arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.03025cond-mat.mtrl-sci
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二维范德华异质结的高效计算设计:能带对齐、晶格失配、Web应用生成与机器学习

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning

Kamal Choudhary, Kevin F Garrity, Steven T. Hartman, Ghanshyam Pilania, Francesca Tavazza

AI总结:

本研究开发了计算数据库、网络应用和机器学习模型,基于DFT生成并分类了226,779种二维异质结,验证了能带对齐结果,以加速光催化剂等新型二维材料的筛选与设计。

摘要:

我们开发了计算数据库、网络应用和机器学习(ML)模型,以加速二维(2D)异质结的设计与发现。利用基于密度泛函理论(DFT)的晶格参数和电子能带能量,针对674种非金属可剥离二维材料,我们生成了226,779种可能的异质结。根据基于非相互作用单层的相对能带对齐的Anderson规则,我们将这些异质结分为I型、II型和III型系统。我们发现II型是最常见的,而III型是最不常见的异质结类型。随后,我们根据组成元素周期表分析了每种异质结类型的化学趋势。能带对齐数据还可用于识别光催化剂和高功函数二维金属以用于接触。我们通过将结果与实验数据以及混合泛函预测进行比较来验证我们的结果。此外,我们对少数选定的系统(MoS2/WSe2、MoS2/h-BN、MoSe2/CrI3)进行了DFT计算,以将能带对齐描述与Anderson规则的预测进行比较。我们开发了网络应用,使用户能够虚拟组合二维材料并预测其性质。此外,我们使用ML工具预测二维材料的能带对齐信息。这些网络应用、工具和相关数据将通过JARVIS-Heterostructure网站(https://jarvis.nist.gov/jarvish/)发布。我们的分析、结果和开发的网络应用可应用于筛选和设计任务,例如寻找新型光催化剂、光电探测器和高功函数(WF)二维金属接触。

英文摘要:

We develop a computational database, web-apps, and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic band-energies for 674 non-metallic exfoliable 2D-materials, we generate 226,779 possible heterostructures. We classify these heterostructures into type-I, II and III systems according to Andersons rule, which is based on the relative band-alignments of the non-interacting monolayers. We find that type-II is the most common and the type-III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the periodic table of constituent elements. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS2/WSe2, MoS2/h-BN, MoSe2/CrI3), to compare the band-alignment description with the predictions from Andersons rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools and associated data will be distributed through JARVIS-Heterostructure website (https://jarvis.nist.gov/jarvish/). Our analysis, results and the developed web-apps can be applied to the screening and design applications, such as finding novel photocatalysts, photodetectors, and high-work function (WF) 2D-metal contacts.

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。

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