Classical and machine learning interatomic potentials alike incorporate design choices that reflect the intuition of their authors and that are justified only a-posteriori by the performance of the model. Design choices comprise, for example, the form of the embedding function of an embedded atom potential or a specific angular dependence of a descriptor in a machine learning potential.
The...
Autonomous materials discovery with desired properties is one of the ultimate goals of materials science. We implemented and applied constrained crystal deep convolutional generative adversarial networks to design unreported (meta-)stable crystal structures. Using an image-based continuous latent space, the physical properties can be optimized while exploring a big chemical space. Our approach...
Intermetallic clathrate alloys are promising materials for thermoelectric applications. Their cage-like unit cell allows for tailoring the electronic properties through doping. Yet, a realistic theoretical description is hard to achieve due to the complex interplay between temperature, (dis)order and electronic properties. In this work, we show a novel approach to compute the...
Sampling from Boltzmann distributions through normalizing flows promises to be computationally much cheaper than molecular dynamics (MD) simulations. However, flows struggle to approximate complicated target distributions due to topological constraints and still heavily rely on MD samples to be trained on. Here, we present two lines of research addressing these issues, the former by...
