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https://github.com/SimonEnsemble/porous-material-AI-gym
open data sets for machine learning pertaining to porous materials
https://github.com/SimonEnsemble/porous-material-AI-gym
Last synced: 3 months ago
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open data sets for machine learning pertaining to porous materials
- Host: GitHub
- URL: https://github.com/SimonEnsemble/porous-material-AI-gym
- Owner: SimonEnsemble
- Created: 2021-05-24T19:51:54.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-28T15:45:08.000Z (12 months ago)
- Last Synced: 2024-07-31T15:38:43.698Z (3 months ago)
- Size: 592 KB
- Stars: 23
- Watchers: 4
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-chemistry-datasets - porous materials AI gym
README
# porous materials AI gym
open data sets for machine learning pertaining to porous materials.
- MOF = metal-organic framework
- COF = covalent organic framework
## crystal structures
### experimental
- MOFs: CoRE MOFs ([Paper](https://doi.org/10.1021/acs.jced.9b00835), [Database](https://zenodo.org/record/3677685)), CSD MOF Subset ([Paper](https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.7b00441), [Database](https://sites.google.com/view/csdmofsubset/home)), CSD MOF Collection ([Paper](https://doi.org/10.1016/j.matt.2021.03.006), [Database](https://www.ccdc.cam.ac.uk/Community/csd-community/csd-mof-collection/))
- COFs: CURATED COFs ([Paper](https://pubs.acs.org/doi/10.1021/acscentsci.9b00619), [Database](https://github.com/danieleongari/CURATED-COFs)), CoRE COFs ([Paper](https://doi.org/10.1016/j.ces.2017.05.004), [Database](https://github.com/core-cof/CoRE-COF-Database))
- zeolites: IZA database ([Database](http://www.iza-structure.org/databases/))
### hypothetical
- MOFs: B&W ([Paper](https://www.nature.com/articles/s41586-019-1798-7), [Database](https://doi.org/10.24435/materialscloud:2018.0016/v3)), ToBaCCo ([Paper](https://pubs.acs.org/doi/abs/10.1021/acs.cgd.7b00848), [Database](https://mof.tech.northwestern.edu/databases), [Code](https://github.com/tobacco-mofs/tobacco_3.0)), hMOFs ([Paper](https://www.nature.com/articles/nchem.1192), [Database](https://mof.tech.northwestern.edu/databases)), Anderson et al. ([Paper](https://chemrxiv.org/articles/preprint/Deep_Learning_Combined_with_IAST_to_Screen_Thermodynamically_Feasible_MOFs_for_Adsorption-Based_Separation_of_Multiple_Binary_Mixtures/14122901/1), [Database](https://osf.io/7dgvy/)), MOF-5 analogues ([Paper](https://doi.org/10.1021/jp401920y), [Database](http://www.nanoporousmaterials.org/databases/)), PORMAKE ([Paper](https://doi.org/10.1021/acsami.1c02471), [Code](https://github.com/Sangwon91/PORMAKE))
- COFs: Mercado et al. ([Paper](https://doi.org/10.1021/acs.chemmater.8b01425), [Database](https://archive.materialscloud.org/record/2018.0003/v1)), Haranczyk's 3D COF database ([Paper](https://pubs.acs.org/doi/10.1021/jp507152j), [Database](http://www.nanoporousmaterials.org/databases/))
## labeled porous materials for supervised learning
| material class | target y | features x provided? | Reference | size of data set |
| ------------------------------------ | ---------------------------------------------------- | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| MOFs (hypothetical) | CO2, N2 adsorption (sim) | yes | [Paper](https://www.nature.com/articles/s41586-019-1798-7), [Database](https://doi.org/10.24435/materialscloud:2018.0016/v3) | ca. 325,000 |
| MOFs (experimental and hypothetical) | Band gaps, density of states, charge densities (sim) | yes | [Paper](https://doi.org/10.1016/j.matt.2021.02.015), [Database](https://github.com/arosen93/QMOF) | ca. 18,000 |
| MOFs (experimental) | Color (exp) | yes | [Paper](https://doi.org/10.1039/D0SC05337F), [Database](https://doi.org/10.24435/materialscloud:cc-j6) | ? |
| COFs (hypothetical) | CH4 deliverable capacity (sim) | yes, hand-crafted features provided. | [Paper](https://doi.org/10.1021/acs.chemmater.8b01425), [Database](https://archive.materialscloud.org/2018.0003/v3) | ca. 70,000 |
| COFs (experimental) | CH4, H2, O2, Xe, Kr, H2S adsorption (sim) | ? | [Paper](https://doi.org/10.1021/acscentsci.0c00988) | ca. 500 |
| MOFs (hypothetical) | H2 adsorption (sim) | yes | [Paper](https://www.sciencedirect.com/science/article/pii/S2666389921001240#bib70) / [Database](https://datahub.hymarc.org/dataset/computational-prediction-of-hydrogen-storage-capacities-in-mofs) | ca. 100K
| MOFs (experimental) | thermal stability, solvent removal stability | yes (RAC & geometric features) | [Paper](https://www.nature.com/articles/s41597-022-01181-0) / [Database](https://zenodo.org/record/5737968#.YjNo6lRlAuU) | ca. 2-3K (extracted from experimental lit)
| MOFs (experimental) | CO2, H2O DFT-calculated adsorption energy | no | [Paper](https://arxiv.org/pdf/2311.00341.pdf), [DataBase](https://open-dac.github.io/) | ca. 8400 MOFs, but 38M DFT calcs |
## labeled nodes for supervised learning
| material class | target y | Reference | size of data set (# materials) |
| ------------------------------------ | -------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------- |
| MOFs (experimental) | DDEC6 charges on atoms (sim) | [Paper](https://doi.org/10.1021/acs.chemmater.5b03836), [Database](https://zenodo.org/record/3986573#.XzfKiJMzY8N) | ca. 3,000 |
| MOFs (experimental and hypothetical) | DDEC6/CM5/Bader charges on atoms (sim) | [Paper](https://doi.org/10.1016/j.matt.2021.02.015), [Database](https://github.com/arosen93/QMOF) | ca. 18,000 (DDEC6/CM5), ca. 5,000 (Bader) |
| MOFs (experimental and hypothetical) | Effective bond orders on atoms (sim) | [Paper](https://doi.org/10.1016/j.matt.2021.02.015), [Database](https://github.com/arosen93/QMOF) | ca. 18,000 |
| MOFs (experimental) | Formal oxidation states on atoms (exp) | [Paper](https://chemrxiv.org/articles/preprint/Using_Collective_Knowledge_to_Assign_Oxidation_States/11604129/1), [Database](https://doi.org/10.24435/materialscloud:dq-ey) | ca. 49,000 |
# other data sets, pertaining to materials, for machine learning
see `matminer` [here](https://hackingmaterials.lbl.gov/matminer/dataset_summary.html). pointed out by [Jack Evans](https://twitter.com/jackevansADL/status/1439730395570851841).
# construct your own crystal structures!
here is a list of open-source codes for building your own crystal structure models.
| name of code | link to code | link to associated paper |
| -- | -- | -- |
| `tobacco` | [link](https://github.com/tobacco-mofs/tobacco_3.0) | [link](https://pubs.acs.org/doi/abs/10.1021/acs.cgd.7b00848) |
| `pormake` | [link](https://github.com/Sangwon91/PORMAKE) | [link](https://pubs.acs.org/doi/full/10.1021/acsami.1c02471) |
| `ToBasCCo` | [link](https://github.com/peteboyd/tobascco) | [link](https://www.nature.com/articles/s41586-019-1798-7?proof=tNature) |
| `Zeo++` | [link](http://www.zeoplusplus.org/) | [link](https://pubs.acs.org/doi/abs/10.1021/cg500158c) |
| `stk` | [link](https://github.com/lukasturcani/stk) | [link](https://aip.scitation.org/doi/10.1063/5.0049708) |
| `PoreMatMod.jl` (only modifies) | [link](https://github.com/SimonEnsemble/PoreMatMod.jl) | [link](https://pubs.acs.org/doi/10.1021/acs.jcim.1c01219) |
| `pyCOFBuilder` | [link](https://github.com/lipelopesoliveira/pyCOFBuilder) | [link](https://arxiv.org/abs/2310.14822) |