Modelling Molecules in Intense Laser Fields - Numerical Methods for an IBM-SP2 Parallel Computer Environment
Bibliographic record
Abstract
The behaviour, control and manipulation of molecules exposed to ultrashort (femtosecond) intense (>10**14 W/cm2) is of current interest to the new femtosecond technology including ultrafast electronic and electromagnetic technologies. The theoretical modelling and simulation of the interaction of molecules with the new ultashort and intense laser pulses available today requires large scale computations as one is dealing with nonlinear nonperturbative phenomena coupling two fundamental partial differential equations, pdf, s, the hyperbolic Maxwell equations of electromagnetism and the quantum equations of Schroedinger and Dirac for the molecules. Most of our work in this area involving nonperturbative solutions of these equations which are of interest to modem photochemistry and photophysics has been performed on IBM-SP2, s with 16 processors at CACPUS and up to a hundred processors at the Maui High Performance Computing Center. Special high order numerical methods, based on split operator methods and large matrix Lanczos diagonaliztion preocedures have been developped to treat nonperturbatively such laser-molecule systems. We will describe in the present paper these new methods, their advantages over previous ones, their limitations and possible improvements compatible with new supercomputer technologies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".