Phase diagram of CO2-I/III from molecular dynamics simulation using a PBE0-accuracy machine learning potential
Bibliographic record
Abstract
We employ the PBC-GEBF-AL workflow, integrating active learning (AL), the linear-scaling generalized energy-based fragmentation approach under periodic boundary conditions (PBC-GEBF), and multiTPU-OPES enhanced sampling, to create a PBE0-D3(BJ)/aug-cc-pVDZ-accuracy machine learning potential for studying the carbon dioxide (CO2) molecular crystal phase diagram. Based on up to 170 ns MLP-based multiTPU-OPES simulations, we obtain the phase diagram across a wide temperature range (250-700 K) and pressure range (10.5-14.0 GPa). It suggests a CO2-I/III coexistence line peaks at ∼12.3 GPa, 525 K, with a negative slope at higher temperatures, closely matching experimental I/III and I/VII transition pressures. Trajectory analysis revealed a concerted CO2-I/III transition mechanism driven by molecular rotation, lattice deformation, and non-monotonic volume changes (expansion followed by contraction). We propose a new Cmca space group structure for the true CO2-III phase, characterized by molecules tilted relative to the ac plane and lattice parameters a > b. It differs from the reported CO2-III structure determined by powder x-ray diffraction but closely resembles the CO2-VII crystal, where CO2 molecules are aligned parallel to the ac plane. This proposed tilted Cmca structure coexists with CO2-VII at high temperatures. We conject that CO2-III and CO2-VII belong to the same phase, with the discrepancies in their experimental Raman spectra primarily caused by slight structural changes due to the thermal effect. This work provides deeper insights into CO2 phase transitions and establishes a generalizable strategy for high-precision MLPs in complex rare event systems.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".