作為納米技術(shù)研究的新領(lǐng)域,二維(2D)鐵磁(FM)材料在自旋電子學(xué)、傳感器、存儲(chǔ)技術(shù)等工程領(lǐng)域有巨大的應(yīng)用潛力。目前已經(jīng)有大量的二維鐵磁材料被計(jì)算預(yù)測(cè),包括一些通用的二維材料數(shù)據(jù)庫(kù),包含數(shù)百到數(shù)千個(gè)條目。然而,這些數(shù)據(jù)庫(kù)都沒(méi)有包含與實(shí)際應(yīng)用相關(guān)的2D鐵磁材料的最關(guān)鍵參數(shù):轉(zhuǎn)變溫度或居里點(diǎn)(TC )。這是因?yàn)門CIS的計(jì)算確定是一個(gè)非常復(fù)雜的過(guò)程,它涉及到基于人工啟發(fā)的基態(tài)和低能自旋組態(tài)搜索。
來(lái)自印度科學(xué)院(IISc)電子系統(tǒng)工程系納米器件研究實(shí)驗(yàn)室Arnab Kabiraj團(tuán)隊(duì)開發(fā)了一個(gè)程序,該程序執(zhí)行基于第一原理的計(jì)算,然后進(jìn)行基于海森堡模型的蒙特卡羅模擬,以從任何磁性二維材料晶體結(jié)構(gòu)準(zhǔn)確預(yù)測(cè)居里點(diǎn)。基于此代碼的程序能夠以高通量的方式執(zhí)行計(jì)算,使用此代碼能夠從合適的數(shù)據(jù)庫(kù)確定材料的居里點(diǎn)。令人驚訝的是,786種材料中有47%被歸為FM,成功地測(cè)定了157種材料的TC 等磁性能。對(duì)于一些材料,預(yù)測(cè)結(jié)果與實(shí)驗(yàn)測(cè)量的TC 非常接近,驗(yàn)證了高通量方法的準(zhǔn)確性。為了更快地發(fā)現(xiàn)高TC材料,進(jìn)一步開發(fā)了使用這157個(gè)數(shù)據(jù)點(diǎn)的機(jī)器學(xué)習(xí)(ML)流水線。使用這個(gè)ML模型,可以從文獻(xiàn)和其他數(shù)據(jù)庫(kù)中識(shí)別出一些高居里溫度的2DFM材料。本方法采用的信息學(xué)在準(zhǔn)確性和效率之間達(dá)到了最佳平衡,提供了前所未有的機(jī)會(huì)來(lái)比較大量具有不同結(jié)構(gòu)的材料的磁性,這可能會(huì)導(dǎo)致對(duì)二維磁性的許多新的見解。本研究證明了機(jī)器學(xué)習(xí)模型可以非常精確地捕捉與溫度有關(guān)的自旋翻轉(zhuǎn)的復(fù)雜過(guò)程。因此,可以大大提升計(jì)算材料的效率,并以促進(jìn)二維磁性的快速篩選和實(shí)際應(yīng)用。
該文近期發(fā)表于npj Computational Materials 6: 35 (2020),英文標(biāo)題與摘要如下,點(diǎn)擊左下角“閱讀原文”可以自由獲取論文PDF。
High-throughput discovery of high Curie point two-dimensional ferromagnetic materials
Arnab Kabiraj, Mayank Kumar and Santanu Mahapatra
Databases for two-dimensional materials host numerous ferromagnetic materials without the vital information of Curie temperature since its calculation involves a manually intensive complex process. In this work, we develop a fully automated, hardwareaccelerated, dynamic-translation based computer code, which performsfirst principles-based computations followed by Heisenberg model-based Monte Carlo simulations to estimate the Curie temperature from the crystal structure. We employ this code to conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end machine learning model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. Such material informatics, which agrees well with recent experiments, is expected to foster practical applications of two-dimensional magnetism.
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