Search Results for author: Patrick Reiser

Found 6 papers, 4 papers with code

Implementing graph neural networks with TensorFlow-Keras

1 code implementation7 Mar 2021 Patrick Reiser, Andre Eberhard, Pascal Friederich

Graph neural networks are a versatile machine learning architecture that received a lot of attention recently.

MEGAN: Multi-Explanation Graph Attention Network

3 code implementations23 Nov 2022 Jonas Teufel, Luca Torresi, Patrick Reiser, Pascal Friederich

Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications.

Explainable artificial intelligence Graph Attention +2

Connectivity Optimized Nested Graph Networks for Crystal Structures

1 code implementation27 Feb 2023 Robin Ruff, Patrick Reiser, Jan Stühmer, Pascal Friederich

Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry.

graph construction

Analyzing dynamical disorder for charge transport in organic semiconductors via machine learning

no code implementations2 Feb 2021 Patrick Reiser, Manuel Konrad, Artem Fediai, Salvador Léon, Wolfgang Wenzel, Pascal Friederich

Organic semiconductors are indispensable for today's display technologies in form of organic light emitting diodes (OLEDs) and further optoelectronic applications.

BIG-bench Machine Learning

Graph neural networks for materials science and chemistry

no code implementations5 Aug 2022 Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich

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 new materials, and to predict synthesis routes of new materials.

Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms

1 code implementation21 Mar 2023 Henrik Schopmans, Patrick Reiser, Pascal Friederich

However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types.

Space group classification

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