The Abinitproject: Impact, environment and recent developments
Bibliographic record
Abstract
Abinit is a material- and nanostructure-oriented package that implements density-functional theory (DFT) and many-body perturbation theory (MBPT) to find, from first principles , numerous properties including total energy, electronic structure, vibrational and thermodynamic properties, different dielectric and non-linear optical properties , and related spectra. In the special issue to celebrate the 40th anniversary of CPC, published in 2009, a detailed account of Abinit was included [Gonze et al. (2009)], and has been amply cited. The present article comes as a follow-up to this 2009 publication. It includes an analysis of the impact that Abinit has had, through for example the bibliometric indicators of the 2009 publication. Links with several other computational materials science projects are described. This article also covers the new capabilities of Abinit that have been implemented during the last three years, complementing a recent update of the 2009 article published in 2016. Physical and technical developments inside the abinit application are covered, as well as developments provided with the Abinit package, such as the multibinit and a-tdep projects, and related Abinit organization developments such as AbiPy . The new developments are described with relevant references, input variables, tests, and tutorials. Program summary Program Title: Abinit Program Files doi: http://dx.doi.org/10.17632/csvdrr4d68.1 Licensing provisions: GPLv3 Programming language: Fortran2003, Python Journal reference of previous version: X .Gonze et al, Comput. Phys. Commun. 205 (2016) 106–131 Does the new version supersede the previous version?: Yes. The present 8.10.3 version is now the up-to-date stable version of abinit , and supercedes the 7.10.5 version. Reasons for the new version: New developments Summary of revisions: • Many new capabilities of the main abinit application, related to density-functional theory, density-functional perturbation theory , GW, the Bethe-Salpeter equation, dynamical mean-field theory, etc. • New applications in the package: multibinit (second-principles calculations)and tdep (temperature-dependent properties) Nature of problem: Computing accurately material and nanostructure properties: electronic structure, bond lengths, bond angles, primitive cell , cohesive energy , dielectric properties , vibrational properties, elastic properties , optical properties , magnetic properties , non-linear couplings, electronic and vibrational lifetimes, etc. For large-scale systems, second-principles calculations, building upon the first-principles results, are also possible. Solution method: Software application based on density-functional theory and many-body perturbation theory , pseudopotentials , with plane waves or wavelets as basis functions. Different real-time algorithms are implemented for second-principles calculations.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.018 |
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".