Abstract
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory (DFT). Using Gaussian-type orbital functions, we create surrogate electronic densities of the molecule from which we compute invariant "solid harmonic scattering coefficients" that account for different types of interactions at different scales. Multilinear regressions of various physical properties of molecules are computed from these invariant coefficients. Numerical experiments show that these regressions have near state-of-the-art performance, even with relatively few training examples. Predictions over small sets of scattering coefficients can reach a DFT precision while being interpretable.
Original language | English |
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Article number | 241732 |
Journal | Journal of Chemical Physics |
Volume | 148 |
Issue number | 24 |
DOIs | |
Publication status | Published - 28 Jun 2018 |
Bibliographical note
Funding Information:M.E., G.E., and S.M. are supported by ERC Grant Invari-antClass No. 320959; M.H. is supported by the Alfred P. Sloan Fellowship (Grant No. FG-2016-6607), the DARPA YFA (Grant No. D16AP00117), and NSF Grant No. 1620216.
Funding Information:
M.E., G.E., and S.M. are supported by ERC Grant InvariantClass No. 320959; M.H. is supported by the Alfred P. Sloan Fellowship (Grant No. FG-2016-6607), the DARPA YFA (Grant No. D16AP00117), and NSF Grant No. 1620216.
Publisher Copyright:
© 2018 Author(s).
Funding
M.E., G.E., and S.M. are supported by ERC Grant Invari-antClass No. 320959; M.H. is supported by the Alfred P. Sloan Fellowship (Grant No. FG-2016-6607), the DARPA YFA (Grant No. D16AP00117), and NSF Grant No. 1620216. M.E., G.E., and S.M. are supported by ERC Grant InvariantClass No. 320959; M.H. is supported by the Alfred P. Sloan Fellowship (Grant No. FG-2016-6607), the DARPA YFA (Grant No. D16AP00117), and NSF Grant No. 1620216.
ASJC Scopus subject areas
- General Physics and Astronomy
- Physical and Theoretical Chemistry