Criteria for Publications
APL Machine Learning considers manuscripts for publication that meet the following criteria:
- Contain high-quality original and novel research
- Present research that is timely and has significant potential to advance the field
- Report results that are well supported by appropriate data, methods, and analysis
- Describe research that is of interest to the broad machine learning for applied physics and applied physics for machine learning communities
Format and Content
APL Machine Learning is published quarterly (4 issues per year) by AIP Publishing. APL Machine Learning publishes an original research manuscript in the format of an Article. We also publish Reviews and Perspectives.
Articles contain novel and significant findings relevant to the majority of researchers in the field. The timeliness, relevance of the research, and clarity of presentation are important factors that we consider when evaluating Article submissions.
Although there is no length limit, manuscripts should be as concise as possible and present a clear description of the research. As a guideline, the main text of an Article (excluding title and references) should contain approximately 3500 words. Articles should include sufficient experimental information to allow other researchers to reproduce the reported results. Authors may include supplementary material, including video or other multimedia files. In all cases, the editors decide whether the length of an article is appropriate for the information presented.
Reviews published in APL Machine Learning are succinct overviews that detail recent progress in topics covered by the journal’s scope. Reviews are by invitation only and should be written in a way that enhances or introduces the work to researchers in the field.
Perspectives cover emerging topics or highlight a recent discovery. They provide a forward-looking discussion on the direction of a particular sub-field. Perspectives differ from Reviews in that they can present personal viewpoints from leaders in the field. These are by invitation only.
A Roadmap is a type of review article. This article type combines multiple sections, each written by different authors. The content of a Roadmap should focus on the status, advances, challenges, and future directions of subtopics within a field from multiple expert perspectives. As a united review, the Roadmap should provide a high-level overview of the field as a whole. Roadmaps should be composed when there is a need or opportunity for useful discussion and evaluation of the field. If you are interested in submitting a roadmap to APL Machine Learning please contact the editorial team for the appropriate next steps at firstname.lastname@example.org.
Tutorials have a strong educational focus and are meant to provide an overview of the fundamental principles and techniques which are important within a given field or sub-field. These articles should be particularly useful for students and/or senior researchers looking to increase their understanding of a field that is new to them. These are by invitation only.
Special topic issues are published occasionally and contain a grouping of Articles, Reviews, and/or Perspectives on a topic of current or emerging interest. These are intended to be reports of original research that significantly advance our understanding of the field. Editors review Special Topics using the usual publication criteria. Journal editors or guest editors may assemble a Special Topic.
Comments and Responses address scientific issues within articles published in the journal. Researchers considering a Comment are encouraged to directly contact the authors of the original article first, as comments will be published only if the same result cannot be achieved through publication of either an Erratum or a new article. We discourage Comments on questions of priority or calling attention to an oversight in a reference list.
Generally, the authors of the original article will be invited to submit a Response to the Comment, and the Comment and Response will be peer reviewed together. If the Comment and Response are both accepted, they will appear in the same journal issue. No further exchange beyond this point will be considered for publication.
Comments and Responses should be no longer than roughly 1000 words.
Errata are corrections of errors in previously published papers. These may be errors introduced in the publication process by the author or the publisher, or errors in the research that were discovered after the paper was published. Errata should be confined to specific errors. Further discussion or additional work that either confirms or denies previous work should be presented as a separate Article or Comment.
Data Availability & Reporting Standards
AIP Publishing believes that science should be shared as widely as possible, and we actively support sustainable models of access to research that ensure the permanence, discoverability, and reuse of published work. All data, code, methods, and models should be well documented and described either in the main text of the article or supporting information to provide the research community with enough transparency and detail to effectively replicate the findings.
APL Machine Learning requires that authors make any data, code, and additional supplementary material publicly available on a repository of the author’s choosing at the time of manuscript submission. Any reasons that the material cannot be made available to the readers should be disclosed and explained in detail to the editors.
The Review Process
The editor-in-chief, aided by the associate editors, is responsible for the content and editorial matters related to APL Machine Learning. To identify papers that meet the journal’s publication standards, the editors initially screen all submitted manuscripts. Manuscripts that pass the screening are evaluated by expert referees. Generally, two referees are sought, but decisions on publication may be made with additional reviews if required. Generally, we decide whether to publish a manuscript after one or two rounds of review. We will allow additional reviews if deemed necessary by the editors.
It is the authors’ responsibility to ensure that manuscripts are written clearly. A manuscript can be rejected if the scientific meaning is unclear due to poor English. Manuscripts that do not meet APL Machine Learning’s language standard will be returned to the authors for rewrite before peer review, during the review process and/or if provisionally accepted pending language editing.
Because good science has no value unless it is clearly communicated, AIP Publishing recommends that authors use AIPP Author Services to improve the quality of your paper’s written English. AIPP Author Services was developed in line with our commitment to diversity, equity and inclusion for all authors. Using this service ensures that your paper will be free of language deficiencies, so editors and reviewers will be able to fully understand your research during the review process. A native English-speaking subject matter expert of AIP Author Services will correct spelling, grammar and punctuation and verify the use and consistency of technical terms and content in your paper. Note that this is not a requirement or a guarantee of acceptance for review or publication.
If your manuscript is not accepted for publication in APL Machine Learning, an editor may recommend a transfer to another AIP Publishing journal for immediate consideration. In some cases, the transfers are offered after consultations with the editors of other AIP Publishing journals.
If you choose to transfer your manuscript, all reviewer reports and editor recommendations will be transferred along with the manuscript. Please visit the receiving journal's website for more information. Manuscripts must meet the receiving journal’s acceptance criteria. Note that there is no guarantee that the receiving journal will publish a transferred manuscript. A list of AIP Publishing journals and descriptions can be found here.
The Appeals Process
Authors may appeal a decision to reject a manuscript. To submit an appeal, authors can visit the APL Machine Learning submission site. To receive further consideration, the authors should request a formal appeal with justification for why the manuscript requires further consideration. If referee reports were included with the rejection letter, the comments must be addressed in the appeal request.
Once an appeal is submitted, the editors will collate all information relevant to the manuscript, including the cover letter, communications with the authors, and referee reports, if any. This information is discussed with the Editor-in-Chief, editors who worked on the manuscript, and any relevant Editorial Board members. When reviewing an appeal, any member of the Editorial Board with a real or perceived conflict of interest will not participate. The discussion will focus on the manuscript under consideration, the range of submissions the journal receives in the area, the overall status of the field, and the editors’ expectations for a paper in the area.
Successful appeals focus on clarifying the suitability or importance of the work if the editors rejected your manuscript because it did not fit APL Machine Learning’s criteria for publication. If the editors rejected your manuscript based on technical issues, your appeal must rebut the technical issues raised in the referee’s reports. In your appeal, please address APL Machine Learning’s acceptance standards. Also, keep in mind that because your manuscript was initially rejected, you must provide an insight or argument that goes beyond what the editor has already learned through the review process, thereby compelling the editor to conclude that your manuscript deserves further consideration.
Consider the following points when making an appeal:
- Do not include a simple revision of the manuscript to address referees’ comments. If a simple revision would have addressed the main issue, the editors would have returned your manuscript and allowed you to update it. Do include a strong argument for why the editors should reconsider your manuscript.
- Do not resubmit the manuscript under a new manuscript number, even if it has been updated in response to reviewers’ comments. The editorial office rejects resubmissions; instead, use the appeal process to request that the editors reconsider your manuscript.
- Do not reinterpret the referees’ reports for the editors. Do provide the editors with new information or insights that might lead them to reconsider publishing your manuscript.
- Do not provide a list of articles on the same topic that have recently been published in APL Machine Learning. Do provide information that supports the novelty and importance of your work.
Retraction and Correction Policies
AIP Publishing’s policy is based on best practices in academic publishing. We take our responsibility to maintain the integrity and thoroughness of the scholarly record of our content seriously. We place great importance on the accuracy of published articles. Authors may make changes to articles after they have been published online only under the circumstances outlined in AIP Publishing’s Retraction and Correction Policies.