Generative Models for Molecules and Materials: Guidance, Acceleration, and Sampling Dynamics
Background Generative modeling has become one of the most active areas of machine learning for scientific discovery, particularly in chemistry and materials science. Recent work has shown that diffusion models, flow-based generators, and related stochastic generative frameworks can produce chemically plausible molecules and crystalline structures, often while respecting geometric and physical constraints. The field is moving quickly, but many open questions remain about what makes these models reliable, controllable, and computationally efficient for atomistic systems. ...