AP/MIL-88B(Fe) showed a 66% higher decomposition enthalpy of 1218 J g-1 ; the main exothermic decomposition temperature was decreased by 71 °C. Besides, MIL-88B(Fe) resulted in a decrease in AP decomposition activation energy by 23% and 25% using Kissinger and Kissinger–Akahira–Sunose (KAS) models, respectively.

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“…Currently, over 100,000 MOFs have been reported with nearly 500,000 predicted. − To an incoming student interested in MOFs the task of deciphering MOF structures is daunting not just for their sheer number but also because their structures can initially appear complicated. However, many MOF structures can be reduced in a systematic fashion into simpler basic forms, which in themselves are familiar to students.…”

mentioning

confidence: 99%

Visualizing Pore Packing and Topology in MOFs

Lisensky

Yaghi

2022

J. Chem. Educ.

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Metal organic frameworks (MOFs) can have open pore structures with high surface areas and applications in hydrogen storage, carbon dioxide capture, water harvesting from air, chemical separations, and catalysis. The chemical structure of these crystalline solids can initially appear daunting. Simplification by considering the arrangement of the pores and by identifying the links and vertices as well as switching between multiple representations can make visualization of these structures accessible.

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The launch of a freely accessible MOF CIF collection from the CSD

Cited by 23 publications

References 5 publications

Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

Microwave-Assisted Synthesis of the Flexible Iron-Based MIL-88B Metal-Organic Framework for Advanced Energetic Systems

Visualizing Pore Packing and Topology in MOFs

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