We propose a full-day, medium-sized workshop at ICLR 2025 titled “AI for Accelerated Materials Design” (AI4Mat-ICLR-2025). This workshop will serve as a venue for researchers at the intersection of AI and materials science to address pressing scientific challenges using AI-driven techniques. AI is starting to revolutionize materials science and engineering, driving major global research initiatives from academic and government institutions and corporate research labs, alongside the rise of several startups for AI driven materials discovery. AI4Mat's holistic approach to materials design and machine learning ensures comprehensive discussions and foster novel directions across the materials landscape. AI4Mat-ICLR-2025 centers on understanding crucial and timely technical challenges that are unique to AI for materials design: 1. How Do We Build a Foundation Model for Materials Science?: The success of foundation models in various machine learning domains has led to growing relevance and interest in materials foundation models. As such, 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. 2. What are Next-Generation Representations of Materials Data?: Materials representation learning continue to be a rapidly evolving technical challenge with unique considerations informed by real-world materials challenges.AI4Mat-ICLR-2025 also aims to grow and empower a notable community to leverage AI for impactful materials applications. Concretely we plan to build upon past AI4Mat programs: 1. Travel Grant Program: Building upon the success of past AI4Mat programs, we plan to continue a travel grant program funded by AI4Mat corporate sponsors to enable researcher participation with a focus on underrepresented communities. 2: Tiny Papers Track: This track extends our efforts in inclusive research participation based on previous ICLR innovations. 3. Themed Submission Track: We plan to conduct a themed submission track on multi-modal data collection, structured data sharing, and multi-modal representation learning, in order to encourage the community to tackle a common problem of interest. 4. Journal Track: Similar to previous AI4Mat workshops, we aim to provide AI4Mat researchers an opportunity to submit their work to a prestigious venue for their interdisciplinary research.