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Hardiagon A, Coudert FX. Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior. Acc Chem Res 2024; 57:1620-1632. [PMID: 38752454 DOI: 10.1021/acs.accounts.4c00161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
ConspectusNanoporous frameworks are a large and diverse family of supramolecular materials, whose chemical building units (organic, inorganic, or both) are assembled into a 3D architecture with well-defined connectivity and topology, featuring intrinsic porosity. These materials play a key role in various industrial processes and applications, such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both their physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multiscale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption.In the study of the mechanical behavior of nanoporous materials, the past few years have seen a rapid acceleration of research. For example, computational resources have been pooled to create a public large-scale database of elastic constants as part of the Materials Project initiative to accelerate innovation in materials research: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models.The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such a large scale. Tentative databases have been assembled at the DFT level on specific families of materials, such as zeolites, but prediction at larger scale currently requires the use of transferable classical force fields, whose accuracy can be limited.Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at a large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties: in particular, we describe the recent research efforts to go beyond the study of thermodynamic quantities (uptake, adsorption enthalpy, and thermodynamic selectivity) and predict transport properties using data-based methods and high-throughput computational schemes. Finally, we stress the importance of data-based methods of addressing all sources of uncertainty.The Account concludes with some perspectives about the latest developments and open questions in data-based approaches and the integration of computational and experimental data together in the materials discovery loop.
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Affiliation(s)
- Arthur Hardiagon
- Chimie ParisTech, PSL University, CNRS, Institut de Recherche de Chimie Paris, 75005 Paris, France
| | - François-Xavier Coudert
- Chimie ParisTech, PSL University, CNRS, Institut de Recherche de Chimie Paris, 75005 Paris, France
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2
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Lee J, Lee W, Kim J. MatGD: Materials Graph Digitizer. ACS APPLIED MATERIALS & INTERFACES 2024; 16:723-730. [PMID: 38147629 DOI: 10.1021/acsami.3c14781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
We developed Material Graph Digitizer (MatGD), which is a tool for digitizing a data line from scientific graphs. The algorithm behind the tool consists of four steps: (1) identifying graphs within subfigures, (2) separating axes and data sections, (3) discerning the data lines by eliminating irrelevant graph objects and matching with the legend, and (4) data extraction and saving. From the 62,534 papers in the areas of batteries, catalysis, and metal-organic frameworks (MOFs), 501,045 figures were mined. Remarkably, our tool showcased performance with over 99% accuracy in legend marker and text detection. Moreover, its capability for data line separation stood at 66%, which is much higher compared to those of other existing figure-mining tools. We believe that this tool will be integral to collecting both past and future data from publications, and these data can be used to train various machine learning models that can enhance material predictions and new materials discovery.
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Affiliation(s)
- Jaewoong Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Wonseok Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Jihan Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
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3
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Gulbalkan H, Aksu GO, Ercakir G, Keskin S. Accelerated Discovery of Metal-Organic Frameworks for CO 2 Capture by Artificial Intelligence. Ind Eng Chem Res 2024; 63:37-48. [PMID: 38223500 PMCID: PMC10785804 DOI: 10.1021/acs.iecr.3c03817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 01/16/2024]
Abstract
The existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO2) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal-organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO2 adsorption and separation. In this perspective, we highlight the pioneering works in which AI, molecular simulations, and experiments have been combined to produce exceptional MOFs and MOF-based composites that outperform traditional porous materials in CO2 capture. We outline the future directions by discussing the current opportunities and challenges in the field of harnessing experiments, theory, and AI for accelerated discovery of porous materials for CO2 capture.
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Affiliation(s)
| | | | - Goktug Ercakir
- Department of Chemical and Biological
Engineering, Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
| | - Seda Keskin
- Department of Chemical and Biological
Engineering, Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
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4
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Zheng Z, Rong Z, Rampal N, Borgs C, Chayes JT, Yaghi OM. A GPT-4 Reticular Chemist for Guiding MOF Discovery. Angew Chem Int Ed Engl 2023; 62:e202311983. [PMID: 37798813 DOI: 10.1002/anie.202311983] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/07/2023]
Abstract
We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities.
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Affiliation(s)
- Zhiling Zheng
- Department of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA-94720, United States
| | - Zichao Rong
- Department of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA-94720, United States
| | - Nakul Rampal
- Department of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA-94720, United States
| | - Christian Borgs
- Department of Electrical Engineering and Computer Sciences and Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA-94720, United States
| | - Jennifer T Chayes
- Department of Electrical Engineering and Computer Sciences, Department of Statistics, Department of Mathematics, School of Information, and Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA-94720, United States
| | - Omar M Yaghi
- Department of Chemistry, Kavli Energy Nanoscience Institute, and Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA-94720, United States
- KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications, King Abdulaziz City for Science and Technology, Riyadh, 11442, Saudi Arabia
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5
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Li S, Zhang Y, Fang Z, Meng K, Tian R, He H, Sun S. Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature. J Chem Inf Model 2023; 63:6249-6260. [PMID: 37807535 DOI: 10.1021/acs.jcim.3c01442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%.
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Affiliation(s)
- Shuyuan Li
- Beijing Key Laboratory for Green Catalysis and Separation, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Yunjiang Zhang
- Beijing Key Laboratory for Green Catalysis and Separation, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Zhaolin Fang
- Beijing Key Laboratory for Green Catalysis and Separation, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Kong Meng
- Beijing Key Laboratory for Green Catalysis and Separation, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Rui Tian
- Beijing Engineering Research Center for IoT Software and Systems, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Hong He
- Beijing Key Laboratory for Green Catalysis and Separation, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Shaorui Sun
- Beijing Key Laboratory for Green Catalysis and Separation, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
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6
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Zheng Z, Zhang O, Borgs C, Chayes JT, Yaghi OM. ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis. J Am Chem Soc 2023; 145:18048-18062. [PMID: 37548379 PMCID: PMC11073615 DOI: 10.1021/jacs.3c05819] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic framework (MOF) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information, an issue that previously made the use of large language models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different trade-offs among labor, speed, and accuracy. We deploy this system to extract 26 257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the data set built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions about chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines.
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Affiliation(s)
- Zhiling Zheng
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
| | | | - Christian Borgs
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
| | - Jennifer T Chayes
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
- Department of Mathematics, University of California, Berkeley, California 94720, United States
- Department of Statistics, University of California, Berkeley, California 94720, United States
- School of Information, University of California, Berkeley, California 94720, United States
| | - Omar M Yaghi
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute, University of California, Berkeley, California 94720, United States
- Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, United States
- KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
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7
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Glasby L, Gubsch K, Bence R, Oktavian R, Isoko K, Moosavi SM, Cordiner JL, Cole JC, Moghadam PZ. DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:4510-4524. [PMID: 37332681 PMCID: PMC10269341 DOI: 10.1021/acs.chemmater.3c00788] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/29/2023] [Indexed: 06/20/2023]
Abstract
The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.
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Affiliation(s)
- Lawson
T. Glasby
- Department
of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
| | - Kristian Gubsch
- Department
of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
| | - Rosalee Bence
- Department
of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
| | - Rama Oktavian
- Department
of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
| | - Kesler Isoko
- Department
of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
| | - Seyed Mohamad Moosavi
- Chemical
Engineering & Applied Chemistry, University
of Toronto, Toronto, Ontario M5S 3E5, Canada
| | - Joan L. Cordiner
- Department
of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
| | - Jason C. Cole
- Cambridge
Crystallographic Data Centre, Cambridge CB2 1EZ, U.K.
| | - Peyman Z. Moghadam
- Department
of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
- Department
of Chemical Engineering, University College
London, London WC1E 7JE, U.K.
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8
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Demir H, Daglar H, Gulbalkan HC, Aksu GO, Keskin S. Recent advances in computational modeling of MOFs: From molecular simulations to machine learning. Coord Chem Rev 2023. [DOI: 10.1016/j.ccr.2023.215112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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9
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Domingues NP, Moosavi SM, Talirz L, Jablonka KM, Ireland CP, Ebrahim FM, Smit B. Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF. Commun Chem 2022; 5:170. [PMID: 36697847 PMCID: PMC9814730 DOI: 10.1038/s42004-022-00785-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022] Open
Abstract
The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al2(OH)2TCPP) [H2TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.
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Affiliation(s)
- Nency P. Domingues
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland
| | - Seyed Mohamad Moosavi
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland ,grid.14095.390000 0000 9116 4836Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Leopold Talirz
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland ,grid.5333.60000000121839049Theory and Simulation of Materials (THEOS), School of Engineering (STI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Vaud Switzerland
| | - Kevin Maik Jablonka
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland
| | - Christopher P. Ireland
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland
| | - Fatmah Mish Ebrahim
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland ,grid.5335.00000000121885934Cavendish Laboratory, School of Physical Sciences, University of Cambridge, Cambridge, UK
| | - Berend Smit
- grid.5333.60000000121839049Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais Switzerland
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10
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Yin X, Gounaris CE. Computational discovery of Metal–Organic Frameworks for sustainable energy systems: Open challenges. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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11
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Li A, Bueno-Perez R, Madden D, Fairen-Jimenez D. From computational high-throughput screenings to the lab: taking metal–organic frameworks out of the computer. Chem Sci 2022; 13:7990-8002. [PMID: 35919420 PMCID: PMC9278459 DOI: 10.1039/d2sc01254e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/13/2022] [Indexed: 12/22/2022] Open
Abstract
Computational high-throughput screenings (HTS) have become a standard method of sieving the vast amount of metal–organic frameworks (MOFs) data. But not many HTS studies have been able to bring MOFs to the lab.
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Affiliation(s)
- Aurelia Li
- The Adsorption & Advanced Materials Laboratory (A2ML), Department of Chemical Engineering & Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
| | - Rocio Bueno-Perez
- The Adsorption & Advanced Materials Laboratory (A2ML), Department of Chemical Engineering & Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
| | - David Madden
- The Adsorption & Advanced Materials Laboratory (A2ML), Department of Chemical Engineering & Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
| | - David Fairen-Jimenez
- The Adsorption & Advanced Materials Laboratory (A2ML), Department of Chemical Engineering & Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
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