• Format: All submissions must be in PDF format using the ICLR 2026 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 ICLR style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review. 

• 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).

  1. • Dataset & Benchmark Submissions: For submissions containing new data or benchmarks, non-anonymous external links may be included in the paper.

• Dual-Submission Policy: We welcome ongoing and unpublished work. We will also accept papers that are under review at any venue at the time of submission. Submissions under review in venues for related fields (e.g. materials science, chemistry) are welcome. Per ICLR guidelines, work that is concurrently published at ICLR or has been published at prior machine learning venues is not eligible. We will consider work that builds upon prior published work (e.g., small modifications that might not lead to a standalone paper) if we believe it can add to the discussion and knowledge sharing quality of the workshop. In such cases, authors should make clear why they are submitting the work.

• 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.

• AI4Mat-ICLR 2026 Themed Track: We especially encourage papers aligned to the themed track - "Feedback-Based Learning for Materials Design".

• Findings Track: We encourage the submission of diverse forms of preliminary work through the Findings Track.

• Tiny Paper Track: We encourage the submission of diverse forms of preliminary work through the Tiny Papers Track.

• Goals: The goal of the Feedback-Based Learning for Materials Design track is to encourage submissions of research that address the unique nature of feedback based learning in AI for materials, including relevant methods, datasets and best practices in real-world conditions to further community understanding of important technical concepts and challenges. Building on AI4Mat-ICLR-2026’s focus on “ Reinforcement Learning & Beyond”, this track aims to highlight both the advancements and challenges of incorporating feedback-based learning in materials discovery setttings to foster community discussion. Example research topics include, but are not limited to:

  • • New algorithms that incorporate feedback-based learning techniques (e.g., reinforcement learning, Bayesian optimization) applied to materials design

    • New benchmarks for materials design that enable studying feedback-based algorithms 

    • Methods in settings with sparse data and sparse sources of feedback

    • Methods addressing feedback from diverse data modalities

    • New algorithms for feedback based learning

    • Discussion of gaps and limitations in current approaches 

• Instructions: Similar to the Paper Track, 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 ICML 2026 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 polished and generally complete work compared to short-form papers, which are typically regarded as work in progress.

• 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:

    • Machine learning algorithms and deep learning architectures for accelerated materials simulations and property modelling 

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

    • Datasets, benchmarks and analysis methods

  • Automated Synthesis

    • Optimization and discovery of materials synthesis and chemical synthesis procedures 

    • Datasets, benchmarks, knowledge extraction and data processing techniques for materials synthesis

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

  • Automated Characterization

    • Analysis of real-world characterization data, e.g., microscopy data (including multi-modal data like images, spectra and diffraction patterns), x-ray diffraction, optimal measurements, property measurements

    • Datasets, benchmarks and 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

• 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 peer-reviewed conferences and 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 polished and complete work compared to short-form papers, which are typically regarded as work in progress.

• Goals: The goal of the Findings & Open Challenges 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:

• • Open Challenges: Submissions introducing and discussing overlooked scientific questions and potential future directions for a given application area. We encourage submissions that address open challenges and describe: 1. Why the current research and state-of-the-art fall short for a given challenges; 2. What directions the authors believe the community can focus on to help address the open challenge. We hope that submissions describing open challenges will enable the AI4Mat community to expand the range of interdisciplinary research the research community is working on.

  • Tools: Submissions introducing and discussing useful tools for research in automated materials design that can be disseminated to the research community through the workshop. Tools can be conceptual (e.g. AI for new data modalities in materials science) 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.  

  • Behind the Scenes: 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.

  • Surveys: 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.

  • Responsible Use: 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.