Prediction of the near-IR spectra of ices by<i>ab initio</i>molecular dynamics
Bibliographic record
Abstract
A method to predict the near-infrared spectra of amorphous solids by means of ab initio molecular dynamics is presented. These solids can simulate molecular ices. To test the method, mixtures of methane, water and nitrogen are generated as amorphous samples of various concentrations. The full theoretical treatment includes as a first step, the optimization of their geometrical structure for a range of densities, after which, the most stable systems are taken as initial structures for molecular dynamics, performed at 200 K in trajectories of 4 ps duration with a 0.2 fs time step. All the dynamics are carried out using the first principles method, solving the quantum problem for the electrons using density-functional theory (DFT), and integrating the DFT forces, following the Born-Oppenheimer dynamics. After the dynamics, near-IR spectra are predicted by the Fourier transform of the macroscopic polarization autocorrelation function. The calculated spectra are compared with the experimental spectra of ice mixtures of CH4 and H2O recorded in our laboratory, and with some spectra recorded by the New Horizons mission on Pluto.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".