强相关的纯密度泛函和机器学习的热力学极限
Pure density functional for strong correlations and the thermodynamic limit from machine learning
摘要：
我们使用密度矩阵重整化群，应用于一维连续哈密顿模型，精确地解决了各种分离和原子数的氢原子链。我们训练和测试机器学习近似$F[n]$，电子密度泛函的通用部分，在量子化学的准确性。我们的计算（a）绕过了标准的Kohn-Sham方法，避免了寻找轨道的需要，（b）包括高度拉伸键的强相关性，没有任何特定的困难（不像所有标准的DFT近似），（c）非常准确，可以用来找到量子化学精确度的热力学极限中的能量。
英文摘要：
We use density-matrix renormalization group, applied to a one-dimensional model of continuum Hamiltonians, to accurately solve chains of hydrogen atoms of various separations and numbers of atoms. We train and test a machine-learned approximation to $F[n]$, the universal part of the electronic density functional, to within quantum chemical accuracy. Our calculation (a) bypasses the standard Kohn-Sham approach, avoiding the need to find orbitals, (b) includes the strong correlation of highly-stretched bonds without any specific difficulty (unlike all standard DFT approximations) and (c) is so accurate that it can be used to find the energy in the thermodynamic limit to quantum chemical accuracy.
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。
