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We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex manybody physical systems. We apply these networks to molecular systems with two goals: learning atomic potential energy surfaces for use in Molecular Dynamics simulations, and learning ground state properties of molecules calculated by Density Functional Theory. Some of the key features of our network are that (a) each neuron explicitly corresponds to a subset of atoms; (b) the activation of each neuron is covariant to rotations, ensuring that overall the network is fully rotationally invariant. Furthermore, the nonlinearity in our network is based upon tensor products and the ClebschGordan decomposition, allowing the network to operate entirely in Fourier space. Cormorant significantly outperforms competing algorithms in learning molecular Potential Energy Surfaces from conformational geometries in the MD17 dataset, and is competitive with other methods at learning geometric, energetic, electronic, and thermodynamic properties of molecules on the GDB9 dataset.
Author Information
Brandon Anderson (University of Chicago)
Truong Son Hy (The University of Chicago)
Research areas: Kernel methods, Graph kernels, Graph Neural Networks
Risi Kondor (U. Chicago)
Risi Kondor joined the Flatiron Institute in 2019 as a Senior Research Scientist with the Center for Computational Mathematics. Previously, Kondor was an Associate Professor in the Department of Computer Science, Statistics, and the Computational and Applied Mathematics Initiative at the University of Chicago. His research interests include computational harmonic analysis and machine learning. Kondor holds a Ph.D. in Computer Science from Columbia University, an MS in Knowledge Discovery and Data Mining from Carnegie Mellon University, and a BA in Mathematics from the University of Cambridge. He also holds a diploma in Computational Fluid Dynamics from the Von Karman Institute for Fluid Dynamics and a diploma in Physics from Eötvös Loránd University in Budapest.
Related Events (a corresponding poster, oral, or spotlight)

2019 Poster: Cormorant: Covariant Molecular Neural Networks »
Fri. Dec 13th 01:00  03:00 AM Room East Exhibition Hall B + C #76
More from the Same Authors

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2020 Tutorial: (Track2) Equivariant Networks »
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2019 : Solutions »
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