Transactions of Nonferrous Metals Society of China The Chinese Journal of Nonferrous Metals

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中國(guó)有色金屬學(xué)報(bào)

ZHONGGUO YOUSEJINSHU XUEBAO

第30卷    第8期    總第257期    2020年8月

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文章編號(hào):1004-0609(2020)-08-1887-08
基于SISSO和機(jī)器學(xué)習(xí)方法的鈣鈦礦結(jié)構(gòu)的穩(wěn)定性預(yù)測(cè):新型容許因子建立與驗(yàn)證
胡紅青1,吳邵剛1,郭治廷1,周高鋒1,戴東波1,魏 曉1, 2,張惠然1, 2

(1. 上海大學(xué) 計(jì)算機(jī)工程與科學(xué)學(xué)院,上海 200444;
2. 上海大學(xué) 材料基因組工程研究院,上海 200444
)

摘 要: 由于鈣鈦礦型材料具有廣泛的應(yīng)用前景,因此對(duì)其結(jié)構(gòu)及物理、化學(xué)性質(zhì)的研究一直是材料研究領(lǐng)域的熱點(diǎn)之一。其中,利用容許因子(Tolerance factor)來(lái)預(yù)測(cè)鈣鈦礦型材料的結(jié)構(gòu)穩(wěn)定性可以幫助研究者發(fā)現(xiàn)更多的新型功能材料,而傳統(tǒng)的基于離子半徑定義的容許因子tIR存在一定的局限性。本文基于SISSO(Sure independence screening and sparsifying operator)方法和鍵價(jià)模型提出一種新型的容許因子τBV,其可以有效地避免由離子半徑帶來(lái)的局限性。本工作使用機(jī)器學(xué)習(xí)中的決策樹(shù)算法建立容許因子驗(yàn)證模型,實(shí)驗(yàn)結(jié)果表明,新型容許因子τBV可以很好地預(yù)測(cè)ABO3型化合物是否具有鈣鈦礦結(jié)構(gòu),并大大提高了預(yù)測(cè)精度。

 

關(guān)鍵字: 鈣鈦礦;結(jié)構(gòu)穩(wěn)定性;SISSO;新型容許因子

New tolerance factor based on SISSO and machine learning for predicting stability of perovskite structure
HU Hong-qing1, WU Shao-gang1, GUO Zhi-ting1, ZHOU Gao-feng1, DAI Dong-bo1, WEI Xiao1, 2, ZHANG Hui-ran1, 2

1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;
2. Materials Genome Institute, Shanghai University, Shanghai 200444, China

Abstract:Due to the wide application prospects of perovskite materials, research on their structures and physical and chemical properties of perovskite materials has been one of the hot topics in the field of materials research. Among them, predicting the stability of perovskite structure with the help of tolerance factor can help researchers discover more new functional materials. The conventional tolerance factor tIR for determining the stability of the perovskite structure based on ion radius has certain shortcomings and limitation. In view of this, this work proposes a new type of tolerance factor τBV based on the bond valence model using the SISSO (sure independence screening and sparsifying operator) method which can effectively avoid the defect limitation caused by the ionic radius. This work uses the decision tree algorithm in machine learning to establish the new tolerance factor verification model and the results show that the new tolerance factor τBV can excellently predict whether the ABO3 compound is perovskite or non-perovskite, which greatly improves the prediction accuracy.

 

Key words: perovskites; structural stability; sure independence screening sparsifying operator (SISSO); new tolerance factor

ISSN 1004-0609
CN 43-1238/TG
CODEN: ZYJXFK

ISSN 1003-6326
CN 43-1239/TG
CODEN: TNMCEW

主管:中國(guó)科學(xué)技術(shù)協(xié)會(huì) 主辦:中國(guó)有色金屬學(xué)會(huì) 承辦:中南大學(xué)
湘ICP備09001153號(hào) 版權(quán)所有:《中國(guó)有色金屬學(xué)報(bào)》編輯部
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