2 code implementations • 11 Dec 2022 • Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, Michael Gastegger
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning.
no code implementations • 30 Mar 2022 • Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
1 code implementation • 21 Dec 2021 • Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, Gregoire Montavon
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 learning models such as deep neural networks.
no code implementations • 5 Jun 2020 • Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions, i. e. by identifying groups of edges that jointly contribute to the prediction.