论文标题

信任区域增强了针对特定州和州平均CASSCF波功能的Hessian实施

A trust-region augmented Hessian implementation for state-specific and state-averaged CASSCF wave functions

论文作者

Helmich-Paris, Benjamin

论文摘要

在这项工作中,我们为州特异性(SS)和状态平均(SA)完整的主动空间自洽场(CASSCF)波函数提出了一步的二阶转化器。强大的收敛是通过使用信任区域增强Hessian(TRAH)算法实现的。为避免数值不稳定性,采用了变异配置参数的指数参数化,该参数符合非冗余正交补体的基础。这是SS-CASSCF的常见方法,在这项工作中扩展到SA-CASSCF波函数。我们的实施是不可或缺的直接,并且基于中间体,这些中间体是在稀疏的原子轨道或小型活性分子轨道基础上进行的。因此,它受益于与有效的积分分解技术的组合,例如定位分辨率或用于交换近似值的链链。这有助于对具有231个原子和5154个基函数的Ni(II)络合物等大分子进行计算。 TRAH-CASSCF的运行时性能与其他近似和完整二阶算法的其他最先进的实现具有竞争力。与复杂的一阶转化器相比,TRAH-CASSCF计算通常需要更多的迭代以达到收敛,因此具有更长的运行时间。但是,即使一阶算法失败,TRAH-CASSCF计算仍然可靠地收敛到真正的最小值。

In this work, we present a one-step second-order converger for state-specific (SS) and state-averaged (SA) complete active space self-consistent field (CASSCF) wave functions. Robust convergence is achieved through step restrictions using a trust-region augmented Hessian (TRAH) algorithm. To avoid numerical instabilities, an exponential parametrization of variational configuration parameters is employed, which works with a nonredundant orthogonal complement basis. This is a common approach for SS-CASSCF and is extended to SA-CASSCF wave functions, in this work. Our implementation is integral direct and based on intermediates that are formulated either in the sparse atomic-orbital or small active molecular-orbital basis. Thus, it benefits from a combination with efficient integral decomposition techniques, such as the resolution-of-the-identity or the chain-of-spheres for exchange approximations. This facilitates calculations on large molecules such as a Ni(II) complex with 231 atoms and 5154 basis functions. The runtime performance of TRAH-CASSCF is competitive with other state-of-the-art implementations of approximate and full second-order algorithms. In comparison with a sophisticated first-order converger, TRAH-CASSCF calculations usually take more iterations to reach convergence and, thus, have longer runtimes. However, TRAH-CASSCF calculations still converge reliably to a true minimum even if the first-order algorithm fails.

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