Details

Autor(en) / Beteiligte

• Schütt, Kristof T.
• Tkatchenko, Alexandre
• Müller, Klaus-Robert

Titel

Learning Representations of Molecules and Materials with Atomistic Neural Networks

Ist Teil von

• Machine Learning Meets Quantum Physics, p.215-230

Ort / Verlag

Cham: Springer International Publishing

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Deep learning has been shown to learn efficient representations for structured data such as images, text, or audio. In this chapter, we present neural network architectures that are able to learn efficient representations of molecules and materials. In particular, the continuous-filter convolutional network SchNet accurately predicts chemical properties across compositional and configurational space on a variety of datasets. Beyond that, we analyze the obtained representations to find evidence that their spatial and chemical properties agree with chemical intuition.