268 citations · 508 across the 7 of their papers we have counts for
7 papers
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Malte Esders, Thomas Schnake, Jonas Lederer +4
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a te…
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer +5
Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abst…
Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials
Klara Bonneau, Jonas Lederer, Clark Templeton +3
Machine learned potentials are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic r…
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer +2
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with a…
Automatic Identification of Chemical Moieties
Jonas Lederer, Michael Gastegger, Kristof T. Schütt +3
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this…
Toward Explainable AI for Regression Models
Simon Letzgus, Patrick Wagner, Jonas Lederer +3
In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear…
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
Thomas Schnake, Oliver Eberle, Jonas Lederer +4
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explain…