Abstract: This talk will give a detailed introduction to generative models with an emphasis on design principles that apply to molecular systems. We will first discuss several relevant paradigms for building generative models, including score-based diffusion and continuous normalizing flows. We will then detail how both intramolecular and intermolecular constraints inform the architectures appropriate for modeling nanoscale materials. Finally, we will come to the crucial question of how to build scalable training strategies for systems where thermal fluctuations cannot be neglected. The talk will end with a discussion of the most pressing open problems for generative "AI" in the physical sciences.