Abstract Ab initio calculations based on the density functional theory (DFT) become a vital tool in material science for understanding and predicting material properties. However, DFT calculations involve several parameters and procedures that call for deep understanding on underlying theories and preceding knowledge on certain properties of target materials. Such technicalities cost a significant amount of human time and expose practitioners to mistakes. Here, we introduce a fully automated package for DFT calculations, automated ab initio modeling of materials property package (AMP2), which aims to produce key DFT properties of crystalline materials with essentially no user intervention except for initial structural information. This is achieved through algorithms that automatically determine various technical parameters and make self-decisions during computational workflow. As results, AMP2 is material-agnostic and provides a highly accurate band structure, band gap, effective mass, density of states and dielectric constant for the given material. Notably, the package finds the antiferromagnetic ground state by applying a genetic algorithm to effective Ising models. AMP2 also addresses band-gap underestimation in semilocal functionals with help of a hybrid functional, thereby producing a more accurate band gap, even if the material turns out to be metallic within the semilocal functional. We believe that the present package will significantly expand usage of DFT calculations by making them more accessible. Program summary Program Title: AMP2 CPC Library link to program files: http://dx.doi.org/10.17632/5rdw9jv5vp.1 Licensing provisions: GPLv3 Programming language: Python Nature of problem: Conducting a b i n i t i o calculation under a fully automated protocol to obtain crystalline properties Solution method: Construct workflow to estimate material properties using the density functional theory. Ising model and genetic algorithm are used for identifying magnetic spin ordering.

中文翻译：

---

AMP2：用于从头计算晶体材料的全自动程序

摘要 基于密度泛函理论 (DFT) 的从头算计算成为材料科学中理解和预测材料特性的重要工具。然而，DFT 计算涉及多个参数和程序，需要深入理解目标材料的某些特性的基础理论和先验知识。这样的技术性花费了大量的人力，并使从业者容易犯错误。在这里，我们介绍了一个用于 DFT 计算的全自动包，即材料属性包（AMP2）的自动从头建模，旨在生成晶体材料的关键 DFT 属性，除了初始结构信息外，基本上没有用户干预。这是通过自动确定各种技术参数并在计算工作流程中自行决定的算法来实现的。因此，AMP2 与材料无关，可为给定材料提供高度准确的带结构、带隙、有效质量、状态密度和介电常数。值得注意的是，该软件包通过将遗传算法应用于有效的 Ising 模型来找到反铁磁基态。AMP2 还借助混合泛函解决了半局域泛函中的带隙低估问题，从而产生了更准确的带隙，即使材料在半局域泛函中是金属的。我们相信，当前的包将通过使它们更易于访问来显着扩展 DFT 计算的使用。程序概要 程序名称：AMP2 CPC 库程序文件链接：http://dx.doi.org/10.17632/5rdw9jv5vp.1 许可条款：GPLv3 编程语言：Python 问题性质：在全自动协议下进行abinitio计算以获得结晶特性解决方法：构建工作流程以使用密度泛函理论估计材料属性。Ising模型和遗传算法用于识别磁自旋排序。