Large-scale <i>ab initio</i> simulations of MAS DNP enhancements using a Monte Carlo optimization strategy
Bibliographic record
Abstract
Magic-angle-spinning (MAS) dynamic nuclear polarization (DNP) has recently emerged as a powerful technology enabling otherwise unrealistic solid-state NMR experiments. The simulation of DNP processes which might, for example, aid in refining the experimental conditions or the design of better performing polarizing agents, is, however, plagued with significant challenges, often limiting the system size to only 3 spins. Here, we present the first approach to fully ab initio large-scale simulations of MAS DNP enhancements. The Landau-Zener equation is used to treat all interactions concerning electron spins, and the low-order correlations in the Liouville space method is used to accurately treat the spin diffusion, as well as its MAS speed dependence. As the propagator cannot be stored, a Monte Carlo optimization method is used to determine the steady-state enhancement factors. This new software is employed to investigate the MAS speed dependence of the enhancement factors in large spin systems where spin diffusion is of importance, as well as to investigate the impacts of solvent and polarizing agent deuteration on the performance of MAS DNP.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".