2.3k citations · 16.8k across the 81 of their papers we have counts for
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Learning representations of molecules and materials with atomistic neural networks
Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio. In this chapter, we present neural network architectures that are…
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky +2
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global pot…
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
K. T. Schütt, P. Kessel, M. Gastegger +3
SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecule…
Quantum-chemical insights from interpretable atomistic neural networks
Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko +1
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical proper…