论文标题
具有机器学习的非参数本地伪电势：使用高斯流程回归构建的锡伪能力
Non-parametric Local Pseudopotentials with Machine Learning: a Tin Pseudopotential Built Using Gaussian Process Regression
论文作者
论文摘要
我们根据高斯过程回归（GPR）提出了新的非参数表示数学数学（PP）。材料模拟需要使用无轨道密度功能理论（DFT）来降低计算成本并允许动力学功能（KEF）仅适用于价密度的材料模拟。此外，本地PP对于DFT的准确KEF开发很重要，因为它们仅适用于有限数量的元素。我们使用GP回归来优化锡（SN）的局部PPS，以重现α-和\ b {eta} -sn的实验晶格常数，这两个阶段之间的能量差以及它们的电子结构和电荷密度分布，这些分布和电荷密度分布是用Kohn-Sham密度函数函数获得的，这些理论是使用semi-loclocal PPS获得的。基于非参数GPR的PP表示的使用避免了与使用参数函数相关的困难，并且有可能独立于先前的假设构建最佳局部PP。基于GPR的SN局部PP导致α-和\ b {eta} -TIN的重新生产的大量特性，以及与半本地PP获得的电子价密度相似。
We present novel non-parametric representation math for local pseudopotentials (PP) based on Gaussian Process Regression (GPR). Local pseudopotentials are needed for materials simulations using Orbital-Free Density Functional Theory (OF-DFT) to reduce computational cost and to allow kinetic energy functional (KEF) application only to the valence density. Moreover, local PPs are important for the development of accurate KEFs for OF-DFT as they are only available for a limited number of elements. We optimize local PPs of tin (Sn) using GP regression to reproduce the experimental lattice constants of α- and \b{eta}-Sn, the energy difference between these two phases as well as their electronic structure and charge density distributions, which are obtained with Kohn-Sham Density Functional Theory employing semi-local PPs. The use of a non-parametric GPR-based PP representation avoids difficulties associated with the use of parametrized functions and has the potential to construct an optimal local PP independent of prior assumptions. The GPR-based Sn local PP results in well-reproduced bulk properties of α- and \b{eta}-tin, and electronic valence densities similar to those obtained with semi-local PP.
