116 citations · 422 across the 39 of their papers we have counts for
3 papers · 1 filter
Direct prediction of phonon density of states with Euclidean neural networks
Zhantao Chen, Nina Andrejevic, Tess Smidt +6
Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete prope…
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt +1
Equivariant neural networks (ENNs) are graph neural networks embedded in and are well suited for predicting molecular properties. The ENN library e3nn has customizab…
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller
Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them". We demonstrate that symmetry equivariant neu…