ORBKIT -一个用于量子化学波函数数据跨平台后处理的模块化Python编译器
ORBKIT - A Modular Python Toolbox for Cross-Platform Post-Processing of Quantum Chemical Wavefunction Data
摘要：
ORBKIT是一个用于后处理电子结构计算的工具箱，基于高度模块化和可移植的Python架构。该程序允许计算分子系统的电子性质的任意空间网格从其电子波函数的基组表示，以及几个网格独立的属性。所需的数据可以直接从大量量子化学程序的标准输出中提取。ORBKIT可以作为一个独立的程序来确定标准量，例如，电子密度，分子轨道，及其衍生物。ORBKIT的基石是其模块化结构。现有的基本功能可以以单独的方式排列，并且可以通过用户编写的模块轻松扩展，以确定任何其他所需的量。ORBKIT提供了多种输出格式，可以通过常见的可视化工具（VMD，Molden等）进行处理。此外，ORBKIT还拥有根据不同核构型计算的分子轨道的电子特征对其进行排序并在这些构型之间插值波函数的例程。该程序在GNU-LGPLv 3许可下开源，可在http://sourceforge.net/projects/orbkit/上免费获得。本文提供了ORBKIT的概述，特别关注其功能和适用性，并包括几个示例计算。
英文摘要：
ORBKIT is a toolbox for post-processing electronic structure calculations based on a highly modular and portable Python architecture. The program allows computing a multitude of electronic properties of molecular systems on arbitrary spatial grids from the basis set representation of its electronic wavefunction, as well as several grid-independent properties. The required data can be extracted directly from the standard output of a large number of quantum chemical programs. ORBKIT can be used as a standalone program to determine standard quantities, for example, the electron density, molecular orbitals, and derivatives thereof. The cornerstone of ORBKIT is its modular structure. The existing basic functions can be arranged in an individual way and can be easily extended by user-written modules to determine any other desired quantities. ORBKIT offers multiple output formats that can be processed by common visualization tools (VMD, Molden, etc.). Additionally, ORBKIT possesses routines to order molecular orbitals computed at different nuclear configurations according to their electronic character and to interpolate the wavefunction between these configurations. The program is open-source under GNU-LGPLv3 license and freely available at http://sourceforge.net/projects/orbkit/. This article provides an overview of ORBKIT with particular focus on its capabilities and applicability, and includes several example calculations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
