Quantum mechanical free energy profiles with post-quantization restraints: Binding free energy of the water dimer over a broad range of temperatures
Bibliographic record
Abstract
Free energy calculations are a crucial part of understanding chemical systems but are often computationally expensive for all but the simplest of systems. Various enhanced sampling techniques have been developed to improve the efficiency of these calculations in numerical simulations. However, the majority of these approaches have been applied using classical molecular dynamics. There are many situations where nuclear quantum effects impact the system of interest and a classical description fails to capture these details. In this work, path integral molecular dynamics has been used in conjunction with umbrella sampling, and it has been observed that correct results are only obtained when the umbrella sampling potential is applied to a single path integral bead post quantization. This method has been validated against a Lennard-Jones benchmark system before being applied to the more complicated water dimer system over a broad range of temperatures. Free energy profiles are obtained, and these are utilized in the calculation of the second virial coefficient as well as the change in free energy from the separated water monomers to the dimer. Comparisons to experimental and ground state calculation values from the literature are made for the second virial coefficient at higher temperature and the dissociation energy of the dimer in the ground state.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".