7 citations · 14 across the 8 of their papers we have counts for
8 papers
Hyper-Dimensional Fingerprints as Molecular Representations
Jonas Teufel, Luca Torresi, André Eberhard +1
Computational molecular representations underpin virtual screening, property prediction, and materials discovery. Conventional fingerprints are efficient and deterministic but lose…
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…
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…
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…
Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI
Hunter Sturm, Jonas Teufel, Kaitlin A. Isfeld +2
Herein, we present the application of MEGAN, our explainable AI (xAI) model, for the identification of small colloidally aggregating molecules (SCAMs). This work offers solutions t…
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
Jonas Teufel, Luca Torresi, Pascal Friederich
Despite the increasing relevance of explainable AI, assessing the quality of explanations remains a challenging issue. Due to the high costs associated with human-subject experimen…
MEGAN: Multi-Explanation Graph Attention Network
Jonas Teufel, Luca Torresi, Patrick Reiser +1
We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along…