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Source data, models, and scripts necessary to reproduce the results of: "Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles" (M. Veit, D. M. Wilkins, Y. Yang, R. A. DiStasio Jr., M. Ceriotti, arXiv: 2003.12437). The model is a combination of symmetry-adapted Gaussian process regression (SA-GPR) for atomic dipoles and scalar GPR for atomic partial charges, which are fit together to reproduce the molecule's total dipole moment. Source data, kernel matrices, weights, residuals, and scripts for fitting and plotting the results are included.
Electronic structure, Machine learning, Molecular dipole, CCSD, SOAP, DFT, Gaussian process regression
Electronic structure, Machine learning, Molecular dipole, CCSD, SOAP, DFT, Gaussian process regression
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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