The discovery of new materials drives the development of key technologies like solar cells, batteries, carbon capture, and catalysis. While there has been growing interest in materials discovery with machine learning, the specific modeling challenges posed by materials have been largely unknown to the broader community. Compared with drug-like molecules and proteins, the modeling of materials has the following two major challenges. First, materials-specific inductive biases are needed to develop successful ML models. For example, materials often don’t have a handy representation like 2D graphs for molecules or sequences for proteins. Second, there exists a broad range of interesting materials classes, such as inorganic crystals, polymers, catalytic surfaces, nanoporous materials, and more. Each class of materials demands a different approach to represent their structures, and new tasks/data sets to enable rapid ML developments.This workshop aims at bringing together the community to discuss and tackle these two types of challenges. In the first session, we will feature speakers to discuss the latest progress in developing ML models for materials focusing on algorithmic challenges, covering topics like representation learning, generative models, pre-training, etc. In particular, what can we learn from the more developed field of ML for molecules and 3D geometry and where might challenges differ and opportunities for novel developments lie? In the second session, we will feature speakers to discuss unique challenges for each sub-field of materials design and how to define meaningful tasks that are relevant to the domain, covering areas including inorganic materials, polymers, nanoporous materials, catalysis, etc. More specifically, what are the key materials design problems that ML can help tackle?