Standardizing Force Reconstruction in Dynamic Atomic Force Microscopy
Bibliographic record
Abstract
Atomic force microscopy (AFM) enables high-resolution imaging and quantitative force measurement, which are critical for understanding nanoscale mechanical, chemical, and biological interactions. In dynamic AFM modes, however, interaction forces are not directly measured; they must be mathematically reconstructed from observables such as the amplitude, phase, or frequency shift. Many reconstruction techniques have been proposed over the last two decades, but they rely on different assumptions and have been applied inconsistently, limiting reproducibility and cross-study comparison. Here, we systematically evaluate major force reconstruction methods in both frequency- and amplitude-modulation AFM, detailing their theoretical foundations, performance regimes, and sources of error. To support benchmarking and reproducibility, we introduce an open-source software package that unifies all widely used methods, enabling side-by-side comparisons across different formulations. This work represents a critical step toward achieving consistent and interpretable AFM force spectroscopy, thereby supporting the more reliable application of AFM in fields ranging from materials science to biophysics.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".