663 citations · 1.7k across the 24 of their papers we have counts for
8 papers · 1 filter
3DSC - A New Dataset of Superconductors Including Crystal Structures
Timo Sommer, Roland Willa, Jörg Schmalian +1
Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify prom…
Actively Learning Costly Reward Functions for Reinforcement Learning
André Eberhard, Houssam Metni, Georg Fahland +2
Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent…
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 to learn joint representations of disjoint molecular graphs
Chen Shao, Zhou Chen, Pascal Friederich
Graph neural networks are widely used to learn global representations of graphs, which are then used for regression or classification tasks. Typically, the graphs in such data sets…
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…
On scientific understanding with artificial intelligence
Mario Krenn, Robert Pollice, Si Yue Guo +9
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracl…