A charge-density machine-learning workflow for computing the infrared spectrum of molecules
Bibliographic record
Abstract
We present a machine-learning workflow for the calculation of the infrared spectrum of molecules and more generally of other temperature-dependent electronic observables. The main idea is to use the Jacobi-Legendre cluster expansion to predict the real-space charge density of a converged density-functional-theory calculation. This gives us access to both energy and forces and to electronic observables such as the dipole moment or the electronic gap. Thus, the same model can simultaneously drive a molecular dynamics simulation and evaluate electronic quantities along the trajectory, namely, it has access to the same information of ab initio molecular dynamics. A similar approach within the framework of machine-learning force fields would require the training of multiple models, one for the molecular dynamics and others for predicting the electronic quantities. The scheme is implemented here within the numerical framework of the PySCF code and applied to the infrared spectrum of the uracil molecule in the gas phase.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".