arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2103.08274physics.chem-phcond-mat.soft
收藏

液态水分子偶极矩的机器学习推断

Machine Learning Inference of Molecular Dipole Moment in Liquid Water

Lisanne Knijff, Chao Zhang

摘要:

液态水中的分子偶极矩是一个有趣的性质,部分原因是没有唯一的方法将总电子密度划分为单个分子的贡献。规避这个问题的流行方法是使用最大局域化的Wannier函数,它通过最小化Boys的扩展函数来执行所占据的分子轨道的幺正变换。在这里,我们使用满足两个物理约束的数据驱动方法来重新审视这个问题,即:i)原子电荷的位移与Berry相位极化成比例; ii)每个水分子的形式电荷为零。事实证明,从潜变量推断的分子偶极矩在液态水中的分布是惊人的相似,从最大本地化的Wannier函数。除了把一个最大似然脚注建立的方法,这项工作突出了图形卷积为基础的收费模型的能力和物理约束的重要性,提高解释性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑