Atomistic Monte Carlo Simulations of Bio-molecular Systems
Bibliographic record
Abstract
The CECAM school “Atomistic Monte Carlo Simulations of Bio-molecular Systems” took place at the Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany, from 22 to 26 September 2025. A total of 19 scientists from 8 countries attended the school.The school started with a beginner level introduction to the theory of modern Monte Carlo (MC) methods, given by guest lecturer Prof. Anders Irbäck from Lund University, Sweden. Through alternating theory and hands-on sessions, the participants consolidated their understanding of MC techniques and their application to biological macro-molecules. The participants learned to set up their own protein folding simulations using different MC algorithms implemented in the software package ProFASi, and ran them on the JSC cluster JUSUF. ProFASi is an open source project actively developed by the organizers from the Simulation and Data Laboratory Biology at JSC. Guest lecturer, Prof. Stefan Wallin from Memorial University, Canada, demonstrated how the software may be extended and deployed for scientific research on MC techniques and application, beyond the research interests of the primary developers. Participants also presented posters describing their current research work, and, by means of intensive discussions with experienced MC researchers, generated exciting new ideas regarding the application of MC techniques in their research domains.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".