论文标题
有效的可变细胞形状几何优化
Efficient variable cell shape geometry optimization
论文作者
论文摘要
提出了一种快速可靠的几何优化算法，该算法同时优化了原子位置和晶格向量。使用一系列基准测试表明，在大多数情况下，本文介绍的方法优于大多数的标准优化方法，例如量子浓缩咖啡和VASP等流行代码。为了激发此处介绍的可变细胞形状优化方法，对晶格黑森基质的特征值进行了彻底研究。结果表明，它们会根据细胞的形状和细胞内部的颗粒数而改变。对于某些细胞形状，晶格基质的结果数量可以相对于颗粒数四倍地生长。通过可以应用于所有可变细胞形状优化方法的坐标转换，消除了晶格Hessian矩阵的不良条件。
A fast and reliable geometry optimization algorithm is presented that optimizes atomic positions and lattice vectors simultaneously. Using a series of benchmarks, it is shown that the method presented in this paper outperforms in most cases the standard optimization methods implemented in popular codes such as QUANTUM ESPRESSO and VASP. To motivate the variable cell shape optimization method presented in here, the eigenvalues of the lattice Hessian matrix are investigated thoroughly. It is shown that they change depending on the shape of the cell and the number of particles inside the cell. For certain cell shapes the resulting condition number of the lattice matrix can grow quadratically with respect to the number of particles. By a coordinate transformation which can be applied to all variable cell shape optimization methods, the undesirable conditioning of the lattice Hessian matrix is eliminated.
