3 papers
cond-mat.mtrl-sci2026
Generative Models for Crystalline Materials
Houssam Metni, Laura Ruple, Lauren N. Walters +13
Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has eme…
cs.LG2025
Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs
Luca Torresi, Pascal Friederich
Self-driving laboratories (SDLs) are combining recent technological advances in robotics, automation, and machine learning based data analysis and decision-making to perform autono…
cs.LG2025
Symmetry-Aware Bayesian Flow Networks for Crystal Generation
Laura Ruple, Luca Torresi, Henrik Schopmans +1
The discovery of new crystalline materials is essential to scientific and technological progress. However, traditional trial-and-error approaches are inefficient due to the vast se…