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We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body 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 non-linearity in our network is based upon tensor products and the Clebsch-Gordan 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 MD-17 dataset, and is competitive with other methods at learning geometric, energetic, electronic, and thermodynamic properties of molecules on the GDB-9 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)
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2019 Spotlight: Cormorant: Covariant Molecular Neural Networks »
Fri. Dec 13th 12:55 -- 01:00 AM Room West Exhibition Hall A
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2021 : ATOM3D: Tasks on Molecules in Three Dimensions »
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2022 : Multiresolution Mesh Networks For Learning Dynamical Fluid Simulations »
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2021 : ATOM3D: Tasks on Molecules in Three Dimensions »
Raphael Townshend · Martin Vögele · Patricia Suriana · Alex Derry · Alexander Powers · Yianni Laloudakis · Sidhika Balachandar · Bowen Jing · Brandon Anderson · Stephan Eismann · Risi Kondor · Russ Altman · Ron Dror -
2020 Tutorial: (Track2) Equivariant Networks Q&A »
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2020 Tutorial: (Track2) Equivariant Networks »
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2019 : Solutions »
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2019 Workshop: Minding the Gap: Between Fairness and Ethics »
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2018 Poster: Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network »
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