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Record W6884623411 · doi:10.1107/s002188905017358

Quantum software interfaced with crystal-structure databases: tools, results and perspectives

2005· article· en· W6884623411 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2005
Typearticle
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsnot available
Fundersnot available
KeywordsASCIISoftwareDecompositionMicrosoft WindowsEnergy (signal processing)File Transfer ProtocolFile format

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.242
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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