Quantum software interfaced with crystal-structure databases: tools, results and perspectives
Bibliographic record
Abstract
Version 2.0 of Toth's Materials Toolkit runs under Windows and prepares ASCII input files for popular ab initio packages such as ABINIT, VASP etc. Those packages, obtainable from their respective developers, may run in desktop or supercomputer setups with Linux or Windows operating systems. The Toolkit input is taken at will from a direct plug into CRYSTMET, with 93000 crystal-structure entries for metals and inorganic compounds, from CIF files of public-domain crystal-structure databases, or cut-and-paste from electronic journals followed by minimal free-format editing. The collection of fully general and highly graphical tools grouped on two command screens operates on the structure description stored in an editable ASCII screen. After the model has been searched, modified and evaluated in a few keystrokes with the above tools, its ASCII input files for a selection of ab initio packages are produced by selecting the meaningful flags and run options on a dialog. The tedious structure manipulation or decomposition into multiple simulations is performed in the background. Execution is followed by production of a plain-English job report. Four examples among the numerous possible applications of the Toolkit illustrate the fact that daunting topics, like the symmetry of chlorapatite, the voids and channels in the hydrogen-storage material EuNi5, the energy per unit area of the contact plane for spinel twin in diamond, and the hardness of lonsdaleite versus diamond, are amenable to processing by materials scientists more versed in experiment than theory.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.037 |
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".