Chuiko Valerii, Richards Addison D S, Sánchez-Díaz Gabriela, Martínez-González Marco, Sanchez Wesley, B Da Rosa Giovanni, Richer Michelle, Zhao Yilin, Adams William, Johnson Paul A, Heidar-Zadeh Farnaz, Ayers Paul W
Department of Chemistry and Chemical Biology, McMaster University, 1280 Main St. West, Hamilton, Ontario L8S 4M1, Canada.
Department of Physics and Astronomy, McMaster University, 1280 Main St. West, Hamilton, Ontario L8S 4M1, Canada.
J Chem Phys. 2024 Oct 7;161(13). doi: 10.1063/5.0219015.
ModelHamiltonian is a free, open source, and cross-platform Python library designed to express model Hamiltonians, including spin-based Hamiltonians (Heisenberg and Ising models) and occupation-based Hamiltonians (Pariser-Parr-Pople, Hubbard, and Hückel models) in terms of 1- and 2-electron integrals, so that these systems can be easily treated by traditional quantum chemistry software programs. ModelHamiltonian was originally intended to facilitate the testing of new electronic structure methods using HORTON but emerged as a stand-alone research tool that we recognize has wide utility, even in an educational context. ModelHamiltonian is written in Python and adheres to modern principles of software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. While we anticipate that most users will use ModelHamiltonian as a Python library, we include a graphical user interface so that models can be built without programming, based on connectivity/parameters inferred from, for example, a SMILES string. We also include an interface to ChatGPT so that users can specify a Hamiltonian in plain language (without learning ModelHamiltonian's vocabulary and syntax). This article marks the official release of the ModelHamiltonian library, showcasing its functionality and scope.
ModelHamiltonian是一个免费、开源且跨平台的Python库，旨在用单电子和双电子积分来表示模型哈密顿量，包括基于自旋的哈密顿量（海森堡模型和伊辛模型）以及基于占据数的哈密顿量（巴黎-帕尔-波普尔模型、哈伯德模型和休克尔模型），以便这些系统能够被传统量子化学软件程序轻松处理。ModelHamiltonian最初旨在促进使用HORTON测试新的电子结构方法，但后来成为了一个独立的研究工具，我们认识到它具有广泛的用途，甚至在教育领域也是如此。ModelHamiltonian用Python编写，并遵循现代软件开发原则，包括全面的文档、广泛的测试、持续集成/交付协议和包管理。虽然我们预计大多数用户会将ModelHamiltonian用作Python库，但我们提供了一个图形用户界面，这样就可以基于例如从SMILES字符串推断出的连接性/参数，无需编程就能构建模型。我们还提供了一个与ChatGPT的接口，以便用户可以用自然语言指定哈密顿量（无需学习ModelHamiltonian的词汇和语法）。本文标志着ModelHamiltonian库的正式发布，展示了其功能和范围。
