## About the Workshop
The AI for Accelerated Materials Discovery (AI4Mat) Workshop NeurIPS 2024 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. 

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.  Inspired by these trends, we aim to focus the AI4Mat-ICLR-2025 on two major themes this year:

- **How Do We Build a Foundation Model for Materials Science?**  Drawing inspiration from the success of recent foundation models in language and computer vision, a plethora of scientific foundation models have been proposed, including some related to materials science and chemistry. Together, these efforts represent meaningful progress in applying the concept of foundation models to materials, but individually fall short in addressing a wide range of important materials problems. Given the relevance and growing interest in materials foundation models, we propose a discussion that centers on understanding the complex, interdisciplinary nature of foundational models for materials and how the community can contribute towards building them. To that end, we are bringing together experts from diverse institutions and backgrounds for a forum at AI4Mat-ICLR-2025.

- **What are Next-Generation Representations of Materials Data?** Advancements in AI for materials science have led researchers to focus on increasingly intricate and diverse systems, bringing them closer to real-world applications. This increase in complexity has raised questions about how to efficiently represent diverse materials systems, particularly those requiring the integration of multiple data modalities. Materials representation learning remains an open problem with unique challenges to be addressed so as to enable continued progress in the development of new machine learning methods for real-world materials challenges.