About the Workshop

The AI for Accelerated Materials Discovery (AI4Mat) Workshop at NeurIPS 2026 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 focused discussion on latest frontiers and approaches to benchmarking while introducing a new format of live feedback for selected papers through AI4Mat-RLSF (Research Learning from Speaker Feedback. AI4Mat-ICLR-2026, AI4Mat's first workshop in South America, continued growing the global community while hosting discussions on feedback-based learning and multi-modal representations. The AI4Mat-NeurIPS-2026 will continue this effort by further expanding the workshop's geographic reach and a new program will focus on:

Scaling Laws for Materials Reasoning: From Compute to Scientific Discovery: The progress of foundation models has been driven by scaling laws relating model size, data, and compute to predictable capability gains. Materials science, however, presents a fundamentally different scaling landscape: data is heterogeneous, expensive, and success is often driven by deep thought and domain specific reasoning. In this session, we will explore how scaling laws show up in the materials domain, with a focus on reasoning-intensive tasks including but not limited to synthesis planning, property prediction under structural and compositional complexity, inverse design, reasoning model training and training of materials science specific models like Machine Learning Interatomic Potentials (MLIPs). As such, some of the motivating questions include: Do larger models yield predictable improvements on materials benchmarks, or do domain-specific bottlenecks break conventional scaling behavior? How can test-time compute and chain-of-thought reasoning be leveraged for problems requiring deep scientific reasoning? What are the returns on scaling compute for simulation-in-the-loop workflows where each data point carries significant cost?
Automating Discovery That Delivers: When AI Meets the Messiness of Real Experiments: Generative models, universal potentials, and agentic design loops now populate the machine learning literature at an increasing pace, yet a persistent gap remains between algorithmic innovation and tangible deployment for materials discovery. In this session, we aim to directly address this gap, sharing firsthand research focusing on systems that deliver impactful discovery ranging from AI-driven prediction through robotic synthesis, automated characterization, and iterative refinement. We also aim to focus on automated characterization and data collection as a critical bottleneck in autonomous discovery, including high-throughput measurements that might generate rich but noisy data streams. This session will also explore practical challenges, such as distribution shifts, integration challenges with robotic platforms and self-driving laboratories, and the underlying infrastructure required for autonomous systems that deliver beyond the conference paper.

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.