2 citations · 3 across the 10 of their papers we have counts for
10 papers
MatBind: A Shared Embedding Space for Multimodal Materials Characterization
Le Yang, Anoop K. Chandran, Jona Östreicher +8
Fully characterizing a crystalline material requires integrating heterogeneous data sources -- atomic structures, diffraction patterns, electronic density of states, and natural la…
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…
Building Deep Graph Predictors with Graph Imitation Learning
André Eberhard, Gerhard Neumann, Pascal Friederich
Recent years have seen substantial progress in neural generation of text, images, and audio, supported by mature training pipelines and large-scale optimization. For graphs, howeve…
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…
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
Jonas Teufel, Annika Leinweber, Pascal Friederich
Explainable AI (xAI) interventions aim to improve interpretability for complex black-box models, not only to improve user trust but also as a means to extract scientific insights f…
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…
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…
Global Concept Explanations for Graphs by Contrastive Learning
Jonas Teufel, Pascal Friederich
Beyond improving trust and validating model fairness, xAI practices also have the potential to recover valuable scientific insights in application domains where little to no prior…
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…
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Jannik Deuschel, Caleb N. Ellington, Yingtao Luo +3
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models force a tradeoff between accuracy and interpretabilit…