Methods for Atomistic Simulations of Linear and Nonlinear Damping in Nanomechanical Resonators
Bibliographic record
Abstract
Atomistic simulations can be used to compute damping from first principles and gain unprecedented insights into the mechanisms of dissipation. However, the technique is still in its infancy and many foundational aspects remain unexplored. As a step toward addressing these issues, we present here a comparative study of five different methods for estimating damping under isothermal conditions. Classical molecular dynamics was used to simulate the fundamental longitudinal-mode oscillations of nanowires and nanofilms of silicon and nickel at room temperature (300 K) in the canonical ensemble using the Nosé-Hoover thermostat. In the subresonant regime, damping was quantified using the loss tangent and loss factor during steady-state harmonic vibration. The quality factor was obtained by analyzing the spectrum of thermomechanical noise and also from the Duffing-like nonlinearity in the frequency response under harmonic excitation. In addition, the nonlinear logarithmic decrement was obtained from the Hilbert transform of freely decaying oscillations. We discuss the factors that must be considered while selecting simulation parameters, establish criteria for convergence and linearity, and highlight the relative merits and limitations of each method.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".