Generative Models

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. ...

Add link to the project on the DTU website: Background At the Machine Learning in Life Science (MLSS) Research Center, we have been developing methodologies that we aim to apply and test on real-world data. In short, our work focuses on latent representations, typically obtained from a Variational Autoencoder (VAE), with the goal of extracting meaningful and reliable knowledge from them. An example of this research direction is this article, which explores representations of protein sequences. They found that the geodesic distances seem to recover evolutionary development protein sequences. ...