Bibliographic record
Abstract
# aPBE0 # `.xyz` files for all molecules from the QM5, QM9, QM7b, W4-17 datasets used for training and testing in the paper along with text files containing relevant aopt, apred, reference energies etc. are available in separate folders within the `train_test_data.tar.xz` file. Each folder for the dataset contains a `readme.txt` file explaining the reported data.# The "qmspin" folder also contains the 4 carbene structures stores separately for which no aopt value could be obtained by optimizing the singlet states alone such that the MRCISD+Q gap could be recovered# Text files containing predicted a value, aPBE0 energy, PBE0 energy, and mean absolute error (MAE) across all subsets from GMTKN55 reported in figure 3 are available in the `gmtkn55_data.tar.xz` file along with a 'readme.txt` description. CCSD(T) training data for the 1169 amons used to generate training labels is available in the `cc_train_data.npz` file. Chemical symbols, coordinates, CCSD total energies, CCSD(T) total energies, CCSD(T) atomization energies (all in Hartree) for each molecule are available in the `elements`, `coordinates`, `eccsd`, `eccsdt`, `hccsdt` arrays respectively in the same order. Spin gap test set used in figure 2 from the QMspin dataset is available in `qmspin_test_set.npz` along with MRCISD+Q spin gap energies. HOMO-LUMO gap test set used in figure 3 from the QM7b dataset is available in `qm7b_test_set.npz` along with GW HOMO, LUMO and HOMO-LUMO gap eigenvalues. All energies are reported in Hartrees. ML model for predicting the optimal exact exachange ratio to be used in the PBE0 functional Python libraries required : * Numpy* Numba* Joblib* Ase (if supplying xyz files)* cMBDF (https://github.com/dkhan42/cMBDF)* qml2 (https://github.com/dkhan42/qml2/tree/develop)* Pyscf (only for the `get_atomization` function) Usage : ```from get_exchange import get_predictionsopt_exchange = get_predictions(charges, coords)opt_exchange = get_predictions(xyz = 'mol.xyz') #if supplying xyz file instead```where `charges` and `coords` are arrays containing atomic numbers and atomic coordinates for each molecule To obtain aPBE0 atomization energy for a molecule (in Hartree) with the predicted exact exchange : ```from get_exchange import get_atomizationenergy = get_atomization(elements, coords, opt_exchange, basis)``` where `elements` is the array (strings) of chemical symbols in the molecule and `exchange` is the predicted exact exchange fraction
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.074 | 0.022 |
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".