论文标题
平均场密度基质分解
Mean-Field Density Matrix Decompositions
论文作者
论文摘要
我们介绍了依靠局部分子轨道和物理上声音荷兰语人口方案的使用，介绍了平均场Hartree-fock（HF）和Kohn-Sham密度功能理论（KS-DFT）的新的和强大的分解。将新的无损特性分解允许将1-电子的密度矩阵划分为键合或原子贡献，将其与文献中的替代方案进行了比较。除了评论可能的应用作为某些电子现象合理化的解释性工具外，我们还证明了分解平均场理论如何使在机器学习量子化学的背景下暴露和放大组成特征成为可能。通过改善基础数据的粒度来使这成为可能。根据我们的初步概念验证结果，我们猜测，当今存在的许多结构性质量推论可以通过有效利用数据集复杂性和丰富性的提高来进一步完善。
We introduce new and robust decompositions of mean-field Hartree-Fock (HF) and Kohn-Sham density functional theory (KS-DFT) relying on the use of localized molecular orbitals and physically sound charge population protocols. The new lossless property decompositions, which allow for partitioning 1-electron reduced density matrices into either bond-wise or atomic contributions, are compared to alternatives from the literature with regards to both molecular energies and dipole moments. Besides commenting on possible applications as an interpretative tool in the rationalization of certain electronic phenomena, we demonstrate how decomposed mean-field theory makes it possible to expose and amplify compositional features in the context of machine-learned quantum chemistry. This is made possible by improving upon the granularity of the underlying data. On the basis of our preliminary proof-of-concept results, we conjecture that many of the structure-property inferences in existence today may be further refined by efficiently leveraging an increase in dataset complexity and richness.
