论文标题
定期耦合聚类理论计算中有限尺寸校正的机器学习
Machine learning for a finite size correction in periodic coupled cluster theory calculations
论文作者
论文摘要
我们引入了金属周期性耦合群集单打和双打（CCSD）计算的过渡结构因子（CCSD）计算的直率高斯过程回归（GPR）模型。这是灵感来自Liao和Grüneis在过渡结构因子上插值的方法，以获得CCSD的有限尺寸校正[J。化学物理。 145，141102（2016）]，通过我们自己的先前工作，使用过渡结构因子有效收敛金属的CCSD，以将金属收集到热力学极限[Nat。计算。科学。 1，801（2021）]。在我们的CCSD-FS-GPR方法以纠正有限尺寸错误的方法中，我们将结构因子拟合到动量传输中的1D功能，即$ G $。然后，我们通过将其投影到K点网格上以获得与推断结果的比较来集成此功能。显示了锂，钠和均匀电子气体的结果。
We introduce a straightforward Gaussian process regression (GPR) model for the transition structure factor of metal periodic coupled cluster singles and doubles (CCSD) calculations. This is inspired by the method introduced by Liao and Grüneis for interpolating over the transition structure factor to obtain a finite size correction for CCSD [J. Chem. Phys. 145, 141102 (2016)], and by our own prior work using the transition structure factor to efficiently converge CCSD for metals to the thermodynamic limit [Nat. Comput. Sci. 1, 801 (2021)]. In our CCSD-FS-GPR method to correct for finite size errors, we fit the structure factor to a 1D function in the momentum transfer, $G$. We then integrate over this function by projecting it onto a k-point mesh to obtain comparisons with extrapolated results. Results are shown for lithium, sodium, and the uniform electron gas.
