Molecular Dynamics for Two-Body Potential from Unobserved Gaussian Regression
Bibliographic record
Abstract
A molecular dynamic simulation requires the local potential energy per atom because it provides the force applied to each atom. On the other hand, an electronic structure computation provides only the global potential energy of an atomic system. We propose a stochastic fitting for deducing a potential from a total energy which can be incorporated inside a molecular dynamic program. The objective of that fitting process is twofold. First, the total energy can be reproduced with a sufficient accuracy. In addition, we want to separate the potential energy into atomic contributions. The only inputs in the stochastic regression are the total energies of the atomic systems. As for the applications, we examine the performance of the method with the help of a mathematical model. Afterward, we use it for quantum applications in which we compare the direct method and the unobserved fitting with respect to the energy conservations. For a molecular dynamic application, we examine the atomic cluster formations during freezing when the proposed potential is used. Although the method is generally applicable, we restrict in this paper to total energies which are reproduced from two-body potentials for the molecular dynamic simulation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".