Implementing a non-local lattice particle method in the open-source large-scale atomic/molecular massively parallel simulator
Bibliographic record
Abstract
Abstract Using conventional continuum-based simulation frameworks to model crack initiation and extension can be computationally challenging. As an alternative to continuum-based approaches, particle-based simulation methods are well-suited to handle the discontinuities present during fracture propagation. A well-known particle-based method is the lattice particle method (LPM), which discretizes the system into a set of interconnected particles ollowing a periodic arrangement. Discontinuities can be handled simply by removing bonds between particles. For this reason, LPM-based simulations have been employed to simulate fracture propagation in heterogeneous media, notably in civil engineering and biomaterials applications. However, a practical limitation of this method is the absence of implementation within a commonly-used software platform. This work describes such an implementation of a non-local LPM within the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). Specifically, we implemented a new LAMMPS bond style with a many-body term to tune Poisson’s ratios. In order to validate the nonlocal formalism and our implementation of this method within LAMMPS, simulated elastic properties are compared to analytical solutions reported in the literature. Good agreement between simulated and analytical values is found for systems with positive Poisson’s ratios. The computational and parallel efficiency of the LPM-LAMMPS implementation is also benchmarked. Finally, we compare the elastic response of a 3D porous structure and an aircraft wing as calculated using the LPM and finite-element analysis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".