Abstract
Molecular dipole moment in liquid water is an intriguing property, partly due to the fact that there is no unique way to partition the total electron density into individual molecular contributions. The prevailing method to circumvent this problem is to use maximally localized Wannier functions, which perform a unitary transformation of the occupied molecular orbitals by minimizing the spread function of Boys. Here we revisit this problem using a data-driven approach satisfying two physical constraints, namely: (a) The displacement of the atomic charges is proportional to the Berry phase polarization; (b) Each water molecule has a formal charge of zero. It turns out that the distribution of molecular dipole moments in liquid water inferred from latent variables is surprisingly similar to that obtained from maximally localized Wannier functions. Apart from putting a maximum-likelihood footnote to the established method, this work highlights the capability of graph convolution based charge models and the importance of physical constraints on improving the model interpretability.
Highlights
- Do molecular dipole interactions influence solid state organization? J K Whitesell, R E Davis, L L Saunders et al
Molecular dipole moments here are inferred from latent variables and not involved in the training of the model. Another physical constraint built into our regression model is the charge neutrality of each water molecule, which is formally required by the integer change of the polarization quantum in the modern theory of polarization [18, 19]. Taking these ingredients into account, we show that the distribution of molecular dipole moments inferred from our regression model using the graph convolutional neural network architecture PiNet is surprisingly similar to that obtained from maximally localized Wannier functions (MLWFs)
Because reproducing the itinerant polarization of liquid water and retaining the charge neutrality for each water molecule are two main ingredients in the PiNet-dipole model, the question that naturally arises is whether methods which satisfy these two conditions will necessarily lead to molecular dipole moments which are close to the ones obtained with Wannier centers in MLWFs
Summary
- Do molecular dipole interactions influence solid state organization? J K Whitesell, R E Davis, L L Saunders et al. Taking these ingredients into account, we show that the distribution of molecular dipole moments inferred from our regression model using the graph convolutional neural network architecture PiNet is surprisingly similar to that obtained from MLWFs. the trained model, with PiNet using only data at ambient conditions, is transferable to liquid water in a range of different densities.
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