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Record W6894178174 · doi:10.5281/zenodo.7874413

VaspGibbs: A simple way to obtain Gibbs free energy from Vasp calculations

2023· other· en· W6894178174 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMoment of inertiaVibrationGibbs free energyEnergy (signal processing)Simple (philosophy)Set (abstract data type)MoleculeSymmetry (geometry)Normal mode

Abstract

fetched live from OpenAlex

VaspGibbs A simple way to get Gibbs free energy from Vasp calculations Installation pip install vaspgibbs Latest version: 0.2.0 (beta) Usage In a folder with a finished vasp calculation, run vasp_gibbs vasp_gibbs will rerun Vasp to obtain vibration modes and output the gibbs free energy of your system. Use -o (only) or -t(top) to specify a set of atoms for which to calculate vibration modes. Examples: -o C O would only compute vibration modes associated with C and O keeping all other atoms fixed. -o 1 3 6 would compute vibration modes associated with the first, third and sixth atoms in the POSCAR keeping all other atoms fixed. -t 10 would compute vibration modes associated with the first 10 atoms starting from the top of the unit cell along the c axis. This can be useful when computing free energy differences between systems where one part of the system does not change, e.g. adsorption free energies. To run vasp in parallel call: vasp_gibbs -n [number of proc] -m [mpi executable] -v [vasp executable] By default srun and vasp_std are used. VaspGibbs will automatically compute the moment of inertia and symmetry of your molecule and compute rotational and translational contributions if you specify that the system is a molecule with the -m flag. The temperature and pressure can be set using the -T and -P flags. Output All outputs can be found in the VaspGibbs.md file. It contains the following information: Rotational properties Property Value Sigma x P. axes I~1 x eV/THz^2 I~2 x eV/THz^2 I~3 x eV/THz^2 Energy corrections Type Z E (eV) S (eV/K) F (eV) ZPE N/A x N/A N/A Electronic x x x x Vibrational x x x x Rotational x x x x Translational x x x x Thermodynamic Quantities Quantity Value Enthalpy x eV Entropy x eV/K Gibbs Free Energy x eV G - E_dft x eV TS x eV Online Ressources https://pubs.acs.org/doi/abs/10.1021/jp407468t (Supporting Information) https://gaussian.com/thermo/ https://wiki.fysik.dtu.dk/ase/ase/thermochemistry/thermochemistry.html https://chem.libretexts.org/Bookshelves/Physical_and_Theoretical_Chemistry_Textbook_Maps/Statistical_Thermodynamics_(Jeschke)/06%3A_Partition_Functions_of_Gases/6.04%3A_Rotational_Partition_Function https://vaspkit.com/tutorials.html#thermo-energy-correction https://uregina.ca/~eastalla/entropy.pdf (https://doi.org/10.1063/1.473958) Under development Results have been checked with J. Phys. Chem. C 2013, 117, 49. More validation needs to be done; use with care. Next steps: more testing, add to pypi, PV term for solids with Murnaghan equation, hindered translator and rotor?

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.354
Threshold uncertainty score0.921

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.005
Open science0.0100.004
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.3540.141

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 head0.037
GPT teacher head0.257
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreSoftware

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

Citations7
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)French-language works237,207