ab initio REPEAT Charge MOF Database (ARC-MOF)
Bibliographic record
Abstract
This is a database of ~280,000 MOFs which have been either experimentally characterized or computationally generated, spanning all publicly available MOF databases. DFT-derived REPEAT charges, adsorption data, and various descriptors are available for all MOFs. <em>all_structures_1.tar.gz</em> and <em>all_structures_2.tar.gz</em> – these are the cif files that were considered to compose the “entire known design space” of MOFs, with any bad structures removed (split into two separate tarballs since it is a lot of data). <em>ARCMOF_20220610.tar.gz</em> – these are all of the cif files with REPEAT charges composing ARC-MOF. <em>flig-clusters.csv, func-clusters.csv, geo-clusters.csv, mc-clusters.csv</em> – Each file indicates for each MOF which cluster it belongs to, and whether the MOF is present in ARC-MOF. This is done for each "type" of MOF chemistry and for the geometric properties. Clusters with a negative value indicate the MOF does not belong to any cluster (i.e., it is assumed to be "unique"). <em>all_topology_lists.csv</em> – a csv file containing the topology reported by the filename of applicable structures, and the topology reported by CrystalNets.jl ML_test_set.tar.gz – these are the cif files (with REPEAT charges) of the MOFs in the diverse-mc subset, but missing from ARC-MOF (for the purposes of a ML test set for the prediction of metal charges). <em>geometric_properties.csv</em> – a csv file containing geometric descriptors computed for this study for all MOFs. The csv file also indicates which MOFs are present in ARC-MOF, and the order in which they were chosen for the farthest point sampling (up to 100K MOFs). <em>RACs.csv</em> – See geometric_properties.csv description. Same type of file, but with the RAC descriptors. <em>RDFs</em>.csv – The RDFs for each MOF, using several atomic properties. Some atomic properties are not available for all elements. In the cases where the atomic property is not available for a particular structure, no value is assigned. <em>methane.csv, methane_purification-CH4.csv, methane_purification_CO2.csv, post_comb_vsa-CO2.csv, post_comb_vsa-N2.csv, pre_comb_4040-CO2.csv, pre_comb_4040-H2.csv, landfill-CH4.csv, landfill-CO2.csv</em> – these are csv files of the raw uptake data and various temperature, pressure conditions (with standard deviations) for each gas separation process specified in the file <em>overall_process.csv</em>. <em>overall_process.csv</em> – This is a csv file of the adsorption properties of the MOFs. Particularly, the csv files contain the working capacity (mmol/g_working_capacity) and selectivity of each MOF for each of the five process conditions. <em>mc-diverse-set.csv, func-diverse-set.csv</em> – csv files containing which MOFs are present in each diverse set (from farthest point sampling of the MOFs based on either their functional group chemistry or metal chemistry). The file indicates which MOFs are present in ARC-MOF and which are not. Version history of repository: v2 -- added file: "all_topology_lists.csv" v3 -- added file: "ML_test_set.tar.gz" v4 -- replaced file: "ML_test_set.tar.gz". Originally incorrect repository of cifs
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.262 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".