Climate change is a societal and political problem whose impact could be mitigated by technology. Underlying many of its technical challenges is a surprisingly simple yet challenging problem; modeling the interaction of atoms. In this talk, we motivate the problem and provide insights into how this opens up new intriguing directions for machine learning and AI researchers. Recent large-scale datasets released by the Open Catalyst Project enable the training of ML models that generalize across a broad range of the chemical space. Analogies are drawn to computer vision to map recent state-of-the-art approaches for atomic modeling to a more familiar domain. We conclude by exploring the numerous open problems and their potential for wide ranging impact beyond climate change.