[PRE REVIEW]: Machine Learning Validation via Rational Dataset Sampling with astartes

[editorialbot]

Submitting author: @JacksonBurns (Jackson Burns)
Repository: https://github.com/JacksonBurns/astartes
Branch with paper.md (empty if default branch): joss-paper
Version: v1.1.3.post1
Editor: @arfon
Reviewers: @du-phan, @BerylKanali
Managing EiC: Arfon Smith

Status

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Software report:

github.com/AlDanial/cloc v 1.88  T=0.15 s (632.6 files/s, 257230.7 lines/s)
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Language                     files          blank        comment           code
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JavaScript                      12           2405           2470           9221
Python                          41            499            683           3771
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Jupyter Notebook                 6              0          13325            956
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DOS Batch                        1              8              1             26
make                             1              4              7              9
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SUM:                            96           3439          16666          18928
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gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 1917

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.5281/zenodo.6618262 is OK

MISSING DOIs

- 10.1021/acs.chemmater.0c01907.s001 may be a valid DOI for title: Machine Learning for Materials Scientists: An Introductory Guide Toward Best Practices
- 10.1038/sdata.2014.22 may be a valid DOI for title: Quantum Chemistry Structures and Properties of 134 Kilo Molecules
- 10.1021/ci300415d may be a valid DOI for title: Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
- 10.1038/s41597-022-01529-6 may be a valid DOI for title: High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions
- 10.1021/acs.jpca.2c02614 may be a valid DOI for title: Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy
- 10.1039/d1cp04422b may be a valid DOI for title: Progress Towards Machine Learning Reaction Rate Constants
- 10.1039/c8me00012c may be a valid DOI for title: Can Machine Learning Identify the Next High-Temperature Superconductor? Examining Extrapolation Performance for Materials Discovery
- 10.1039/d2dd00039c may be a valid DOI for title: Random Projections and Kernelised Leave One Cluster Out Cross Validation: Universal Baselines and Evaluation Tools for Supervised Machine Learning of Material Properties
- 10.26434/chemrxiv-2022-m8l33 may be a valid DOI for title: Construction of Balanced, Chemically Dissimilar Training, Validation and Test Sets for Machine Learning on Molecular Datasets
- 10.1039/d2sc06150c may be a valid DOI for title: Low-Cost Machine Learning Prediction of Excited State Properties of Iridium-Centered Phosphors
- 10.1063/5.0079574 may be a valid DOI for title: Quantum Chemistry-Augmented Neural Networks for Reactivity Prediction: Performance, Generalizability, and Explainability
- 10.1063/5.0059742 may be a valid DOI for title: Toward the Design of Chemical Reactions: Machine Learning Barriers of Competing Mechanisms in Reactant Space
- 10.2139/ssrn.4289793 may be a valid DOI for title: Machine Learning for Predicting the Viscosity of Binary Liquid Mixtures
- 10.26434/chemrxiv.12758498 may be a valid DOI for title: Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies
- 10.1021/jm9602928 may be a valid DOI for title: The Properties of Known Drugs. 1. Molecular Frameworks
- 10.1021/c160017a018 may be a valid DOI for title: The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service
- 10.1021/ci100050t may be a valid DOI for title: Extended-Connectivity Fingerprints
- 10.1021/acs.jcim.9b00237.s001 may be a valid DOI for title: Analyzing Learned Molecular Representations for Property Prediction

INVALID DOIs

- https://doi.org/10.1016/j.cpc.2022.108579 is INVALID because of 'https://doi.org/' prefix

[arfon]

Noting that this submission was reviewed by pyOpenSci here: pyOpenSci/software-submission#120

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[editorialbot]

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[arfon]

@lwasser – for rOpenSci submissions we've started trying to list the reviewers as the reviewers here too. We could do the same for pyOpenSci too. Does that sound good to you too @BerylKanali @du-phan (this will associate your GitHub handles with the JOSS paper).

[JacksonBurns]

The following reviewers seem suited to this review (links to their reviewer profiles on JOSS):

• Kehan Wang
• Luis Itza Vazquez-Salazar
• Marjan Albooyeh

[BerylKanali]

@lwasser – for rOpenSci submissions we've started trying to list the reviewers as the reviewers here too. We could do the same for pyOpenSci too. Does that sound good to you too @BerylKanali @du-phan (this will associate your GitHub handles with the JOSS paper).

@arfon Sounds good to me.

[lwasser]

@arfon i second @BerylKanali reply - yes please do list our reviewers here. many thanks for recognizing them in the JOSS paper! Let's model your partnership with rOpenSci and make this a part of our pyOpenSci-JOSS partnership. If you need anything from me please say the word!
Many thanks.

[arfon]

@editorialbot add @BerylKanali as reviewer

[editorialbot]

Can't add reviewers: There is no editor assigned yet

[arfon]

@editorialbot assign me as editor

[editorialbot]

Assigned! @arfon is now the editor

[arfon]

@editorialbot add @du-phan as reviewer

[editorialbot]

@du-phan added to the reviewers list!

[arfon]

@editorialbot add @BerylKanali as reviewer

[editorialbot]

@BerylKanali added to the reviewers list!

[arfon]

@editorialbot start review

[editorialbot]

OK, I've started the review over in #5996.