Text Mining Metal–Organic Framework Papers

Adsorption of Gases in Multimolecular Layers

Brunauer, S., Emmett, P. H., & Teller, E. (1938) · Journal of the American Chemical Society · doi:10.1021/ja01269a023 · Cited by 28,156 · see citing works

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094 · see citing works

The Cambridge Structural Database: a quarter of a million crystal structures and rising

Allen, F. H. (2002) · Acta Crystallographica Section B Structural Science · doi:10.1107/s0108768102003890 · Cited by 10,317 · see citing works

Selective gas adsorption and separation in metal–organic frameworks

Li, J., Kuppler, R. J., & Zhou, H.-C. “. (2009) · Chemical Society Reviews · doi:10.1039/b802426j · Cited by 8,945 · see citing works

Design and synthesis of an exceptionally stable and highly porous metal-organic framework

Li, H., Eddaoudi, M., O’Keeffe, M., & Yaghi, O. M. (1999) · Nature · doi:10.1038/46248 · Cited by 8,582 · see citing works

Systematic Design of Pore Size and Functionality in Isoreticular MOFs and Their Application in Methane Storage

Eddaoudi, M., Kim, J., Rosi, N. L., Vodak, D. T., Wachter, J., O’Keeffe, M., & Yaghi, O. M. (2002) · Science · doi:10.1126/science.1067208 · Cited by 8,146 · see citing works

Introduction to Metal–Organic Frameworks

Zhou, H.-C. “., Long, J. R., & Yaghi, O. M. (2012) · Chemical Reviews · doi:10.1021/cr300014x · Cited by 7,754 · see citing works

Carbon Dioxide Capture in Metal–Organic Frameworks

Sumida, K., Rogow, D. L., Mason, J. A., McDonald, T. M., Bloch, E. D., Herm, Z. R., Bae, T., & Long, J. R. (2011) · Chemical Reviews · doi:10.1021/cr2003272 · Cited by 6,425 · see citing works

Ultrahigh Porosity in Metal-Organic Frameworks

Furukawa, H., Ko, N., Go, Y. B., Aratani, N., Choi, S. B., Choi, E., Yazaydın, A. Ö., Snurr, R. Q., O’Keeffe, M., Kim, J., et al. (2010) · Science · doi:10.1126/science.1192160 · Cited by 3,914 · see citing works

Hydrogen Storage in Metal–Organic Frameworks

Suh, M. P., Park, H. J., Prasad, T. K., & Lim, D. (2011) · Chemical Reviews · doi:10.1021/cr200274s · Cited by 3,709 · see citing works

A route to high surface area, porosity and inclusion of large molecules in crystals

Materials Design and Discovery Group, Chae, H. K., Siberio-Pérez, D. Y., Kim, J., Go, Y., Eddaoudi, M., Matzger, A. J., O’Keeffe, M., & Yaghi, O. M. (2004) · Nature · doi:10.1038/nature02311 · Cited by 2,817 · see citing works

Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials

Willems, T., Rycroft, C. H., Kazi, M., Meza, J. C., & Harańczyk, M. (2011) · Microporous and Mesoporous Materials · doi:10.1016/j.micromeso.2011.08.020 · Cited by 1,935 · see citing works

Large-scale screening of hypothetical metal–organic frameworks

Wilmer, C. E., Leaf, M., Lee, C. Y., Farha, O. K., Hauser, B. G., Hupp, J. T., & Snurr, R. Q. (2012) · Nature Chemistry · doi:10.1038/nchem.1192 · Cited by 1,439 · see citing works

GENIA corpus—a semantically annotated corpus for bio-textmining

Kim, J., Ohta, T., Tateisi, Y., & Tsujii, J. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1023 · Cited by 1,236 · see citing works

Development of a Cambridge Structural Database Subset: A Collection of Metal–Organic Frameworks for Past, Present, and Future

Moghadam, P. Z., Li, A., Wiggin, S. B., Tao, A., Maloney, A. G. P., Wood, P. A., Ward, S. C., & Fairen‐Jiménez, D. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b00441 · Cited by 1,086 · see citing works

Natural language processing

Chowdhury, G. G. (2003) · Annual Review of Information Science and Technology · doi:10.1002/aris.1440370103 · Cited by 863 · see citing works

Fast and effective text mining using linear-time document clustering

Larsen, B., & Aone, C. (1999) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312186 · Cited by 863 · see citing works

A survey of current work in biomedical text mining

Cohen, A. M. (2005) · Briefings in Bioinformatics · doi:10.1093/bib/6.1.57 · Cited by 767 · see citing works

Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals

Chung, Y. G., Camp, J. S., Harańczyk, M., Sikora, B. J., Bury, W., Krungleviciute, V., Yildirim, T., Farha, O. K., Sholl, D. S., & Snurr, R. Q. (2014) · Chemistry of Materials · doi:10.1021/cm502594j · Cited by 748 · see citing works

Framework-Catenation Isomerism in Metal−Organic Frameworks and Its Impact on Hydrogen Uptake

Ma, S., Sun, D., Ambrogio, M. W., Fillinger, J. A., Parkin, S., & Zhou, H.-C. “. (2007) · Journal of the American Chemical Society · doi:10.1021/ja067435s · Cited by 636 · see citing works

Natural Language Processing and Text Mining

Kao, A., & Poteet, S. R. (2007) · Springer eBooks · doi:10.1007/978-1-84628-754-1 · Cited by 476 · see citing works

Pore Surface Engineering with Controlled Loadings of Functional Groups via Click Chemistry in Highly Stable Metal–Organic Frameworks

Jiang, H., Feng, D., Liu, T., Li, J., & Zhou, H.-C. “. (2012) · Journal of the American Chemical Society · doi:10.1021/ja3063919 · Cited by 380 · see citing works

Text Mining for Biology And Biomedicine

Ananiadou, S., & McNaught, J. (2005) · Cited by 367 · see citing works

Ontologies improve text document clustering

Andreas Hotho, A., Staab, S., & Gerd Stumme, A. (2004) · Proceedings · doi:10.1109/icdm.2003.1250972 · Cited by 357 · see citing works

Mn2(2,5-disulfhydrylbenzene-1,4-dicarboxylate): A Microporous Metal–Organic Framework with Infinite (−Mn–S−)∞ Chains and High Intrinsic Charge Mobility

Sun, L., Miyakai, T., Seki, S., & Dincă, M. (2013) · Journal of the American Chemical Society · doi:10.1021/ja4037516 · Cited by 341 · see citing works

Text mining and its potential applications in systems biology

Ananiadou, S., Kell, D. B., & Tsujii, J. (2006) · Trends in biotechnology · doi:10.1016/j.tibtech.2006.10.002 · Cited by 324 · see citing works

What Are the Best Materials To Separate a Xenon/Krypton Mixture?

Simon, C. M., Mercado, R., Schnell, S. K., Smit, B., & Harańczyk, M. (2015) · Chemistry of Materials · doi:10.1021/acs.chemmater.5b01475 · Cited by 274 · see citing works

Theoretical Limits of Hydrogen Storage in Metal–Organic Frameworks: Opportunities and Trade-Offs

Goldsmith, J., Wong‐Foy, A. G., Cafarella, M., & Siegel, D. J. (2013) · Chemistry of Materials · doi:10.1021/cm401978e · Cited by 274 · see citing works

High Gas Sorption and Metal‐Ion Exchange of Microporous Metal–Organic Frameworks with Incorporated Imide Groups

Prasad, T. K., Hong, D. H., & Suh, M. P. (2010) · Chemistry - A European Journal · doi:10.1002/chem.201002135 · Cited by 271 · see citing works

Total recovery of resources and energy from rice straw using microwave-induced pyrolysis

Huang, Y., Kuan, W., Lo, S., & Lin, C. (2008) · Bioresource Technology · doi:10.1016/j.biortech.2008.03.026 · Cited by 253 · see citing works

MedMiner: An Internet Text-Mining Tool for Biomedical Information, with Application to Gene Expression Profiling

Tanabe, L. K., Scherf, U., Smith, L. H., Lee, J. K., Hunter, L., & Weinstein, J. N. (1999) · BioTechniques · doi:10.2144/99276bc03 · Cited by 251 · see citing works

Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage

Thornton, A. W., Simon, C. M., Kim, J., Kwon, O., Deeg, K. S., Konstas, K., Pas, S. J., Hill, M. R., Winkler, D. A., Harańczyk, M., et al. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b04933 · Cited by 250 · see citing works

Accurate Characterization of the Pore Volume in Microporous Crystalline Materials

Ongari, D., Boyd, P. G., Barthel, S., Witman, M. D., Harańczyk, M., & Smit, B. (2017) · Langmuir · doi:10.1021/acs.langmuir.7b01682 · Cited by 244 · see citing works

Pattern for python

Smedt, T. D., & Daelemans, W. M. P. (2012) · Journal of Machine Learning Research · doi:10.5555/2188385.2343710 · Cited by 240 · see citing works

Large-Scale Refinement of Metal−Organic Framework Structures Using Density Functional Theory

Nazarian, D., Camp, J. S., Chung, Y. G., Snurr, R. Q., & Sholl, D. S. (2016) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b04226 · Cited by 135 · see citing works

Scalable Surface Area Characterization by Electrokinetic Analysis of Complex Anion Adsorption

Hanaor, D. A. H., Ghadiri, M., Chrzanowski, W., & Gan, Y. (2014) · Langmuir · doi:10.1021/la503581e · Cited by 125 · see citing works

A non-interpenetrated porous metal–organic framework with high gas-uptake capacity

Han, D., Jiang, F., Wu, M., Chen, L., Chen, Q., & Hong, M. (2011) · Chemical Communications · doi:10.1039/c1cc12858b · Cited by 118 · see citing works

Two highly-connected, chiral, porous coordination polymers featuring novel heptanuclear metal carboxylate clusters

Hou, L., Zhang, J., Chen, X., & Ng, S. W. (2008) · Chemical Communications · doi:10.1039/b809069f · Cited by 92 · see citing works

Modeling adsorption properties of structurally deformed metal–organic frameworks using structure–property map

Jeong, W., Lim, D., Kim, S., Harale, A., Yoon, M., Suh, M. P., & Kim, J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706330114 · Cited by 25 · see citing works

Crystalline beryllium carboxylate frameworks with rutile-type and cubic-C 3 N 4 topologies

Kang, M., Luo, D., Luo, X., Chen, Z., & Lin, Z. (2011) · CrystEngComm · doi:10.1039/c1ce06224g · Cited by 18 · see citing works

Recent advances and applications of deep learning methods in materials science

Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172 · see citing works

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 636 · see citing works

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

Zheng, Z.-L., Zhang, O.-F., Borgs, C., Chayes, J., & Yaghi, O. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c05819 · Cited by 484 · see citing works

Machine learning in catalysis

Kitchin, J. R. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0056-y · Cited by 481 · see citing works

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works

A Million Crystal Structures: The Whole Is Greater than the Sum of Its Parts

Taylor, R., & Wood, P. A. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00155 · Cited by 280 · see citing works

Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis

Bucior, B. J., Rosen, A., Harańczyk, M., Yao, Z., Ziebel, M. E., Farha, O. K., Hupp, J. T., Siepmann, J. I., Aspuru‐Guzik, A., & Snurr, R. Q. (2019) · Crystal Growth & Design · doi:10.1021/acs.cgd.9b01050 · Cited by 250 · see citing works

Applications of machine learning in metal-organic frameworks

Chong, S., Lee, S., Kim, B., & Kim, J. (2020) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2020.213487 · Cited by 224 · see citing works

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

Nandy, A., Duan, C., & Kulik, H. J. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c07217 · Cited by 200 · see citing works

Digital Reticular Chemistry

Lyu, H., Ji, Z., Wuttke, S., & Yaghi, O. M. (2020) · Chem · doi:10.1016/j.chempr.2020.08.008 · Cited by 172 · see citing works

A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

Zheng, Z., Rong, Z., Rampal, N., Borgs, C., Chayes, J., & Yaghi, O. M. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202311983 · Cited by 167 · see citing works

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Nandy, A., Terrones, G., Arunachalam, N., Duan, C., Kastner, D. W., & Kulik, H. J. (2022) · Scientific Data · doi:10.1038/s41597-022-01181-0 · Cited by 137 · see citing works

Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

Günay, M. E., & Yıldırım, R. (2020) · Catalysis Reviews · doi:10.1080/01614940.2020.1770402 · Cited by 136 · see citing works

Large language models for reticular chemistry

Zheng, Z., Rampal, N., Inizan, T. J., Borgs, C., Chayes, J. T., & Yaghi, O. M. (2025) · Nature Reviews Materials · doi:10.1038/s41578-025-00772-8 · Cited by 134 · see citing works

Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set

Kang, Y., Lee, W., Bae, T., Han, S., Jang, H., & Kim, J. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.4c11085 · Cited by 111 · see citing works

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Nandy, A., Duan, C., & Kulik, H. J. (2021) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100778 · Cited by 103 · see citing works

From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks

Park, J., Kim, H., Kang, Y., Lim, Y., & Kim, J. (2024) · JACS Au · doi:10.1021/jacsau.4c00618 · Cited by 100 · see citing works

Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis

Lin, J., Liu, Z., Guo, Y., Wang, S., Zhang, T., Xue, X., Li, R., Feng, S., Wang, L., Liu, J., et al. (2023) · Nano Today · doi:10.1016/j.nantod.2023.101802 · Cited by 99 · see citing works

DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining

Glasby, L. T., Gubsch, K., Bence, R., Oktavian, R., Isoko, K., Moosavi, S. M., Cordiner, J., Cole, J. C., & Moghadam, P. Z. (2023) · Chemistry of Materials · doi:10.1021/acs.chemmater.3c00788 · Cited by 92 · see citing works

Intermolecular Interactions in Functional Crystalline Materials: From Data to Knowledge

Vologzhanina, A. V. (2019) · Crystals · doi:10.3390/cryst9090478 · Cited by 82 · see citing works

Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications

Daglar, H., Gülbalkan, H. C., Aksu, G. O., & Keskın, S. (2024) · Advanced Materials · doi:10.1002/adma.202405532 · Cited by 80 · see citing works

Mining Insights on Metal–Organic Framework Synthesis from Scientific Literature Texts

Park, H., Kang, Y., Choe, W., & Kim, J. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01297 · Cited by 74 · see citing works

Machine Learning for the Discovery, Design, and Engineering of Materials.

Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works

Image and data mining in reticular chemistry powered by GPT-4V

Zheng, Z., He, Z., Khattab, O., Rampal, N., Zaharia, M., Borgs, C., Chayes, J., & Yaghi, O. M. (2024) · Digital Discovery · doi:10.1039/d3dd00239j · Cited by 69 · see citing works

Impacts of catalyst and process parameters on Ni-catalyzed methane dry reforming via interpretable machine learning

Vellayappan, K., Yue, Y., Lim, K. H., Cao, K., Tan, J. Y., Cheng, S., Wang, T., Gani, T. Z. H., Karimi, I. A., & Kawi, S. (2023) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2023.122593 · Cited by 58 · see citing works

Accelerated Discovery of Metal–Organic Frameworks for CO 2 Capture by Artificial Intelligence

Gülbalkan, H. C., Aksu, G. O., Ercakir, G., & Keskın, S. (2023) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.3c03817 · Cited by 47 · see citing works

On the shoulders of high-throughput computational screening and machine learning: Design and discovery of MOFs for H2 storage and purification

Altıntaş, Ç., & Keskın, S. (2023) · Materials Today Energy · doi:10.1016/j.mtener.2023.101426 · Cited by 46 · see citing works

Mining Solid-State Electrolytes from Metal–Organic Framework Databases through Large Language Models and Representation Clustering

Zhang, J., Li, J., Zhao, G., Wang, Q., Guo, Y., & Yang, C. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c12212 · Cited by 34 · see citing works

A functionalized UiO-66 MOF acting as a luminescent chemosensor for selective and sensitive turn-on detection of superoxide and acetylacetone

Ghosh, S., Das, A. K., & Biswas, S. (2021) · Microporous and Mesoporous Materials · doi:10.1016/j.micromeso.2021.111251 · Cited by 33 · see citing works

Language models for materials discovery and sustainability: Progress, challenges, and opportunities

Pei, Z., Yin, J., & Zhang, J. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101495 · Cited by 30 · see citing works

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

Batchu, S. P., Hernández, B., Malhotra, A., Fang, H., Ierapetritou, M., & Vlachos, D. G. (2022) · Reaction Chemistry & Engineering · doi:10.1039/d1re00560j · Cited by 30 · see citing works

Data Mining for Binary Separation Materials in Published Adsorption Isotherms

Iacomi, P., & Llewellyn, P. L. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b03376 · Cited by 29 · see citing works

Machine learning to design metal–organic frameworks: progress and challenges from a data efficiency perspective

Gómez‐Gualdrón, D. A., de Vilas, T. G., Ardila, K., Fajardo-Rojas, F., & Pak, A. J. (2025) · Materials Horizons · doi:10.1039/d5mh01467k · Cited by 28 · see citing works

Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

Sturluson, Á., Raza, A., McConachie, G. D., Siderius, D. W., Fern, X. Z., & Simon, C. M. (2021) · Chemistry of Materials · doi:10.1021/acs.chemmater.1c01201 · Cited by 25 · see citing works

Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal–Organic Frameworks

Ongari, D., Talirz, L., Jablonka, K. M., Siderius, D. W., & Smit, B. (2022) · Journal of Chemical & Engineering Data · doi:10.1021/acs.jced.1c00958 · Cited by 24 · see citing works

Modelling of framework materials at multiple scales: current practices and open questions

Fraux, G., Chibani, S., & Coudert, F. (2019) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2018.0220 · Cited by 23 · see citing works

Automated Genre Classification of Books Using Machine Learning and Natural Language Processing

Gupta, S., Agarwal, M., & Jain, S. (2019) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence.2019.8776935 · Cited by 23 · see citing works

New insights and innovation from a million crystal structures in the Cambridge Structural Database

Cole, J. C., Wiggin, S. B., & Stanzione, F. (2019) · Structural Dynamics · doi:10.1063/1.5116878 · Cited by 23 · see citing works

Applicability of Allen–Heine–Cardona Theory on MOx Metal Oxides and ABO3 Perovskites: Toward High-Temperature Optoelectronic Applications

Park, J., Saidi, W. A., Chorpening, B., & Duan, Y. (2022) · Chemistry of Materials · doi:10.1021/acs.chemmater.2c01281 · Cited by 21 · see citing works

Drug delivery system tailoring via metal-organic framework property prediction using machine learning: A disregarded approach

Pouyanfar, N., Ahmadi, M., Ayyoubzadeh, S. M., & Ghorbani-Bidkorpeh, F. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.107938 · Cited by 20 · see citing works

Machine learning approaches for the prediction of hydrogen uptake in metal-organic-frameworks: A comprehensive review

Yamde, A. A., Lade, V. G., Bindwal, A. B., Tiwari, M. S., & Birmod, R. P. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.12.131 · Cited by 20 · see citing works

Development of Mixed Matrix Membranes by Using NH2‐Functionalized UiO‐66 and [APTMS][AC] Ionic Liquid for the Separation of CO2

Khalid, H. M., Mujahid, A., Ali, A., Khan, A. L., Saleem, M., & Santos, R. M. (2024) · International Journal of Energy Research · doi:10.1155/2024/2107340 · Cited by 17 · see citing works

Machine learning-optimized synthesis of doped TiO2 with improved photocatalytic performance: A multi-step workflow supported by designed wet-lab experiments

Gao, B., Sun, M., Ding, Z., & Liu, W. (2021) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2021.160534 · Cited by 17 · see citing works

Unlocking deep eutectic solvent knowledge through a large language model-driven framework and an interactive AI agent

Peng, X., Tew, Y. S., Zhao, K., Wang, C., Li, R., Hu, S., & Wang, X. (2025) · Green Chemical Engineering · doi:10.1016/j.gce.2025.05.006 · Cited by 14 · see citing works

Text mining in MOF research: from manual curation to large language model-based automation

Bae, S., Jeon, M., & Moon, H. R. (2025) · Chemical Communications · doi:10.1039/d5cc02511g · Cited by 13 · see citing works

A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

Zheng, Z., Rong, Z., Rampal, N., Borgs, C., Chayes, J., & Yaghi, O. M. (2023) · Angewandte Chemie · doi:10.1002/ange.202311983 · Cited by 13 · see citing works

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes

Kevlishvili, I., Michel, R. G. S., Garrison, A. G., Toney, J., Adamji, H., Jia, H., Román‐Leshkov, Y., & Kulik, H. J. (2024) · Faraday Discussions · doi:10.1039/d4fd00087k · Cited by 12 · see citing works

In Silico Investigation into H2 Uptake in MOFs: Combined Text/Data Mining and Structural Calculations

Tayfuroğlu, Ö., Kocak, A., & Zorlu, Y. (2019) · Langmuir · doi:10.1021/acs.langmuir.9b03618 · Cited by 11 · see citing works

How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?

Wu, Y., Li, X., Guo, R., Xu, R., Ju, M., & Wang, J. (2025) · National Science Review · doi:10.1093/nsr/nwaf081 · Cited by 9 · see citing works

Computer-aided metal–organic framework screening and design approaches toward efficient carbon capture processes

Wang, Z., & Zhou, T. (2025) · Molecular Systems Design & Engineering · doi:10.1039/d5me00075k · Cited by 9 · see citing works

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts

Tarasova, O. A., Biziukova, N. Y., Rudik, A. V., Dmitriev, A. V., Filimonov, D. A., & Poroikov, V. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01054 · Cited by 9 · see citing works

MatGD: Materials Graph Digitizer

Lee, J., Lee, W., & Kim, J. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c14781 · Cited by 9 · see citing works

Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature

Li, S., Zhang, Y., Fang, Z., Meng, K., Tian, R., He, H., & Sun, S. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01442 · Cited by 8 · see citing works

Using experimental data in computationally guided rational design of inorganic materials with machine learning

Kulik, H. J. (2025) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/s43578-025-01568-w · Cited by 7 · see citing works

Toward smart CO 2 capture by the synthesis of metal organic frameworks using large language models

Mashhadimoslem, H., Abdol, M. A., Zanganeh, K., Shafeen, A., Liu, E., Zendehboudi, S., Elkamel, A., & Yu, A. (2025) · Digital Discovery · doi:10.1039/d5dd00446b · Cited by 4 · see citing works

Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research

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