Bibliographic record
Abstract
Introduction In the preface to his book on the subject, Peierls says of the quantum theory of solids [Pei64] [It] has sometimes the reputation of being rather less respectable than other branches of modern theoretical physics. The reason for this view is that the dynamics of many-body systems, with which it is concerned, cannot be handled without the use of simplifications, or without approximations which often omit essential features of the problem. This book, in essence, is all about ways to further approximate the quantum theory of solids; it is about ways to build approximate models that describe the collective behavior of quantum mechanical atoms in materials. Whether it is the development of density functional theory (DFT), empirical classical interatomic potentials, continuum constitutive laws or multiscale methods, one can only conclude that, by Peierls' measure, modeling materials is a science that is a great deal less respectable than even the quantum theory of solids itself. However, Peierls goes on to say that Nevertheless, the [quantum] theory [of solids] contains a good deal of work of intrinsic interest in which one can either develop a solution convincingly from first principles, or at least give a clear picture of the features which have been omitted and therefore discuss qualitatively the changes which they are likely to cause. It is our view that these redeeming qualities are equally applicable to the modern science of modeling materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".