HlightReaxMD: A Machine Learning-Augmented Multiscale Analysis Framework for Radiation Chemistry Dynamics and Damage Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Molecular dynamics (MD) simulations are currently widely used to study large-scale displacement cascades based on massive simulation trajectories. However, when the irradiation process involves the complex chemical reactions, effectively analyzing and extracting features from these data becomes a challenge. Here, we introduce a new cross-platform toolkit, HlightReaxMD, designed to directly obtain information about the irradiation damage process and chemical reactions from MD trajectories and further achieve the prediction of irradiation damage. The analysis tools in HlightReaxMD include chemical reaction analysis and calculation of reaction kinetic parameters, analysis of the collision cascade process, and calculation of necessary physicochemical properties. HlightReaxMD supports the analysis of all elements used in reactive force fields by reading ReaxFF potential file parameters and provides an automated solution for tracking atomic-scale collision events and analyzing chemical reaction mechanisms through constructing cascade trees and reaction network paths. A machine learning-driven model using the analysis results has been included in HlightReaxMD, which can predict irradiation damage by considering various factors, rather than relying solely on the Norgett-Robinson-Torrens displacements per atom (NRT-dpa) model. It enables researchers to automatically obtain dynamic processes and reaction information from atomic to microscale defects from terabyte-level trajectory data. Thus, HlightReaxMD can promote systematic research on irradiation effects in materials science.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it