论文标题
来自多目标优化的柔软且可转移的伪电势
Soft and transferable pseudopotentials from multi-objective optimization
论文作者
论文摘要
从头算假的是现代分子和凝结物质电子结构计算的关键。在这项工作中，我们采用多目标优化来最大程度地提高伪电位柔软性，同时保持高精度和可传递性。为此，我们开发了一种配方，在该配方中，柔软度和准确性同时最大化，精确度取决于Bravais晶格结构之间再现全电子能量差异的能力，从而扫描了所得的帕累托边境，以扫描最柔软的伪能力，以提供确定的可传输能力测试的预期精度。我们采用一种进化算法来解决多目标优化问题，并将其应用于制作优化的Norm-Conconverving Vanderbilt（ONCV）pseudoptentials（https://github.com/sparc-com/sparc-x/spms-psps）的全面表。我们表明，所得的表比现有精度的现有表柔软，而比可比柔软度的表更准确。因此，这些电势可以使在广泛的应用领域加快计算，同时保持高精度。
Ab initio pseudopotentials are a linchpin of modern molecular and condensed matter electronic structure calculations. In this work, we employ multi-objective optimization to maximize pseudopotential softness while maintaining high accuracy and transferability. To accomplish this, we develop a formulation in which softness and accuracy are simultaneously maximized, with accuracy determined by the ability to reproduce all-electron energy differences between Bravais lattice structures, whereupon the resulting Pareto frontier is scanned for the softest pseudopotential that provides the desired accuracy in established transferability tests. We employ an evolutionary algorithm to solve the multi-objective optimization problem and apply it to generate a comprehensive table of optimized norm-conserving Vanderbilt (ONCV) pseudopotentials (https://github.com/SPARC-X/SPMS-psps). We show that the resulting table is softer than existing tables of comparable accuracy, while more accurate than tables of comparable softness. The potentials thus afford the possibility to speed up calculations in a broad range of applications areas while maintaining high accuracy.
