Department of Chemistry and Physics, Indiana State University, Terre Haute, Indiana, 47809.
Department of Computer Science, University of Wisconsin-Madison, Madison, Wisconsin, 53705.
J Comput Chem. 2019 Sep 5;40(23):2028-2035. doi: 10.1002/jcc.25855. Epub 2019 May 11.
We describe the formal algorithm and numerical applications of a novel convex quadratic programming (QP) strategy for performing the variational minimization that underlies natural resonance theory (NRT). The QP algorithm vastly improves the numerical efficiency, thoroughness, and accuracy of variational NRT description, which now allows uniform treatment of all reference structures at the high level of detail previously reserved only for leading "reference" structures, with little or no user guidance. We illustrate overall QPNRT search strategy, program I/O, and numerical results for a specific application to adenine, and we summarize more extended results for a data set of 338 species from throughout the organic, bioorganic, and inorganic domain. The improved QP-based implementation of NRT is a principal feature of the newly released NBO 7.0 program version. © 2019 Wiley Periodicals, Inc.
我们描述了一种新颖的凸二次规划 (QP) 算法的形式及其在自然共振理论 (NRT) 中变分最小化的数值应用。QP 算法极大地提高了变分 NRT 描述的数值效率、彻底性和准确性，现在允许在以前仅为主要“参考”结构保留的高水平细节上统一处理所有参考结构，而用户指导很少或没有。我们说明了针对腺嘌呤的特定应用的整体 QPNRT 搜索策略、程序 I/O 和数值结果，并总结了来自有机、生物有机和无机领域的 338 种物质数据集的更扩展的结果。改进的基于 QP 的 NRT 实现是新发布的 NBO 7.0 程序版本的主要特点。© 2019 Wiley Periodicals, Inc.
