Gradient-Based Optimization of Force Field Parameters for Martini Lipid Models
Bibliographic record
Abstract
Physics-based coarse-grained (CG) models are widely used in (bio)molecular simulations, yet their parameterization remains challenging and labor-intensive. In this work, we demonstrate how recently developed gradient-based optimization methods can substantially accelerate the refinement of CG force field (FF) parameters within the Martini framework, a cornerstone of modern CG modeling. Using Martini lipid models as a testbed, we explore three increasingly challenging optimization problems. First, a single phosphatidylcholine (PC) lipid, where we optimize bonded parameters by fitting simultaneously to top-down membrane observables (area per lipid, bilayer thickness, and transition temperature) and bottom-up atomistic bond and angle distributions. We then scale the workflow to eight different PC lipids, demonstrating robust performance in a complex multi-system landscape and an order-of-magnitude reduction in computational cost relative to population-based heuristic schemes. Finally, to evaluate performance in higher-dimensional parameter spaces, we additionally optimize the non-bonded parameters of the solvent-free Dry Martini FF, demonstrating that the method scales effectively with increasing numbers of adjustable parameters. Collectively, these results suggest that the large, expert-coordinated workflows that have historically characterized the parameterization of community-driven CG models like Martini can be efficiently streamlined by fully automated, objective-driven gradient-based optimizations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".