Optimization of the Kinetic Activation-Relaxation Technique, an off-lattice and self-learning kinetic Monte-Carlo method
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Bibliographic record
Abstract
We present two major optimizations for the kinetic Activation-Relaxation Technique (k-ART), an off-lattice self-learning kinetic Monte Carlo (KMC) algorithm with on-the-fly event search THAT has been successfully applied to study a number of semiconducting and metallic systems. K-ART is parallelized in a non-trivial way: A master process uses several worker processes to perform independent event searches for possible events, while all bookkeeping and the actual simulation is performed by the master process. Depending on the complexity of the system studied, the parallelization scales well for tens to more than one hundred processes. For dealing with large systems, we present a near order 1 implementation. Techniques such as Verlet lists, cell decomposition and partial force calculations are implemented, and the CPU time per time step scales sublinearly with the number of particles, providing an efficient use of computational resources.
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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 | 0.000 | 0.000 |
| 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