Symmetry-general <i>ab initio</i> computation of physical properties using quantum software integrated with crystal structure databases: results and perspectives
Why this work is in the frame
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Bibliographic record
Abstract
The timely integration of crystal structure databases, such as CRYSTMET, ICSD etc., with quantum software, like VASP, OresteS, ElectrA etc., allows ab initio cell and structure optimization on existing pure-phase compounds to be performed seamlessly with just a few mouse clicks. Application to the optimization of rough structure models, and possibly new atomic arrangements, is detailed. The ability to reproduce observed cell data can lead to an assessment of the intrinsic plausibility of a structure model, even without a competing model. The accuracy of optimized atom positions is analogous to that from routine powder studies. Recently, the ab initio symmetry-general least-squares extraction of the coefficients of the elastic tensor for pure-phase materials using data from corresponding entries in crystal structure databases was automated. A selection of highly encouraging results is presented, stressing the complementarity of simulation and experiment. Additional physical properties also appear to be computable using existing quantum software under the guidance of an automation scheme designed following the above automation for the elastic tensor. This possibility creates the exciting perspective of mining crystal structure databases for new materials with combinations of physical properties that were never measured before. Crystal structure databases can accordingly be expected to become the cornerstone of materials science research within a very few years, adding immense practical value to the archived structure data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it