Rationalization of Structures of Binary Alloys in a Real Space Atomic Level Perspective
Bibliographic record
Abstract
The understanding of the structure and bonding of intermetallic phases poses a unique challenge to theory. On one hand, first-principles calculations may be used to obtain accurate physical properties, on the other hand, the fundamental principles governing the observed structures is still unknown and often limited to simple models such as the Zintl concept or Meidema's rules. In this paper, a real space theoretical framework based on the analysis of the electronic density of states (DOS) in terms of its moments is proposed to bridge the gap between these two extremes. The moments of DOS are directly related to local structural motifs, thus allowing the identification of basic stabilizing structural features as a function of electron concentration. This approach is used to interpret the binary Al-Li phase diagram and the structural stability of high pressure phases of K-Ag alloy. Since there is almost complete electron transfer from the alkali metal (Li and K) to Al and Ag in these alloys, the main factor governing the structural stability is the effective number of valence electrons on Al and Ag that directly determines the structural motifs. Thus, the structure, stability and phase boundary of alloy phases can be rationalized in a systematic manner. The principles developed here can be extended to make reliable predictions of the structure and stability of new intermetallic phases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".