[REVIEW]: AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification #5035

editorialbot · 2022-12-28T15:29:19Z

Submitting author: @ajmedford (Andrew Medford)
Repository: https://github.com/ulissigroup/amptorch
Branch with paper.md (empty if default branch): master
Version: v1.0
Editor: @dhhagan
Reviewers: @ml-evs, @ianfhunter
Archive: 10.5281/zenodo.8151492

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The text was updated successfully, but these errors were encountered:

editorialbot · 2022-12-28T15:29:20Z

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editorialbot · 2022-12-28T15:29:23Z

Software report:

github.com/AlDanial/cloc v 1.88  T=0.26 s (824.6 files/s, 166125.9 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
C++                             10           7091           1998          24319
Python                          48            982            474           5913
C/C++ Header                     9            161             60            719
ANTLR Grammar                  132              0              0            595
reStructuredText                 4             67             13            195
Markdown                         2             32              0            181
YAML                             6              8              9            140
TeX                              1              7              0             93
DOS Batch                        1              8              1             26
make                             1              4              7              9
-------------------------------------------------------------------------------
SUM:                           214           8360           2562          32190
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

editorialbot · 2022-12-28T15:29:24Z

Wordcount for paper.md is 1074

editorialbot · 2022-12-28T15:29:51Z

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

OK DOIs

- 10.1016/j.cpc.2016.05.010 is OK
- 10.48550/arxiv.1908.08381 is OK
- 10.1021/acs.jpcc.0c04225 is OK
- 10.1088/1361-648X/AA680E is OK

MISSING DOIs

- 10.21203/rs.3.rs-952157/v1 may be a valid DOI for title: A Universal Framework for Featurization of Atomistic Systems
- 10.1088/2632-2153/ac8fe0 may be a valid DOI for title: FINETUNA: Fine-tuning Accelerated Molecular Simulations

INVALID DOIs

- 10.1021/ACSCATAL.0C04525/SUPPL\_FILE/CS0C04525\_SI\_001.PDF URL is INVALID

editorialbot · 2022-12-28T15:30:52Z

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

ml-evs · 2022-12-28T15:54:34Z

Review checklist for @ml-evs

Conflict of interest

I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

I confirm that I read and will adhere to the JOSS code of conduct.

General checks

Repository: Is the source code for this software available at the https://github.com/ulissigroup/amptorch?
License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
Contribution and authorship: Has the submitting author (@ajmedford) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

Installation: Does installation proceed as outlined in the documentation?
Functionality: Have the functional claims of the software been confirmed?
Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
State of the field: Do the authors describe how this software compares to other commonly-used packages?
Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

ml-evs · 2023-01-16T12:35:41Z

I started my review a few weeks ago but am still having installation issues raised at ulissigroup/amptorch#108, @ajmedford could you please take a look?

ajmedford · 2023-01-17T21:10:54Z

@ml-evs Thanks for your efforts on this, and apologies for the delay. I just posted a note to ulissigroup/amptorch#108, we would like your advice on how to best proceed.

dhhagan · 2023-02-03T16:34:34Z

@professoralkmin Sorry for the delay here - you can go ahead and generate your checklist by adding a comment with @editorialbot generate my checklist

ml-evs · 2023-03-13T10:53:09Z

Just pinging @ajmedford in case it was missed, I raised an issue with some comments on the code and documentation at ulissigroup/amptorch#112.

dhhagan · 2023-03-14T12:07:31Z

Hey @professoralkmin - I just wanted to ping you and see if you had any questions about proceeding with the review?

ajmedford · 2023-03-14T12:55:04Z

@ml-evs - Thanks for the reminder and suggestions. Nicole has been working on this, and fixed a few more things along the way. We're hoping to have it turned around by the end of the week.

dhhagan · 2023-03-21T13:22:33Z

👋 @hiendn would you be willing to review this manuscript?

ajmedford · 2023-03-21T13:24:56Z

@dhhagan - We are in the process of a large revision to the code based on @ml-evs feedback, and expect to have the new version out in the next few days. I am not sure of the exact workflow of the revision process, but it might make sense to wait for these changes to the code before the next reviewer takes a look. Let me know what you think.

ajmedford · 2023-03-23T21:29:09Z

@ml-evs @dhhagan Thank you for taking the time to review the code and for providing valuable feedback. We appreciate your thoughtful comments and suggestions, which have helped us to improve the quality of our work. Given the comments, @nicoleyghu has led the implementation of a number of changes, which are listed below.

DONE

Re-organized the example folder and provided a readme.md file that walks through the general structure to energy (and forces) tasks for the two main fingerprinting + neural network models, and constructing and training with lmdb files. (Comment #3)
Completed the documentation for main functions and classes for the module and integrated it into the documentation (see API here). (Comment #5)
Made a default method for loading fitted pseudo-densities so the general users don't have to import it manually while allowing importing if specifically specified. (Comment #6)
Imported all test scripts into unittest, and made all relevant changes. The tests and examples should work now. (Comment #7)

There are also a few points that we have not completed yet, and the discussion is below:

[DOING] Comments #1 and #2: We are currently working on integrating the dataclass(es) for config of AtomsTrainer into the code, but as it requires refactoring how the code loads from Python Dictionary, it would probably happen for a later release.
Comment #2: We hope the example scripts now provide a better starting point for the users as to GMP+SingleNN or SF+BPNN's training schemes.
Comment #3: The key findings in GMP and Uncertainty Quantification paper require training with a larger dataset that is a bit difficult to incorporate into a demo, but the general training pertains to what is shown in the examples folder just with hyperparameter optimized for the given task. Hopefully this is sufficient.
Comment #8: We would like to finalize a release after hearing the feedback from reviewer(s).

Thank you again for your time and effort in reviewing our code and we look forward to your continued feedback. @dhhagan - please advise on the remainder of the peer review process. We would like to wait until the paper is fully "accepted" and then finalize the next release.

dhhagan · 2023-03-28T13:48:55Z

@ajmedford Just to clarify, you would like the review to cover v0.1 and not the new release, correct? I am still trying to find a second reviewer as one seems to have vanished. Otherwise, @ml-evs should be able to go through the changes you listed above and finalize their review.

ajmedford · 2023-03-28T13:54:41Z

@dhhagan We would like to have reviewers look at the current master branch. Once all the reviews are finalized, we will then release a new version that corresponds to the "accepted" paper. Let me know if this makes sense, or if you recommend something else.

dhhagan · 2023-04-16T22:39:36Z

Hi @ml-evs, have you had a chance to look over any of the changes at this point?

dhhagan · 2023-04-16T22:42:49Z

👋 @pmeier @ianfhunter - are either of you able to review this manuscript?

dhhagan · 2023-07-21T13:17:07Z

@editorialbot set 10.5281/zenodo.8151492 as archive

editorialbot · 2023-07-21T13:17:09Z

Done! archive is now 10.5281/zenodo.8151492

dhhagan · 2023-07-21T13:17:17Z

@editorialbot generate pdf

editorialbot · 2023-07-21T13:18:44Z

⚠️ An error happened when generating the pdf.

dhhagan · 2023-07-21T13:20:51Z

@editorialbot generate pdf

dhhagan · 2023-07-21T13:21:18Z

⚠️ An error happened when generating the pdf.

Looks like there is an issue with one of the services used to build the pdf. Will try again and try again later today if needed.

editorialbot · 2023-07-21T13:22:26Z

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

dhhagan · 2023-07-21T13:23:10Z

@editorialbot recommend-accept

editorialbot · 2023-07-21T13:23:12Z

Attempting dry run of processing paper acceptance...

editorialbot · 2023-07-21T13:23:15Z

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

OK DOIs

- 10.1021/acs.jpclett.2c02100 is OK
- 10.1088/2632-2153/ACA7B1 is OK
- 10.1016/j.cpc.2016.05.010 is OK
- 10.48550/arxiv.1908.08381 is OK
- 10.1088/2632-2153/ac8fe0 is OK
- 10.1021/acs.jpcc.0c04225 is OK
- 10.1002/qua.24890 is OK
- 10.1088/1361-648X/AA680E is OK
- 10.1021/acscatal.0c04525 is OK

MISSING DOIs

- None

INVALID DOIs

- None

editorialbot · 2023-07-21T13:24:28Z

⚠️ Error preparing paper acceptance.

ajmedford · 2023-07-21T17:28:14Z

@dhhagan - not sure what's causing this error. Let us know if any action is needed from our side.

dhhagan · 2023-07-25T13:23:16Z

@editorialbot recommend-accept

editorialbot · 2023-07-25T13:23:17Z

Attempting dry run of processing paper acceptance...

editorialbot · 2023-07-25T13:23:21Z

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

OK DOIs

- 10.1021/acs.jpclett.2c02100 is OK
- 10.1088/2632-2153/ACA7B1 is OK
- 10.1016/j.cpc.2016.05.010 is OK
- 10.48550/arxiv.1908.08381 is OK
- 10.1088/2632-2153/ac8fe0 is OK
- 10.1021/acs.jpcc.0c04225 is OK
- 10.1002/qua.24890 is OK
- 10.1088/1361-648X/AA680E is OK
- 10.1021/acscatal.0c04525 is OK

MISSING DOIs

- None

INVALID DOIs

- None

editorialbot · 2023-07-25T13:24:47Z

👋 @openjournals/dsais-eics, this paper is ready to be accepted and published.

Check final proof 👉📄 Download article

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ajmedford · 2023-07-25T15:32:44Z

Looks good to me!

gkthiruvathukal · 2023-07-26T13:28:31Z

Everything looks to be in good shape here. I'm proceeding with final acceptance.

gkthiruvathukal · 2023-07-26T13:28:37Z

@editorialbot accept

editorialbot · 2023-07-26T13:28:38Z

Doing it live! Attempting automated processing of paper acceptance...

editorialbot · 2023-07-26T13:30:51Z

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  orcid: "https://orcid.org/0000-0003-3648-7749"
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  given-names: Xiangyun
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  given-names: Benjamin M.
- family-names: Adams
  given-names: Matt
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  given-names: Jacob
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  orcid: "https://orcid.org/0000-0003-2855-9482"
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  given-names: Andrew J.
  orcid: "https://orcid.org/0000-0001-8311-9581"
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  orcid: "https://orcid.org/0000-0002-9401-4918"
contact:
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  orcid: "https://orcid.org/0000-0002-9401-4918"
doi: 10.5281/zenodo.8151492
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Shuaibi
    given-names: Muhammed
  - family-names: Hu
    given-names: Yuge
    orcid: "https://orcid.org/0000-0003-3648-7749"
  - family-names: Lei
    given-names: Xiangyun
  - family-names: Comer
    given-names: Benjamin M.
  - family-names: Adams
    given-names: Matt
  - family-names: Paras
    given-names: Jacob
  - family-names: Chen
    given-names: Rui Qi
  - family-names: Musa
    given-names: Eric
  - family-names: Musielewicz
    given-names: Joseph
  - family-names: Peterson
    given-names: Andrew A.
    orcid: "https://orcid.org/0000-0003-2855-9482"
  - family-names: Medford
    given-names: Andrew J.
    orcid: "https://orcid.org/0000-0001-8311-9581"
  - family-names: Ulissi
    given-names: Zachary
    orcid: "https://orcid.org/0000-0002-9401-4918"
  date-published: 2023-07-26
  doi: 10.21105/joss.05035
  issn: 2475-9066
  issue: 87
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 5035
  title: "AmpTorch: A Python package for scalable fingerprint-based
    neural network training on multi-element systems with integrated
    uncertainty quantification"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.05035"
  volume: 8
title: "AmpTorch: A Python package for scalable fingerprint-based neural
  network training on multi-element systems with integrated uncertainty
  quantification"

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editorialbot · 2023-07-26T13:30:57Z

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editorialbot added C C++ Python review Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning waitlisted Submissions in the JOSS backlog due to reduced service mode. labels Dec 28, 2022

editorialbot assigned dhhagan Dec 28, 2022

editorialbot mentioned this issue Dec 28, 2022

[PRE REVIEW]: AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification #4698

Closed

This was referenced Dec 28, 2022

[JOSS REVIEW] Support for Python>3.6 ulissigroup/amptorch#108

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Duplicate ulissigroup/amptorch#109

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ml-evs mentioned this issue Feb 10, 2023

[JOSS review] Code feedback ulissigroup/amptorch#112

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editorialbot added the recommend-accept Papers recommended for acceptance in JOSS. label Jul 25, 2023

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editorialbot mentioned this issue Sep 9, 2024

[PRE REVIEW]: Salt: Multimodal Multitask Machine Learning for High Energy Physics #6543

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[PRE REVIEW]: flory: A Python package for finding coexisting phases in multicomponent mixtures #7304

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[REVIEW]: AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification #5035

[REVIEW]: AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification #5035

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Review checklist for @ml-evs

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Software paper

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