High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration
Abstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional ap...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-022-00796-6 |
| _version_ | 1818240773089394688 |
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| author | Andrew S. Rosen Victor Fung Patrick Huck Cody T. O’Donnell Matthew K. Horton Donald G. Truhlar Kristin A. Persson Justin M. Notestein Randall Q. Snurr |
| author_facet | Andrew S. Rosen Victor Fung Patrick Huck Cody T. O’Donnell Matthew K. Horton Donald G. Truhlar Kristin A. Persson Justin M. Notestein Randall Q. Snurr |
| author_sort | Andrew S. Rosen |
| collection | DOAJ |
| description | Abstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project. |
| first_indexed | 2024-12-12T13:18:46Z |
| format | Article |
| id | doaj.art-e0c539f7b8f34ab1bf3579bc903a0157 |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-12-12T13:18:46Z |
| publishDate | 2022-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-e0c539f7b8f34ab1bf3579bc903a01572022-12-22T00:23:22ZengNature Portfolionpj Computational Materials2057-39602022-05-018111010.1038/s41524-022-00796-6High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data explorationAndrew S. Rosen0Victor Fung1Patrick Huck2Cody T. O’Donnell3Matthew K. Horton4Donald G. Truhlar5Kristin A. Persson6Justin M. Notestein7Randall Q. Snurr8Department of Materials Science and Engineering, University of CaliforniaCenter for Nanophase Materials Sciences, Oak Ridge National LaboratoryEnergy Technologies Area, Lawrence Berkeley National LaboratoryMaterials Science Division, Lawrence Berkeley National LaboratoryMaterials Science Division, Lawrence Berkeley National LaboratoryDepartment of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of MinnesotaDepartment of Materials Science and Engineering, University of CaliforniaDepartment of Chemical and Biological Engineering, Northwestern UniversityDepartment of Chemical and Biological Engineering, Northwestern UniversityAbstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.https://doi.org/10.1038/s41524-022-00796-6 |
| spellingShingle | Andrew S. Rosen Victor Fung Patrick Huck Cody T. O’Donnell Matthew K. Horton Donald G. Truhlar Kristin A. Persson Justin M. Notestein Randall Q. Snurr High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration npj Computational Materials |
| title | High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration |
| title_full | High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration |
| title_fullStr | High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration |
| title_full_unstemmed | High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration |
| title_short | High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration |
| title_sort | high throughput predictions of metal organic framework electronic properties theoretical challenges graph neural networks and data exploration |
| url | https://doi.org/10.1038/s41524-022-00796-6 |
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