Format. All submissions must be in PDF format using the ICLR 2024 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).

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 We welcome ongoing and unpublished work.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. We will consider previously published work if we can think it can add to the discussion and knowledge sharing quality of the workshop. In such cases, author should make clear why they are submitting previously published 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.

We especially encourage aligned to the workshops major themes: 1. ML-Potentials Across Multiple Scales; 2. Materials Design With Real-World Data

Submission Link in OpenReview

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 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 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 5 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 explaining the concepts in an accessible manner.

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.