Bibliographic record
Abstract
Quantum chemistry dataset of ~836 thousand small organic and inorganic molecules. Density Functional Theory (DFT) properties for all 784,875 conformers in local minima; 258,242 constitutional isomers (most stable conformer) and 51,072 saddle point structures are available in the DFT_all.npz, DFT_uniques.npz and DFT_saddles.npz files respectively.Diffusion quantum Monte Carlo (DMC) data for 10,793 constitutional isomers is available in the DMC.npz file. All molecules are ordered in the same way across every array. Keys for accessing each property are tabulated in the paper. Usage example : import numpy as np data = np.load('DFT_all.npz', allow_pickle=True) print(data.files) #see a list of all properties key = 'freqs' property = data[key] #DFT vibrational frequencies of all moleculesprint(property[42]) #Frequencies of molecule number 42 in the array (HSCl, Thiohypochlorous acid) Input file samples, tools and kernel ridge regression, graph neural network models : https://github.com/dkhan42/VQM24Atomic energies (in Hartree) used to calculate the atomization energies : #atomic energies wB97X-D3/cc-pVDZ (PSI4 v1.7) eatomic = {'Hydrogen' : -0.5012728848846926, 'Carbon' : -37.83859584856468, 'Nitrogen' : -54.5760607136932450, 'Oxygen' : -75.0474818911551438, 'Fluorine' : -99.7031524437270917, 'Bromine' : -2574.01253635198464, 'Chlorine' : -460.13960793480203, 'Phosphorous' : -341.2510291850040858, 'Sulfur' : -398.1021030909759020, 'Silicon' : -289.3578409507016431} Wavefunctions of all 836 thousand molecules from the dataset are available as .molden files in wavefunctions.tar.gz.molden file for a specific molecule from the dataset can be found using the 'compounds' array in 'DFT_all.npz' file.For instance : the 0-th entry in the 'compounds' array of DFT_all.npz corresponds to 'SH2_0/conformer_1'Wavefunction file for this molecule will be found at 'wavefunctions/SH2_0/conformer_1.molden' after untarring wavefunctions.tar.gzMultiwfn (http://sobereva.com/multiwfn/) can be used to read the .molden wavefunction files Dataset is described in the paper : https://www.nature.com/articles/s41597-025-05428-4
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.060 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".