May 1, 2018 · We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory (DFT).
- 13 properties : atomization energies, polarizability, dipole moment, thermal capacity … - computed using density functional theory (B3LYP). - Error in ...
Solid harmonic wavelets are computed by multiplying solid harmonic functions with Gaussian windows dilated at different scales.
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Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities · Chemistry, Physics. NIPS · 2017.
Molecular properties are independent of position and orientation of the molecule. Solid harmonic scattering invariants are shown to linearize this energy.
We study an application of solid harmonic scattering invariants to the estimation of quantum molecular energies, which are also invariant to rigid motion and ...
Abstract. We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.
We study the application of solid harmonic scattering invariants to the regression of quantum molecular energies. The next section introduces the translation ...
An application of solid harmonic scattering invariants to the estimation of quantum molecular energies, which are also invariant to rigid motion and stable ...