How Do We Simulate Things at the Scale of Molecules and Electrons?  An Introduction to the Technology and HPC Aspects of Computational Chemistry
Bibliographic record
Abstract
Summary form only given. Computer simulations combined with high performance computing have benefited many areas of science and engineering. Chemistry is no different. Computational chemistry involves simulating systems at the atomic and electronic level. When examining individual molecules at this microscopic scale, quantum mechanical effects are important. As a result, to get the physics 'right' we need to solve the quantum mechanical Schrodinger equation or equivalent to properly describe these systems. In this presentation, a brief introduction to the technology of modern computational chemistry and molecular scale modeling will be given. Additionally, some examples how computational chemistry has provided important insights into chemical processes is presented. A study of how anti-wear engine oil additives function at the molecular level in automobile engines is provided (Mosey, Mueser, Woo Science, 2005, 207, 1612) and our recent efforts to search in silico for new pure nitrogen analogues of diamond at high pressure is given.(Phys. Rev. Lett. 2006, 97, 155503)
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".