59 citations · 247 across the 12 of their papers we have counts for
12 papers
Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides
Marlen Neubert, Patrick Reiser, Frauke Gräter +1
Hydrogen atom transfer (HAT) reactions are essential in many biological processes, such as radical migration in damaged proteins, but their mechanistic pathways remain incompletely…
Discovering one molecule out of a million: inverse design of molecular hole transporting semiconductors tailored for perovskite solar cells
Jianchang Wu, Luca Torresi, ManMan Hu +17
The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexit…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
An integrated system built for small-molecule semiconductors via high-throughput approaches
Jianchang Wu, Jiyun Zhang, Manman Hu +16
High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules pr…
Accurate GW frontier orbital energies of 134 kilo molecules
Artem Fediai, Patrick Reiser, Jorge Enrique Olivares Peña +2
The QM9 dataset [Scientific Data, Vol. 1, 140022 (2014)] became a standard dataset to benchmark machine learning methods, especially on molecular graphs. It contains geometries as…
Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
Henrik Schopmans, Patrick Reiser, Pascal Friederich
Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training direct…
Connectivity Optimized Nested Graph Networks for Crystal Structures
Robin Ruff, Patrick Reiser, Jan Stühmer +1
Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. Here, we recapitulate the graph construction for crystalline (…
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…
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard +8
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 n…
Charge Transfer Simulations using Hamiltonian Elements and Forces from Neural Networks
Philipp M. Dohmen, Mila Krämer, Patrick Reiser +3
The trajectory surface hopping method has been widely used in the simulation of charge transport in organic semiconductors. In the present study, we employ the machine learning (ML…
Implementing graph neural networks with TensorFlow-Keras
Patrick Reiser, Andre Eberhard, Pascal Friederich
Graph neural networks are a versatile machine learning architecture that received a lot of attention recently. In this technical report, we present an implementation of convolution…
Analyzing dynamical disorder for charge transport in organic semiconductors via machine learning
Patrick Reiser, Manuel Konrad, Artem Fediai +3
Organic semiconductors are indispensable for today's display technologies in form of organic light emitting diodes (OLEDs) and further optoelectronic applications. However, organic…