Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models
We construct a data set of metal–organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations,...
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| Published in | Journal of the American Chemical Society Vol. 145; no. 51; pp. 28284 - 28295 |
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| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Chemical Society
27.12.2023
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| Online Access | Get full text |
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| Abstract | We construct a data set of metal–organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g–1) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs. |
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| AbstractList | We construct a data set of metal–organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g–1) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs. We construct a data set of metal-organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g ) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs. |
| Author | Alawadhi, Ali H. Liu, Shengchao Rong, Zichao Rampal, Nakul Chayes, Jennifer T. Chheda, Saumil Siepmann, J. Ilja Zheng, Zhiling Gagliardi, Laura Borgs, Christian Anandkumar, Anima Lin, Yen-hsu Nguyen, Ha L. Yaghi, Omar M. Neumann, S. Ephraim |
| AuthorAffiliation | Department of Chemistry, Pritzker School of Molecular Engineering, Chicago Center for Theoretical Chemistry Computing and Mathematical Sciences Department of Electrical Engineering and Computer Sciences Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society Department of Chemical Engineering and Materials Science, Department of Chemistry, and Chemical Theory Center University of California Department of Mathematics Department of Chemistry KACST−UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications NVIDIA Corporation Department of Statistics School of Information Kavli Energy Nanoscience Institute King Abdulaziz City for Science and Technology |
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| Author_xml | – sequence: 1 givenname: Zhiling orcidid: 0000-0001-6090-2258 surname: Zheng fullname: Zheng, Zhiling organization: Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society – sequence: 2 givenname: Ali H. orcidid: 0000-0003-2680-5221 surname: Alawadhi fullname: Alawadhi, Ali H. organization: Kavli Energy Nanoscience Institute – sequence: 3 givenname: Saumil orcidid: 0000-0002-0989-5707 surname: Chheda fullname: Chheda, Saumil organization: Department of Chemical Engineering and Materials Science, Department of Chemistry, and Chemical Theory Center – sequence: 4 givenname: S. Ephraim orcidid: 0000-0002-8515-9621 surname: Neumann fullname: Neumann, S. Ephraim organization: Kavli Energy Nanoscience Institute – sequence: 5 givenname: Nakul surname: Rampal fullname: Rampal, Nakul organization: Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society – sequence: 6 givenname: Shengchao surname: Liu fullname: Liu, Shengchao organization: Department of Electrical Engineering and Computer Sciences – sequence: 7 givenname: Ha L. orcidid: 0000-0002-4977-925X surname: Nguyen fullname: Nguyen, Ha L. organization: Kavli Energy Nanoscience Institute – sequence: 8 givenname: Yen-hsu surname: Lin fullname: Lin, Yen-hsu organization: Kavli Energy Nanoscience Institute – sequence: 9 givenname: Zichao orcidid: 0000-0002-9014-9540 surname: Rong fullname: Rong, Zichao organization: Bakar Institute of Digital Materials for the Planet, College of Computing, Data Science, and Society – sequence: 10 givenname: J. Ilja orcidid: 0000-0003-2534-4507 surname: Siepmann fullname: Siepmann, J. Ilja organization: Department of Chemical Engineering and Materials Science, Department of Chemistry, and Chemical Theory Center – sequence: 11 givenname: Laura orcidid: 0000-0001-5227-1396 surname: Gagliardi fullname: Gagliardi, Laura organization: Department of Chemistry, Pritzker School of Molecular Engineering, Chicago Center for Theoretical Chemistry – sequence: 12 givenname: Anima surname: Anandkumar fullname: Anandkumar, Anima organization: NVIDIA Corporation – sequence: 13 givenname: Christian surname: Borgs fullname: Borgs, Christian organization: Department of Electrical Engineering and Computer Sciences – sequence: 14 givenname: Jennifer T. surname: Chayes fullname: Chayes, Jennifer T. organization: School of Information – sequence: 15 givenname: Omar M. orcidid: 0000-0002-5611-3325 surname: Yaghi fullname: Yaghi, Omar M. email: yaghi@berkeley.edu organization: King Abdulaziz City for Science and Technology |
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| Snippet | We construct a data set of metal–organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying... We construct a data set of metal-organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying... |
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| Title | Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models |
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