Toward interactive scanning tunneling microscopy simulations of large-scale molecular systems in real time
Bibliographic record
Abstract
We have developed a simulation tool in which structural or chemical modifications of an adsorbed molecular layer can be interactively performed, and where structural relaxation and nearly real-time evaluation of a scanning tunneling microscopy (STM) image are considered. This approach is built from an optimized integration of the atomic superposition and electron delocalization molecular orbital theory (ASED-MO) to which a van der Waals correction term is added in conjunction with a non-linear optimization algorithm based on the Broyden-Fletcher-Goldfarb-Shanno method. This integrated approach provides reliable optimized geometries for adsorbed species on metallic surfaces in a reasonable time. Although we performed a major revision of the ASED-MO parameters, the proposed computational approach can accurately reproduce the geometries of a various amount of covalent molecules and weakly bonded complexes contained in two well-defined datasets. More importantly, the relaxation of adsorbed species on a metal surface leads to molecular geometries in good agreement with experimental and Density Functional Theory results. From this, the electronic structure obtained from ASED-MO is used to compute the STM image of the system nearly in real-time using the Tersoff-Hamann formalism. We developed a parallelization strategy that uses Graphics Processing Units to reduce the computing time of STM simulation by a factor of 30. Such improvements allow one to simulate STM images of large supramolecular arrangements and to investigate the influence of realistic local chemical or structural defects on metal surfaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".