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. AI4Mat-2023 at last year’s NeurIPS AI4Mat doubled the number of submissions and attendees, showing the growing interest and community of this emerging field. AI-enabled materials discovery is being increasingly driven by a global and interdisciplinary research community whose joint contributions are bringing materials innovation closer to real-world impact. Inspired by these trends, we aim to focus the workshop on two major themes this year:

Why Isn't it Real Yet? This discussion centers on why AI in materials science has not yet experienced the type of exponential growth seen in adjacent fields at the intersection of science and AI, such as large language models (LLM), multi-modal AI, drug discovery and computational biology.

AI4Mat Unique Challenges: Managing Multimodal, Incomplete Materials Data: A unique challenge in materials science is managing multimodal, incomplete data that is collected from diverse types of real-world equipment, including synthesis and characterization tools. Additionally, datasets and scientific understanding are often incomplete given the fact that fundamental physics and chemistry phenomena are sometimes unknown. This discussion aims to understand how to approach this unique challenge from a machine learning perspective through a panel of diverse experts.