arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2201.09977cond-mat.mtrl-sci
收藏

功能的高通量逆向设计与贝叶斯优化:二维化合物中的自旋劈裂

High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds

Gabriel M. Nascimento, Elton Ogoshi, Adalberto Fazzio, Carlos Mera Acosta, Gustavo M. Dalpian

AI总结:

本文构建了二维材料自旋劈裂数据库,提出整合逆向设计与贝叶斯推断优化的材料设计工作流程,在C2DB中分类出358种材料,并验证了其在自旋电子学应用中的普适性。

摘要:

自旋电子器件的发展需要具有某种自旋劈裂(SS)的材料。在本数据描述符中,我们构建了一个包含2D材料中从头算计算的SS的数据库。不仅如此,我们提出了一种整合逆向设计方法和贝叶斯推断优化的材料设计工作流程。我们以预测用于自旋电子应用的SS原型作为所提出工作流程的说明性示例。预测过程从建立设计原则(目标性质背后的物理机制)开始,这些原则用作材料筛选的过滤器,随后进行密度泛函理论(DFT)计算。将此过程应用于C2DB数据库,我们根据价带和/或导带处的SS类型识别并分类了358种2D材料。贝叶斯优化捕获了用于合理化设计2D材料的趋势,这些材料具有用于潜在自旋电子学应用的理想带隙和SS条件。我们的工作流程可应用于任何其他材料性质。

英文摘要:

The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property.

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。

↑