34 citations · 58 across the 7 of their papers we have counts for
Showing 2022Show all
2 papers · 1 filter
cond-mat.supr-con2022
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…
physics.chem-ph2022★ 34 cited
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…