About the Workshop

The AI for Accelerated Materials Discovery (AI4Mat) Workshop at NeurIPS 2025 provides an inclusive and collaborative platform where AI researchers and material scientists converge to tackle the cutting-edge challenges in AI-driven materials discovery and development. Our goal is to foster a vibrant exchange of ideas, breaking down barriers between disciplines and encouraging insightful discussions among experts from diverse disciplines and curious newcomers to the field. The workshop embraces a broad definition of materials design encompassing matter in various forms, such as crystalline and amorphous solid-state materials, glasses, molecules, nanomaterials, and devices. By taking a comprehensive look at automated materials discovery spanning AI-guided design, synthesis and automated material characterization, we hope to create an opportunity for deep, thoughtful discussion among researchers working on these interdisciplinary topics, and highlight ongoing challenges in the field.

Covering materials such as :

AI4Mat was first held at NeurIPS 2022, bringing together materials scientists and AI researchers into a common forum with productive discussion on major research challenges at the intersection of AI and materials science. Since then, AI4Mat has established itself as a leading venue for the exchange of ideas on the latest developments in the field, bridging together international academic, industry and government institutions. AI4Mat-NeurIPS-2023 highlighted the growing interest and expanding research community of this emerging field. This momentum continued with two workshops held in 2024 (AI4Mat-BOKU-2024 in Vienna and AI4Mat-NeurIPS-2024 in Vancouver) designed to further accelerate research progress. The field of AI-enabled materials discovery is increasingly propelled by a global and interdisciplinary research community, whose collaborative efforts are driving materials innovation toward tangible real-world impact across diverse applications. AI4Mat-ICLR-2025 in Singapore, AI4Mat's first workshop in Asia, focused on the role of foundation models and representation learning for materials science while continuing to build a more global community of researchers for the emerging field. AI4Mat-NeurIPS-2025 aims to continue building upon prior success while continuing to push the research frontier and grow the global community. AI4Mat-NeurIPS-2025 program will focus on:

AI4Mat-RLSF (Research Learning from Speaker Feedback): AI4Mat-RLSF is a new program that provides structured feedback for submitted papers from a panel of invited discussants with relevant expertise. As outlined in our submissions page, submission can opt-in to participate in AI4Mat-RLSF on OpenReview. Selected workshop papers will give a 10-minute lightning talk; the AI4Mat-RLSF panel will then provide feedback on three key questions: 1. What kind of impact can you see for this research in materials science and what can the team do to increase impact? 2. What technical strengths does this research have over existing technologies that you see? 3. What future work directions (2-3 max) do you suggest for the team? We plan to include a handful papers in the AI4Mat-RLSF session, with each selected paper receiving feedback from two discussants. In addition to AI4Mat-RLSF, we will also hold a spotlight session in the traditional format (talk + Q&A).
AI4Mat Frontiers & Benchmarking: Benchmarks are a critical part of machine learning (ML) method development, as they define meaningful goals and allow for empirical comparisons between methods. Unfortunately, many of the current benchmarks proposed in ML for materials science fail to meaningfully capture important considerations that would make ML methods more impactful in materials design. As such, a central theme of AI4Mat-NeurIPS-2025 is to understand limitations of current benchmarks and frontier methods. Our program creates focused discussions with invited speakers on how to develop benchmarks that are meaningful for the community and can effectively evaluate state-of-the-art of frontier methods, especially their limitations. Our goal is to build greater community understanding on how to align benchmarks and new methods to real-world challenges in ML for materials science and thereby create broader engagement from both communities.

Submissions

Check our submissions page for instructions on how to submit through OpenReview.
Accepted peer-reviewed submissions will be invited to present a poster at the workshop and posted on the workshop website for non-archival records. Some peer-reviewed submissions will be invited to present a spotlight talk.