• Format. All submissions must be in PDF format using the NeurIPS 2023 LaTeX style file . Please include the references and supplementary materials in the same PDF as the main paper. The maximum file size for submissions is 50MB. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review. Filling the NeurIPS checklist with the paper is not compulsory.

• Double-blind reviewing. The reviewing process will be double blind. As an author, you are responsible for anonymizing your submission. In particular, you should not include author names, author affiliations, or acknowledgements in your submission and you should avoid providing any other identifying information (even in the supplementary material).

• Dual-submission policy. We welcome ongoing and unpublished work. We will also accept papers that are under review (at any venue, including ICLR 2024) at the time of submission. Per NeurIPS guidelines, work that has been previously published in machine-learning or related fields is ineligible for any workshop. Submissions under review in venues for related fields (e.g. materials science, chemistry) are welcome. Check the FAQ for more details.

• Non-archival. The workshop is a non-archival venue and will not have official proceedings. Workshop submissions can be subsequently or concurrently submitted to other venues. 

• Visibility. Submissions and reviews will not be public. Only accepted papers across the different tracks will be made public on the workshop website.

• Submission Link in OpenReview

This year, AI4Mat is partnering with Digital Discovery from the Royal Society of Chemistry to publish top-tier submissions in a special collection in the journal with a focus on AI for materials design. The submission process for the workshop will remain the same as for AI4Mat-23 with a single round of reviews through OpenReview. For the special collection in Digital Discovery, manuscripts will undergo some additional steps:

• The program committee will recommend high-quality manuscripts from the set of accepted submissions for publication in Digital Discovery.

• • Authors will have the option to indicate if they would like their submission to be considered for the Digital Discovery special collection for AI4Mat-23. 

  • In addition to selecting work for spotlight presentations, the program committe will also recommend manuscripts that would be a good fit for the collection.

• Authors of recommended manuscripts will need to consent for consideration for the collection in Digital Discovery and prepare a manuscript in one of the normal article types of the journal. 

• • Authors should keep in mind that publication in Digital Discovery constitutes a peer-reviewed publication of the work and as such the manuscript may not be published in other venues depending on dual submission policies. This is different from regular workshop submissions which are non-archival.

• All manuscripts considered for publication in the collection must be accompanied by data availability statement and open data and/or code necessary to replicate the results, which will be checked thoroughly by a data reviewer

• Manuscripts considered for Digital Discovery following the initial review phase for the workshop and consent of the author will be reviewed for an additional round to decide which manuscripts will be included in the collection.

• The special collection will be finalized and shared with the community in the first couple of months of 2024. 

The program committee would like to highlight some important features of the Digital Discovery special collection:

• The articles will be open-access with no additional costs to authors.

• • Given the open-data goals of Digital Discovery, authors will be expected to release relevant data and code, and summarize this in a “Data Availability Statement” in the manuscript. This can be performed through standard platforms for data releases like GitHub or Zenodo.

• Articles in the special collection will be considered finished work and the manuscript will be reviewed according to that standard.

• • It is possible for authors to submit a 4-page extended abstract in the first round of reviews and later extend to a full manuscript for consideration for the collection.

• Accepted articles will be published in normal Digital Discovery issues as they complete the journal’s production processes.

• The program committee is open to including perspective and review articles in the collection. Both formats  can be submitted through the OpenReview portal.

• Only submissions to the workshop will be considered for the special collection. It is not possible to submit manuscripts for consideration after the workshop submission deadline.

• Goals: The goal of the Paper Track is to highlight research work related to automated materials discovery that pushes the state-of-the-art and foster discussion among workshop participants. All submissions should explain why the proposed work helps accelerate material discovery and can be related thematically to the three distinct parts of self-driving laboratories (AI-Guided Design, Automated Synthesis, Automated Characterization) in cases where the relation may be ambiguous. If a submission does not fit into one of the aforementioned thematic tracks, we encourage the authors to provide a detailed explanation of why their work relates to automated materials discovery. Example research topics include, but are not limited to:

  • AI-Guided Materials Design:

    • Deep learning architectures for modeling materials properties 

    • Machine learning algorithms for accurate and accelerated materials simulation

    • Natural language techniques for understanding materials design considerations

    • Generative algorithms for materials discovery based on diverse machine learning techniques (e.g. reinforcement learning, GFlowNets, generative adversarial networks, diffusion models)

    • New datasets and benchmarks relevant to AI-Guided Materials Design

  • Automated Chemical Synthesis

    • Discovery and optimization of synthesis procedures for one or many materials

    • Application of knowledge extraction techniques to process relevant information for understanding materials synthesis

    • Machine learning algorithms for small-data regimes (e.g. active learning with costly data acquisition)

    • New datasets and benchmarks relevant to Automated Chemical Synthesis

  • Automated Characterization

    • Analysis of microscopy data, including multi-modal data like images, spectra and diffraction patterns

    • Automation frameworks for data collection and analysis in characterization tools amenable to machine learning algorithms

    • Machine learning algorithms in defect and anomaly detection in settings relevant to automated materials design

    • New datasets and benchmarks relevant to Automated Materials Characterization

• Instructions: We encourage submissions of short-form papers up to 4 pages in length with unlimited pages for references and supplementary materials. We will also consider full-length papers of up to 9 pages in length for works that have been submitted or works that authors intend to submit to other venues, such as ICLR 2024 or peer-reviewed journals. Submissions should be clearly identified as short-form or full-length and we discourage works that do not fall into either category (e.g. 6 page submissions that do not meet the standards for full-length papers). We will ask reviewers to apply higher standards to full-length submissions as those are assumed to be more finished work compared to short-form papers that are considered to be work in progress.

• Goals: The goal of the Tutorials Track is to encourage submissions of useful tools for research in automated materials design that can be disseminated to the research community through the workshop. Tutorials can be conceptual (e.g. AI for microscopic data) or practical in nature (e.g software libraries) as long as they have a clear relation to advancing the research themes of the workshop. We encourage submissions that describe novel experimental equipment, tools and workflows that can facilitate the application of machine learning to materials discovery.

• Instructions: We encourage submissions of extended abstracts up to 4 pages in length along with supplementary materials outlining the primary content of the tutorial. For software submissions, we encourage supplementary materials in the form of a notebook showcasing the presented tools. For conceptual or experimental tutorials, we encourage supplementary materials in the form of a presentation and video through the NeurIPS workshop site explaining the concepts in an accessible manner to the NeurIPS community.

• Goals: The goal of the Findings Track is to encourage submissions of research that has strong relevance in enabling the application of AI to automated materials design, but might not fit perfectly with the other tracks. Submissions to the findings track may include, but are not limited to:

• • Essential engineering work that can often be lost in research discussions, such as dataset preparation, putting together simulations of complex systems or assembling intricate hardware systems for different types of robotic automation workflows.

  • Survey papers centered around a relevant theme for the workshop that provides new conclusions based on the analysis of the existing literature or highlights new insights or ideas that have received less attention, including negative results.

  • Submissions introducing and discussing overlooked scientific questions and potential future directions for a given application area.

  • Submissions discussing responsible use, in an inclusive sense, of data and methods related to AI for automated materials design.

• Through the findings track, we aim to learn more about and share with the community the various technical considerations and best practices needed to develop the complex systems required for state-of-the-art automated materials design.

• Instructions: We encourage submissions of short-form papers up to 5 pages in length with unlimited pages for references and supplementary materials. Submissions in this track should clearly explain how the proposed work helps accelerate material discovery in cases where the relation may be ambiguous. Reviewers will be asked to evaluate the thoroughness and quality of technical work described in the submission.