Title: Machine Learning Inference of Molecular Dipole Moment in Liquid Water Show Chinese title

Title: 机器学习在液态水分子偶极矩中的推断

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: i) The displacement of the atomic charges is proportional to the Berry phase polarization; ii) 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 interpretability. Show Chinese abstract

Abstract: 液态水中分子偶极矩是一个引人入胜的性质，部分原因在于没有一种唯一的方法将总电子密度划分为各个分子的贡献。 解决这个问题的主流方法是使用最大局部化Wannier函数，它们通过最小化Boys的扩散函数对占据的分子轨道进行酉变换。 在此，我们使用一种满足两个物理约束的数据驱动方法重新审视这个问题，即：i) 原子电荷的位移与贝里相位极化成比例；ii) 每个水分子具有零的名义电荷。 结果表明，从潜在变量推断出的液态水中分子偶极矩分布与从最大局部化Wannier函数得到的结果惊人地相似。 除了为现有方法添加最大似然脚注外，这项工作突出了基于图卷积的电荷模型的能力以及物理约束在提高可解释性方面的重要性。