材料界面的突破：自动化高通量筛选

在材料科学领域，基于密度泛函理论的高通量筛选技术可有效解析复杂的化学结构，加速了光电子、能量存储等前沿技术中新材料研发进程。尽管材料表面在能源转换过程中扮演着至关重要的角色，但由于技术上的复杂性，表面属性的研究相对落后，且目前还缺乏一套全面的高通量方案来应对这一挑战。

Fig. 1 | Schematic overview of the workflow.

由德国卡尔·冯·奥西茨基奥尔登堡大学大学物理系Caterina Cocchi教授领导的团队，提出了一种自动化计算程序。这一流程能够自动计算并处理基于密度泛函理论（DFT）得到的表面刻面的结构和电子性质。作者呈现了一个专门设计的工作流程，用于处理从无机块体晶体生成的准二维薄层模型。该工作流基于内部实现的python库，并与AiiDA基础设施以及已建立的ase和spglib库进行了高效接口对接。

Fig. 2 | Bulk crystals.

作者以铯碲化物(Cs₂Te)为例描述了该工具的特点。Cs₂Te是一种在粒子加速器光阴极中使用的知名半导体材料。与大多数计算预测的材料一样，Cs₂Te并不以单晶形式存在，而是通过蒸发沉积生长，形成具有共存相和复杂表面形貌的多晶样品。这些样品难以被系统地再现和表征。正因为缺乏有关Cs₂Te基本表面性质的实验信息，它成为了一个理想的案例，以展示作者在这项工作中引入的计算工作流程的有效性。

Fig. 3 | Surface types. Schematic view of a a stoichiometric surface, b a nonstoichiometric surface with excess Cs atoms, and c a non-stoichiometric surface with an excess of Te atoms. White and black dots indicate Cs and Te atoms, respectively, and dashed lines highlight the unit cell boundaries.

通过这项工作，作者分析了这种化合物形成低米勒指数薄层的特性，考虑了所有可能的终止面，并集中研究了表面稳定性、带隙和电离能等关键性质。作者将这些可观测量与薄层的结构特征联系起来，并特别弛豫了最外层的原子层，因为这些层对表面特性影响最为关键。

Fig. 4 | Surface stability and under-coordination.

通过这些计算分析，作者揭示了表面结构与其功能性能之间的联系。这些发现不仅增强了对材料表面电子性质的理解，还提供了可靠的数据支持，有助于预测实验中可能观察到的结果，从而为未来的材料设计和应用开辟了新的道路。该文近期发表于npj Computational Materials 10: 38 (2024).

Fig. 5 | Electronic band structures and PDOS of selected facets.

Editorial Summary

Materials interface science: Automated high-throughput surface screening

In the field of material science, high-throughput screening techniques based on density functional theory can effectively decipher complex chemical structures, accelerating the development of new materials in cutting-edge technologies such as optoelectronics and energy storage. Despite the critical role that material surfaces play in energy conversion processes, research on surface properties has lagged due to technical complexities, and a comprehensive high-throughput approach to address these challenges is currently lacking.

Fig. 6 | Electronic properties of all facets.

A team led by Prof. Caterina Cocchi from Carl von Ossietzky Universität Oldenburg, Physics Department, Germany, filled the gap by presenting an automated computational procedure to calculate and post-process structural and electronic properties of surface facets from DFT in an automated fashion. The authors present a workflow specifically aimed to deal with slabs generated from inorganic bulk crystals and based on an in-house implementedpython library interfaced with the AiiDA infrastructure as well as with the established libraries ase and spglib. The authors describe the features of the implemented tool with the example of Cs2Te, a known semiconductor for photocathodes in particle accelerators. Like most computationally predicted materials, Cs2Te is not available in single-crystalline form: it is grown via vapor deposition and gives rise to polycrystalline samples with coexisting phases and complex surface morphologies that are hard to be systematically reproduced and characterized. This lack of experimental information about the fundamental surface properties of Cs2Te makes it the ideal case study for the computational workflow introduced in this work. The characteristics of the low-Miller-index slabs of this compound are analyzed, taking into account all possible terminations and focusing on key properties such as surface stability, band gap, and ionization potential. The authors relate these observables with the structural fingerprints of the slabs posing particular emphasis on the relaxation of the outermost atomic layers, which are known to most critically impact the characteristics of the surfaces. Correlations identified among computed quantities represent an added value for insight and predictions of measurable output. This article was recently published in npj Computational Materials 10: 38 (2024).

原文Abstract及其翻译

Automated analysis of surface facets: the example of cesium telluride（表面刻面的自动化分析：以铯碲化物为例）

Holger-Dietrich Saßnick & Caterina Cocchi

Abstract High-throughput screening combined with ab initio calculations is a powerful tool to explore technologically relevant materials characterized by complex configurational spaces. Despite the impressive developments achieved in this field in the last few years, most studies still focus on bulk materials, although the relevant processes for energy conversion, production, and storage occur on surfaces. Herein, we present an automatized computational scheme that is capable of calculating surface properties in inorganic crystals from first principles in a high-throughput fashion. After introducing the method and its implementation, we showcase its applicability, focusing on four polymorphs of Cs₂Te, an established photocathode material for particle accelerators, considering slabs with low Miller indices and different terminations. This analysis gives insight into how the surface composition, accessible through the proposed high-throughput screening method, impacts the electronic properties and, ultimately, the photoemission performance. The developed scheme offers new opportunities for automated computational studies beyond bulk materials.

摘要高通量筛选技术结合从头算(ab initio)计算，已成为研究那些构型空间复杂的技术材料的有力工具。尽管这一领域在过去几年已经取得了显著进展，但大部分研究依旧主要关注于体材料。然而能量转换、产生以及储存等重要过程实际却发生在材料表面。在本文中，我们提出了一个自动化的计算框架，它能够从第一性原理出发，以高通量方式精确计算无机晶体的表面性质。在详细说明了这种方法及其实现之后，我们通过几个实例来展示它的实际应用能力，特别是聚焦于四种不同形态的铯碲化物(Cs₂Te)——这是一种在粒子加速器中被广泛使用的光阴极材料。我们考虑了具有不同终止面和低米勒指数的晶体薄层模型。这种分析深入探讨了表面组成如何影响电子性质，以及最终的光电发射性能，而这些组成是可以通过我们提出的高通量筛选方法来实现的。我们所开发的计算方案不仅为材料科学的研究人员提供了新的视角，还为自动化计算研究拓宽了路径，使其能够超越传统的体材料分析，迈向更加广阔的应用前景。

原创文章，作者：计算搬砖工程师，如若转载，请注明来源华算科技，注明出处：https://www.v-suan.com/index.php/2024/03/06/72575de482/

​材料界面的突破：自动化高通量筛选

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材料界面的突破：自动化高通量筛选