34 citations · 49 across the 8 of their papers we have counts for
9 papers
Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization
Henrik Schopmans, Christopher von Klitzing, Pascal Friederich
Sampling from unnormalized probability densities is a central challenge in computational science. Boltzmann generators are generative models that enable independent sampling from t…
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…
Learning Boltzmann Generators via Constrained Mass Transport
Christopher von Klitzing, Denis Blessing, Henrik Schopmans +2
Efficient sampling from high-dimensional and multimodal unnormalized probability distributions is a central challenge in many areas of science and machine learning. We focus on Bol…
opXRD: Open Experimental Powder X-ray Diffraction Database
Daniel Hollarek, Henrik Schopmans, Jona Östreicher +21
Powder X-ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still pr…
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…
Temperature-Annealed Boltzmann Generators
Henrik Schopmans, Pascal Friederich
Efficient sampling of unnormalized probability densities such as the Boltzmann distribution of molecular systems is a longstanding challenge. Next to conventional approaches like m…
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
Henrik Schopmans, Pascal Friederich
Efficient sampling of the Boltzmann distribution of molecular systems is a long-standing challenge. Recently, instead of generating long molecular dynamics simulations, generative…
Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
Henrik Schopmans, Patrick Reiser, Pascal Friederich
Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training direct…
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard +8
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design n…