原子级全局优化 X：一个用于原子结构优化的 Python 软件包
Atomistic Global Optimization X: A Python package for optimization of atomistic structures
AI总结：
本文提出 AGOX 框架及 Python 软件包，以模块化方式构建和组合全局优化算法，并通过低成本示例和 DFT 级金属氮化物纳米团簇优化问题验证其有效性。
摘要：
从第一性原理出发对材料性质进行建模和理解，需要掌握其背后的原子级结构知识。这需要知道所有涉及原子的个体身份和位置。对于大分子、纳米颗粒、团簇，以及非晶态和固态材料的表面、界面和体相，获取此类信息是一个困难的高维全局优化问题。然而，机器学习技术在材料科学中的兴起带来了许多引人注目的进展，可能加速此类结构搜索。新方法的复杂性确立了以一种高效方式进行实验并将其组装成全局优化算法的必要性。在本文中，我们介绍了 Atomistic Global Optimization X（AGOX）框架和代码，作为一种可定制的方法，用于构建高效的全局优化算法。我们描述了一种模块化的全局优化算法表达方式，并采用现代编程实践在免费提供的 AGOX Python 软件包中实现这种模块化。我们分析了两个全局优化问题的示例：一个是计算成本较低的示例，用于展示 AGOX 能够表达多种全局优化算法。另一个示例中，AGOX 被用于解决一个复杂的原子级优化问题，即在密度泛函理论（DFT）层面描述的嵌入石墨烯片中的金属氮化物纳米团簇。
英文摘要：
Modelling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual identity and position of all involved atoms. Obtaining such information for macro-molecules, nano-particles, clusters, and for the surface, interface, and bulk phases of amorphous and solid materials represents a difficult high dimensional global optimization problem. The rise of machine learning techniques in materials science has, however, led to many compelling developments that may speed up such structure searches. The complexity of the new methods have established the necessity for an efficient way of experimenting with and assembling them into global optimization algorithms. In this paper we introduce the Atomistic Global Optimization X (AGOX) framework and code, as a customizable approach to building efficient global optimization algorithms. A modular way of expressing global optimization algorithms is described and modern programming practices are used to enable that modularity in the freely available AGOX python package. Two examples of global optimization problems are analyzed: One that is computationally inexpensive which is used to showcase that AGOX enables the expression of multiple global optimization algorithms. As the other example, AGOX is used for solving a complex atomistic optimization problem for a metal-nitride nano-cluster embedded in a graphene sheet as described at the density functional theory (DFT) level.
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。
