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Unlocking the potential of weberite-type metal fluorides in electrochemical energy storage (釋放氟鋁鎂鈉石型金屬氟化物在電化學儲能中的潛力)
Holger EuchnerOliver Clemens & M. Anji Reddy 
npj Computational Materials 5:31 (2019)
doi:s41524-019-0166-3
Published online:6 March 2019
Abstract| Full Text | PDF OPEN

摘要:鈉離子電池(NIBs)是有望取代鋰離子電池(LIB)的替代電池技術中的先行者,然而鈉離子電池的比能量明顯低于鋰離子電池,這主要是由于鈉嵌入型正極材料具有較低的反應電位和較高的分子量。NIB要想與LIB的高能量密度競爭,它就需要高電壓的正極材料。本研究報告了對Weberite型鈉金屬氟化物(SMF)的理論研究,該氟化物是一種新型的高電壓和高能量密度的材料,迄今為止尚未作為NIB的正極材料而被研究。Weberite型結構對于含鈉過渡金屬氟化物非常有利,其中多種過渡金屬組合(M,M')均屬于相應的Na2MM'F7結構。本工作通過計算研究了一系列具有Weberite型結構的已知和假設的化合物,以評估它們作為NIB正極材料的潛力。WeberiteSMF顯示出Na+擴散的二維路徑,具有異常低的活化能壘。高能量密度與Na+的低擴散勢壘結合,使得這種類型的化合物有望成為NIB正極材料的候選。   

Abstract:Sodium-ion batteries (NIBs) are a front-runner among the alternative battery technologies suggested for substituting the state-of-the-art lithium-ion batteries (LIBs). The specific energy of Na-ion batteries is significantly lower than that of LIBs, which is mainly due to the lower operating potentials and higher molecular weight of sodium insertion cathode materials. To compete with the high energy density of LIBs, high voltage cathode materials are required for NIBs. Here we report a theoretical investigation on weberite-type sodium metal fluorides (SMFs), a new class of high voltage and high energy density materials which are so far unexplored as cathode materials for NIBs. The weberite structure type is highly favorable for sodium-containing transition metal fluorides, with a large variety of transition metal combinations (M, M’) adopting the corresponding Na2MM’F7 structure.. A series of known and hypothetical compounds with weberite-type structure were computationally investigated to evaluate their potential as cathode materials for NIBs. Weberite-type SMFs show two-dimensional pathways for Na+ diffusion with surprisingly low activation barriers. The high energy density combined with low diffusion barriers for Na+ makes this type of compounds promising candidates for cathode materials in NIBs. 

Editorial Summary

New hope of sodium-ion batteries: Weberite-type metal fluoridesNa離子電池的新希望:weberite型金屬氟化物

該研究考查了一系列擬作為NIB正極材料的weberite鈉金屬氟化物。來自德國烏爾姆亥姆霍茲研究所M. Anji Reddy領導的研究小組,篩查了一些真實和虛擬的化合物,以揭示weberite金屬氟化物作為NIB正極材料的潛力。雖然他們將研究限定于考查僅一定數量的化合物,但這些材料的范圍及對它們的各種修飾的可能性將非常大。除了不同元素組合外,通過多種物種填充每個金屬亞晶格也可能是有意義的,這些策略可促進更快的擴散路徑的形成同時又保持高的能量密度,以實現化合物的進一步優化。按照這一策略,他們建議將一些高能量密度的材料與一定量的Ti合金化,以產生快速擴散通道。他們的研究從理論角度證明了這些材料具有作為NIB正極的潛力,作者希望未來的研究會開啟這些化合物的合成和實驗測試。

A series of weberite-type sodium metal fluorides as cathode materials for NIBs have investigated. A group led by M. Anji Reddy from the Helmholtz Institute Ulm, Germany, screened real and virtual compounds revealing the potential of weberite-type metal fluorides as cathode materials for NIBs. They limited their study to the investigation of only a certain number of compounds, but the playground for these materials in combination with their variety of possible modifications might be even larger. Apart from other element combination, they highlighted that it may also be of interest to populate each of the metal sublattices by more than one species, which could allow for further optimization of the compounds by facilitating faster diffusion pathways while maintaining high energy density. Following this strategy, they suggested to alloy some high-energy density materials with a certain amount of Ti to create fast diffusion channels. With the potential of these materials being demonstrated from the theoretical viewpoint, the authors aim to trigger the synthesis and experimental testing of these compounds in future studies.

Topological superconducting phase in high-Tc superconductor MgB2 with Dirac–nodal-line fermions (Tc超導體MgB2中的拓撲超導相具有Dirac節點線費米子)
Kyung-Hwan JinHuaqing HuangJia-Wei MeiZheng LiuLih-King Lim & Feng Liu 
npj Computational Materials 5:57 (2019)
doi:s41524-019-0191-2
Published online:3 March 2019
Abstract| Full Text | PDF OPEN

摘要:拓撲超導體是一種有趣且難以捉摸的量子相,具有拓撲?;さ奈藪侗礱?/span>/邊緣態特征,存在于體材超導帶隙中,包含了Majorana費米子。不幸的是,所有目前已知的拓撲超導體轉變溫度都非常低,限制了Majorana費米子的實驗測量。本研究發現,在眾所周知的傳統高溫超導體MgB2中存在拓撲狄拉克節線態。第一性原理計算表明,受空間反演和時間反演對稱性?;さ?/span>Dirac節點線結構具有獨特的一維色散特征,連接著電子和空位Dirac態。最重要的是,我們用傳統的s波超導帶隙實現了拓撲超導相,用MgB2薄膜的拓撲邊緣模式證明了手性邊緣狀態。我們的這一發現可以在高溫下實現對Majorana費米子的實驗測量。   

Abstract:Topological superconductors are an intriguing and elusive quantum phase, characterized by topologically protected gapless surface/edge states residing in a bulk superconducting gap, which hosts Majorana fermions. Unfortunately, all currently known topological superconductors have a very low transition temperature, limiting experimental measurements of Majorana fermions. Here we discover the existence of a topological Dirac–nodal-line state in a well-known conventional high-temperature superconductor, MgB2. First-principles calculations show that the Dirac–nodal-line structure exhibits a unique one-dimensional dispersive Dirac–nodal line, protected by both spatial-inversion and time-reversal symmetry, which connects the electron and hole Dirac states. Most importantly, we show that the topological superconducting phase can be realized with a conventional s-wave superconducting gap, evidenced by the topological edge mode of the MgB2 thin films showing chiral edge states. Our discovery may enable the experimental measurement of Majorana fermions at high temperature. 

Editorial Summary

Topological superconducting phase in high-Tc superconductor MgB2 with Dirac–nodal-line fermionsTc超導體MgB2中的拓撲超導相具有Dirac節點線費米子

本研究在高溫超導體MgB2中揭示了一種有趣的反演和時間反演對稱?;さ?/span>Dirac節點線態。來自由美國猶他大學和中國量子物質協同創新中心的劉鋒領導的團隊,使用第一性原理計算和模型分析,揭示了這種Dirac節點線態。最重要的是,他們用傳統的s波超導帶隙實現了拓撲超導相,用MgB2薄膜的拓撲邊緣模式證明了手性邊緣狀態。他們的發現為在前所未有的高溫下研究拓撲超導相提供了一個令人興奮的機會,并可能為構建新型量子和自旋電子器件,提供有前途的材料平臺。有可能在高溫下實現對Majorana費米子的實驗測量,將激發未來更廣泛的超導材料拓撲相(如蜂窩狀層狀晶格結構)研究。

An intriguing inversion and time-reversal symmetry- protected Dirac nodal line state is revealed in a high-temperature superconductor MgB2. A team led by Feng Liu from the University of Utah, USA, and Collaborative Innovation Center of Quantum Matter, China, performed first-principles calculations to discover the existence of a topological Dirac–nodal-line state in a well-known conventional high-temperature superconductor, MgB2. Most importantly, they showed that the topological superconducting phase can be realized with a conventional s-wave superconducting gap, evidenced by the topological edge mode of the MgB2 thin films showing chiral edge states. Their finding provokes an exciting opportunity to study a topological superconducting phase in an unprecedented high temperature and may offer a promising material platform to building novel quantum and spintronics devices. The authors’ discovery may enable the experimental measurement of Majorana fermions at high temperature. And it will stimulate future studies of topological phases in a broader range of superconducting materials, such as a honeycomb lattice layered structure.

Predicting surface deformation during mechanical attrition of metallic alloys (預測金屬合金機械磨損過程中的表面變形)
Shan Cecilia CaoXiaochun ZhangJian LuYongli WangSan-Qiang Shi & Robert O. Ritchie 
npj Computational Materials 5:36 (2019)
doi:s41524-019-0171-6
Published online:15 March 2019
Abstract| Full Text | PDF OPEN

摘要:新的材料設計和加工技術能幫助我們制造出性能更堅固和更堅韌材料,為此人們在科學和工程領域都作了廣泛的努力。而其中最為有效的是一種用于增強合金中損傷容限的方法,作為表面納米結晶技術,是采用高功率超聲波振動數百個小硬球并將它們以高速度轟擊材料表面的技術,或稱為表面機械磨損處理。然而,目前很少有針對該技術相關的精確的力學機制和加工參數的影響機制研究。由于表面機械磨損處理是動態塑性變形過程,本研究使用隨機沖擊變形作為研究沖擊變形與加工參數之間關系的手段,著眼于球尺寸、沖擊速度、球密度和動能。使用分析和數值解決方案,檢查了隨機沖擊引起的凹痕尺寸和相關塑性區域的深度,并用奧氏體不銹鋼進行實驗,且驗證了結果。此外,本研究還開發了全局隨機影響和局部影響頻率新型模型,以分析隨機影響覆蓋的統計特征,并描述了隨機多重沖擊的影響,這些影響更能反映表面機械磨損處理的機制及材料的變形。我們相信,此模型將成為進一步開發金屬材料強化技術的必要基礎,為開發節能低廉高新能的新型輕材料做出貢獻。   

Abstract:Extensive efforts have been devoted in both the engineering and scientific domains to seek new designs and processing techniques capable of making stronger and tougher materials. One such method for enhancing such damage-tolerance in metallic alloys is a surface nano-crystallization technology that involves the use of hundreds of small hard balls which are vibrated using high-power ultrasound so that they impact onto the surface of a material at high speed (termed Surface Mechanical Attrition Treatment or SMAT). However, few studies have been devoted to the precise underlying mechanical mechanisms associated with this technology and the effect of processing parameters. As SMAT is dynamic plastic deformation process, here the modeling of random impact deformation is used as a means to investigate the relationship between impact deformation and the parameters involved in the processing, specifically ball size, impact velocity, ball density and kinetic energy. Using analytical and numerical solutions, we examine the size of the indents and the depths of the associated plastic zones induced by random impacts, with results verified by experiment in austenitic stainless steels. In addition, global random impact and local impact frequency models are developed to analyze the statistical characteristics of random impact coverage, together with a description of the effect of random multiple impacts, which are more reflective of SMAT. We believe that these models will serve as a necessary foundation for further, and more energy-efficient, development of such surface nano-crystalline processing technologies for the strengthening of metallic materials. 

Editorial Summary

Surface deformation during mechanical attrition: Predicting model合金表面變形:預測模型

本項研究重點描述了一種新開發的模型、一種可行的方法,用于表征表面機械磨損處理針對各種金屬材料產生的力學效應。來自中國科學院上海應用物理研究所的張小春博士,香港城市大學呂堅教授和美國加州大學/勞倫斯國家實驗室的Robert O. Ritchie教授及加州大學伯克利分校博士后曹珊博士等合作,使用隨機沖擊變形作為研究沖擊變形與加工參數之間關系的手段,尤其是球尺寸、沖擊速度、球密度和動能等參數。使用分析和數值解決方案,他們檢查了隨機沖擊引起的凹痕尺寸和相關塑性區域的深度,并用奧氏體不銹鋼實驗驗證了模擬結果。正如數值解決方案所示,使用同樣的分析公式可以獲得不同材料的材料常數。因此,通過使用任意沖擊參數所獲得材料常數,就可很容易地將所分析公式應用于其他金屬材料。他們通過實驗驗證的結果,可以為1)更好地認識金屬的表面機械磨損處理加工機制,以及2)金屬合金中微結構演變的進一步發展,提供必要的基礎,提高其強度、延展性、硬度和韌性。

A new model which represents a viable approach for characterizing the mechanical effect of Surface Mechanical Attrition Treatment for a wide range of metallic materials is developed. A team co-led by Xiaochun Zhang and Robert O. Ritchie from the Shanghai Institute of Applied Physics, CAS, China, and the University of California and the Lawrence Berkeley National Laboratory, USA, respectively, used random impact deformation as a means to investigate the relationship between impact deformation and the parameters involved in the processing, specifically ball size, impact velocity, ball density and kinetic energy. Using analytical and numerical solutions, they examine the size of the indents and the depths of the associated plastic zones induced by random impacts, with results verified by experiment in austenitic stainless steels. As indicated in the numerical solution, the material constants can be obtained for other materials using these same analytical formulas. As such, their analytical formulations can be readily applied to other metallic materials by employing any impact parameters to obtain the material constants. Their results, which were verified by experiment, can provide a necessary foundation for an improved understanding of the procedure of Surface Mechanical Attrition Treatment for metals, as well as for the further development of microstructural evolution in metallic alloys for improved strength, ductility, hardness and toughness.

Predicting interfacial thermal resistance by machine learning (用機器學習預測界面熱阻)
Yen-Ju WuLei Fang & Yibin Xu 
npj Computational Materials 5:56 (2019)
doi:s41524-019-0193-0
Published online:3 May 2019
Abstract| Full Text | PDF OPEN

摘要:兩種材料之間的界面熱阻(ITR)受到多種因素影的影響,使得ITR預測成為一個高維數學問題?;餮笆墻餼穌飧鑫侍獾囊恢志糜行У姆椒?。本研究基于實驗數據報道了ITR預測模型。ITR的物理、化學和材料屬性分為三組描述符,三種算法用于這些模型。這些描述符有助于模型減少預測值和實驗值之間的錯配,并實現了預測能力高達96%。由293種材料組成的80,000多種材料體系作為預測模型的輸入信息,在三種不同算法的前100個高ITR預測中,使用至少兩種算法重復預測了25種材料體系。Bi / Si作為25種材料體系中的一種,在我們之前的工作中實現了超低熱導率。我們相信所預測的高ITR材料體系是熱電應用的潛在候選者。該研究提出了用機器學習作為熱管理材料探索的策略。   

Abstract:Various factors affect the interfacial thermal resistance (ITR) between two materials, making ITR prediction a high-dimensional mathematical problem. Machine learning is a cost-effective method to address this. Here, we report ITR predictive models based on experimental data. The physical, chemical, and material properties of ITR are categorized into three sets of descriptors, and three algorithms are used for the models. hose descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%. Over 80,000 material systems composed of 293 materials were inputs for predictions. Among the top-100 high-ITR predictions by the three different algorithms, 25 material systems are repeatedly predicted by at least two algorithm. One of the 25 material systems, Bi/Si achieved the ultra-low thermal conductivity in our previous work. We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications. This study proposed a strategy for material exploration for thermal management by means of machine learning. 

Editorial Summary

Machine learning: Predicting interfacial thermal resistance機器學習:預測界面熱阻

該研究使用機器學習對界面熱阻作了精確的預測,其預測結果的相關系數R高達0.96。來自日本物質材料研究所的華人科學家Yibin Xu領導的團隊,通過進一步考慮基于化學、物理和材料特性的界面條件,精確預測了界面熱阻。他們將描述符分為三種符集:性能描述符、化合物描述符和過程描述符。在80,282種材料體系中界面熱阻預測準確度最高的前100名中,三種模型中至少兩個模型重復預測了25種材料體系的結果。25種材料體系有兩個主要組:Bi /氧化物和AsI3 /碲化物或碘化物。其中,Bi/Si實現了0.16 Wm1K1的超低導熱率。所預測的高界面熱阻材料,被證明是絕熱或熱電應用的潛在候選者。通過限制新材料的搜索空間,如高溫環境的高熔點,界面熱阻預測模型還可擴展到更具體的熱需求。該策略可以加速熱管理的新材料體系開發。

A precise prediction of interfacial thermal resistance (ITR) through machine learning with high correlation coefficient R of 0.96 is achieved. A group led by Yibin Xu from the National Institute for Materials Science (NIMS), Japan, predicted ITR by further considering the interfacial conditions based on chemical, physical, and material properties. They categorized descriptors into three descriptor sets: property descriptors, compound descriptors, and process descriptors. From the top-100 high-ITR prediction among 80,282 kinds of material systems, 25 material systems were repeatedly predicted by at least two of the models. There are two main groups of the 25 material systems Bi/oxides and AsI3/ Tellurides or Iodides. One of the 25 material systems, Bi/Si, accomplished the ultralow thermal conductivity of 0.16Wm1K1. The high-ITR prediction is proved to be the potential candidates for thermal insulating or thermoelectric applications. The ITR predictive model can also be extended for more specific thermal needs by limiting the material searching space, such as high melting point for high temperature environment. Their present strategy can accelerate the material development for thermal management.

Origin of ultrafast growth of monolayer WSe2 via chemical vapor deposition(化學氣相沉積實現單層WSe2超快速生長的機理)
Shuai ChenJunfeng GaoBharathi M. SrinivasanGang ZhangViacheslav SorkinRamanarayan Hariharaputran & Yong-Wei Zhang 
npj Computational Materials 5:28 (2019)
doi:s41524-019-0167-2
Published online:27 February 2019
Abstract| Full Text | PDF OPEN

摘要:最近通過化學氣相沉積在金基板上實現了具有緊湊三角形形態的大面積、高質量WSe2的超快生長。然而,超快速增長的機制仍有待闡釋。本研究首先分析了其生長過程,確定了實現超快速生長的兩種可能途徑:路徑1,快速邊緣附著和超快邊緣擴散;路徑2,快速扭結成核和超快扭結傳播。我們使用動力學Monte Carlo模擬和第一原理計算,來評估這兩條路徑的可行性,結果表明:第一原理計算得出高邊緣擴散勢壘,因此路徑1不可行。值得注意的是,路徑2再現了所有實驗性生長特征(形態、定向和生長速率),相關的能量數據也與第一性原理計算一致。本工作揭示了WSe2超快速生長的潛在機制,并為其他二維材料的超快速生長提供了新的途徑。   

Abstract:The ultrafast growth of large-area, high-quality WSe2 domains with a compact triangular morphology has recently been achieved on a gold substrate via chemical vapor deposition. However, the underlying mechanism responsible for ultrafast growth remains elusive. Here, we first analyze growth processes and identify two possible pathways that might achieve ultrafast growth: Path 1, fast edge attachment and ultrafast edge diffusion; Path 2, fast kink nucleation and ultrafast kink propagation. We perform kinetic Monte Carlo simulations and first-principles calculations to assess the viability of these two paths, finding that Path 1 is not viable due to the high edge diffusion barrier calculated from first-principles calculations. Remarkably, Path 2 reproduces all the experimental growth features (domain morphology, domain orientation, and growth rate), and the associated energetic data are consistent with first-principles calculations. The present work unveils the underlying mechanism for the ultrafast growth of WSe2, and may provide a new route for the ultrafast growth of other two-dimensional materials. 

Editorial Summary

Ultrafast growth of monolayer WSe2: Kink nucleation and propagation單層WSe2的超快生長:扭結成核和扭結傳播

該研究揭示了在Au111)上通過化學氣相沉積得到致密邊緣的規則三角形WSe2(一種過渡金屬二硫化物)單層的超快生長機制。來自新加坡科技局A-STARYong-Wei Zhang 領導的團隊,通過對生長過程作kMC模擬和第一性原理計算分析,提出了引起緊湊三角形疇超快速增長的兩種可能途徑,即路徑1:快速邊緣附著和超快邊緣擴散,以及路徑2:快速扭結成核和沿邊緣的超快扭結傳播?;?/span>DFT計算、與實驗生長速率比較、與實驗結果的形態比較,發現沿著疇邊緣擴散勢壘很高,因而排除了路徑1。而kMC模擬結果表明,路徑2才是超快速生長的潛在機制,因為模擬的疇形態、疇取向和生長速率都與沉積通量和溫度在寬范圍內的實驗結果一致,而且扭結成核顯然是限制因素。該機制有可能擴展到其他2D材料。例如,如果基底能夠極大地增強吸附原子的表面擴散,可能促進它們從蒸氣中直接沉積到生長前沿,從而實現其它過渡金屬二硫化物的超快速生長。

The underlying mechanism for the ultrafast growth of regular triangular WSe2 monolayer with compact edges on Au(111) via CVD is revealed. A team led by Yong-Wei Zhang from the Institute of High Performance Computing, A*STAR, Singapore, performed growth process analysis via kMC simulations and first-principles calculations, from which they suggested two possible paths leading to the ultrafast growth of compact triangular domain, that was, Path 1: fast edge attachment and ultrafast edge diffusion, and Path 2: fast kink nucleation and ultrafast kink propagation along the edge. Based on DFT calculations and comparison with the experimental growth rate and morphology, they ruled out Path 1 due to the high diffusion barriers along the domain edges. Our kMC simulations suggested that Path 2 is the underlying mechanism responsible for the ultrafast growth since the simulated domain morphology, domain orientation, and growth rate are in agreement with the experimental results under wide ranges of deposition flux and temperature,of which kink nucleation is clearly the limiting factor. This mechanism can be potentially extended to other 2D materials. For example, the ultrafast growth of other TMDs may also be achievable if a substrate is able to greatly enhance the surface diffusion of adatoms or promote their direct deposition from vapor to the growth front.

Nanoscale self-healing mechanisms in shape memory ceramics (形狀記憶陶瓷的納米級自愈機制)
Ning Zhang & Mohsen Asle Zaeem 
npj Computational Materials 5:54 (2019)
doi:s41524-019-0194-z
Published online:1 May 2019
Abstract| Full Text | PDF OPEN

摘要:形狀記憶陶瓷,如釔穩定的四方氧化鋯(YSTZ),具有獨特的性能,包括超高的工作溫度和強抗氧化能力。然而,它們在制造和/或機械變形過程中容易形成缺陷。為了充分利用它們的形狀記憶特性,有必要充分了解缺陷在外場刺激下的納米結構演化。本研究采用原子模擬的方法研究了YSTZ納米柱的缺陷演化行。選取\\ left [{01 \ bar 1} \ right] \)和[001]兩個特征方向分別代表相變和位錯遷移的主要變形機制。體積膨脹與四方相到單斜相轉變有關,并可以觀察到促進裂縫和空隙的愈合。原子應力分析揭示了沿新形成的單斜相帶的應力集中。確定了一個臨界裂縫寬度,小于此寬度時的裂縫可以在壓縮過程中完全閉合。對于[001]取向的YSTZ納米柱,位錯遷移可導致非晶態相的形成,這也有助于裂紋和空隙的閉合。揭示的裂縫/空隙愈合機制可能為減輕影響形狀記憶陶瓷的力學性能和變形機制的內部缺陷,提供了一條有效途徑。   

Abstract:Shape memory ceramics, such as yttria-stabilized tetragonal zirconia (YSTZ), offer unique properties including ultra-high operating temperatures and high resistance to oxidation. However, they are susceptible to formation of defects during manufacturing and/or by mechanical deformation. To completely take advantage of their shape memory properties, it is necessary to fully understand the nano-structural evolution of defects under external stimuli. In this study, defect evolution behaviors in YSTZ nanopillars are investigated by atomistic simulations. Two characteristic orientations of \(\left[ {01\bar 1} \right]\) and [001] are selected to represent the dominant deformation mechanisms of phase transformation and dislocation migration, respectively. Volume expansion associated with the tetragonal to monoclinic phase transformation is observed to promote healing of crack and void. Atom stress analyses reveal stress concentrations along the newly formed monoclinic phase bands. A critical crack width is identified, less than which the crack can be fully closed in compression. For [001]-oriented YSTZ nanopillars, dislocation migration leads to formations of an amorphous phase, which also assist the crack and void closure process. The revealed crack/void healing mechanisms may provide a path for mitigating internal defects that influences the mechanical properties and deformation mechanisms of shape memory ceramics. 

Editorial Summary

Shape memory ceramics: Mechanisms for its nanoscale self-healing形狀記憶陶瓷:納米級自愈機制

該研究揭示了YSTZ陶瓷在一定條件下可通過機械加載自愈以消除內部缺陷的機制。來自美國美國科羅拉多州礦業學院的Ning ZhangMohsen Asle Zaeem,使用分子動力學計算揭示,YSTZ納米柱中裂縫和空隙的閉合是材料體積的擴大和相變作用的結果。裂紋尺寸效應研究表明,存在一個臨界裂紋寬度,在此寬度以下的裂紋在壓縮作用下可以完全閉合。當裂縫寬度增大到大于臨界值時,相變引起一定的體積膨脹(~4%),此時僅有部分裂縫可以愈合。原子應力分析表明,負壓應力集中在新的單斜帶附近。值得注意的是,空隙尺寸對力學響應和空隙愈合具有顯著影響,而裂縫尺寸的影響則可忽略不計。位錯成核和位錯遷移導致堆垛層錯和非晶態相的形成,這些非晶態相是裂紋和孔洞閉合的中間態。本研究結果提供了深入研究裂縫和空隙尺寸調控的可能性,以獲得所需力學性能的SM陶瓷,并指導設計適當的結構部件與自愈合能力,以獲得更長的使用壽命。

The mechanisms for YSTZ ceramics, in some appropriate circumstances being self-healed by mechanical loading to eliminate their internal defects is revealed. Ning Zhang and Mohsen Asle Zaeem from the Colorado School of Mines, USA, by using molecular dynamics, showed that the closure of cracks and voids in YSTZ nanopillars was due to the volume extension accompanied by phase transformation. Crack size effect study revealed that a critical crack width exists, below which the crack can be fully closed under compression. When the crack width increases to larger than the critical value only partial crack healing can be achieved due to the limited volume extension (~4%) by phase transformation. Atom stress analyses disclosed that the negative compressive stress concentrates along the new transformed monoclinic band. YSTZ pillars. It is noted that the void size has significant effect on the mechanical response and void healing, while the crack size has negligible effect. Dislocation nucleation and migration lead to the formation of stacking faults and amorphous phase, which mediate crack and void closure. The findings of this work will provide insight into the possibility of tuning the crack and void sizes to elicit desired mechanical properties of SM ceramics, and also to guide the design of the appropriate structural components with self-healing ability in order to obtain longer service life.

Implementation of distortion symmetry for the nudged elastic band method with DiSPy (DiSPy計算微彈性帶法的畸變對稱性)
Jason M. MunroVincent S. LiuVenkatraman Gopalan & Ismaila Dabo 
npj Computational Materials 5:52 (2019)
doi:s41524-019-0188-x
Published online:23 April 2019
Abstract| Full Text | PDF OPEN

摘要:微動彈性帶(nudged elastic band,NEB)方法是計算動力學過程最小能量路徑的常用方法。然而,該方法得到的最終路徑在很大程度上依賴于初始路徑的選取,因此為獲得準確結果通常需要基于不同初始結構開展多個計算。近期研究表明,NEB算法只能保有或提高初始路徑所呈現的畸變對稱性?;謖廡┤鮮犢梢隕杓貧猿菩允視Φ娜哦?,由此系統地降低初始路徑的對稱性,從而能夠探索可能存在的其他低能量路徑。本研究詳細介紹了這一過程背后的群與表示理論(representation theory),并基于該方法發展了一套軟件(DiSPy)。然后將該方法應用于計算LiNbO3的鐵電反轉路徑,展示了其有效性。結果表明,應用該方法可更容易得到之前報道的路徑,同時發現了包含更復雜原子運動的新路徑。   

Abstract:The nudged elastic band (NEB) method is a commonly used approach for the calculation of minimum energy pathways of kinetic processes. However, the final paths obtained rely heavily on the nature of the initially chosen path. This often necessitates running multiple calculations with differing starting points in order to obtain accurate results. Recently, it has been shown that the NEB algorithm can only conserve or raise the distortion symmetry exhibited by an initial pathway. Using this knowledge, symmetry-adapted perturbations can be generated and used as a tool to systematically lower the initial path symmetry, enabling the exploration of other low-energy pathways that may exist. Here, the group and representation theory details behind this process are presented and implemented in a standalone piece of software (DiSPy). The method is then demonstrated by applying it to the calculation of ferroelectric switching pathways in LiNbO3. Previously reported pathways are more easily obtained, with new paths also being found which involve a higher degree of atomic coordination. 

Editorial Summary

Distortion symmetry: finding the minimum energy path more efficiently畸變對稱性:高效尋找最低能量路徑

該研究介紹了畸變對稱性的概念、數學、方法,及其在微彈性帶nudged elastic band,NEB計算中的應用。來自美國賓州州立大學的Jason M. Munro團隊提出了對稱性適應微擾方法,并將該方法應用于NEB計算過程中。尋找最低能量路徑是研究各種動態過程,如催化、生化反應、鐵電疇反轉等化學反應和相變過程的核心。微動彈性帶方法是尋找最低能量路徑的常用方法。然而,該方法得到的最終路徑在很大程度上依賴于初始路徑的選取,通過一次計算通常難以找到對應全局能量最低的路徑。通過對初始路徑中的結構對稱性施加擾動,該方法可以快速、全局地找到能量最低路徑。他們將該方法應用于研究LiNbO3鐵電極化反轉過程,發現該方法不僅更高效地找到文獻中報道的反轉路徑,同時還找到包含更復雜原子運動的新路徑,證明了其有效性。該方法有望成為今后NEB計算中重要的組成部分。

Distortion symmetry with its concepts, mathematics, methods, and its applications in the calculation of nudged elastic band (NEB) is introduced. A team led by Jason M. Munro from the Pennsylvania State University introduces the distortion symmetry in the NEB calculations. Obtaining the minimum energy path is key in the research of kinetic processes, such as catalysis, biochemical reactions and diverse phase transitions. The NEB method is a common approach for this issue. However, the calculated paths depend heavily on the symmetry of the initially chosen paths, and the global energy minimum path can hardly be obtained by one calculation. By impose perturbations on the symmetry of structures in the initial path, this method is capable of obtaining energy minimum path with high efficiency. They demonstrated the validity of this method by applying it for searching the ferroelectric switching pathways in LiNbO3. This approach is expected to be an important integral part of NEB calculations in the future.

Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials (使用機器學習勢研究晶格弛豫對高熵合金相變的影響)
Tatiana KostiuchenkoFritz KormannJorg Neugebauer & Alexander Shapeev 
npj Computational Materials 5:55 (2019)
doi:s41524-019-0195-y
Published online:1 May 2019
Abstract| Full Text | PDF OPEN

摘要:近年來,高熵合金(HEAs)以其優異的材料性能引起了人們的廣泛關注。識別新的HEAs的一個主要挑戰是缺乏有效的方法來探索它們巨大的組成空間。從頭算方法已經成為一種補充實驗的強有力方法。然而,對于多組分合金,面對其化學復雜性,現有的方法充滿問題。在這項工作中,我們提出了一種計算學習的HEAs方法,該方法將從頭算數據的機器學習勢與蒙特卡洛模擬計算相結合。在體心立方(bcc)NbMoTaW HEA為原型,我們驗證了這一方法的高效率和高性能。該方法可用于研究相穩定性、相變和化學短程有序。本研究揭示了局部弛豫效應的重要性:在室溫下,局部弛豫顯著穩定了bcc NbMoTaW的單相形成。最后,我們的研究揭示了一個迄今為止未知的機制,在環境溫度下由原子弛豫而驅動化學有序的機制。   

Abstract:Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space. Ab initio calculations have emerged as a powerful approach that complements experiment. However, for multicomponent alloys existing approaches suffer from the chemical complexity involved. In this work we propose a method for studying HEAs computationally. Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations. The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA. The approach is employed to study phase stability, phase transitions, and chemical short-range order. The importance of including local relaxation effects is revealed: they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature. Finally, a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered. 

Editorial Summary

Lattice relaxations on HEA phase transitions: studied by machine-learning potentials晶格弛豫與高熵合金相變:機器學習勢的研究

該研究提出了一種新的研究高熵合金熱力學性質的計算方法——原子勢法。由來自德國馬普所的Fritz Kormann和俄羅斯Skolkovo創新中心的Alexander Shapeev共同領導的團隊,提出了一種計算方法,采用了一種稱為低秩勢(LRP)的、“針對晶格格點”的機器學習交互模型,該模型能夠包含弛豫效應,并準確地表示多組分體系中的相互作用。相互作用勢通過DFT超胞計算訓練,因此可以系統地考慮局部晶格畸變的影響。通過這種畸變,他們發現固溶體在室溫下是穩定的,并轉變為一種新發現的層狀半有序亞穩態。這一結果突出了局部弛豫對于固溶體穩定的重要作用,與競爭有序構型相比,原子弛豫不受對稱性的約束。原子間勢的集合可以進一步分析預測的不確定性。因此,所提出的方法能夠在整個溫度范圍內以高計算效率精確地對多組分合金(包括HEAs)進行建模,并尋找迄今尚未發現的新的多組分有序態。

A new computational approach, called atomic potentials, for investigation of thermodynamic properties of high-entropy alloys is proposed. A team co-led by Fritz Kormann and Alexander Shapeev from the Max-Planck-Institut für Eisenforschung Gmb H, Germany, and the Skolkovo Innovation Center, Russia, respectively, proposed the computational approach by employing an “on-lattice” machine-learning interaction model called low-rank potential (LRP), capable of including relaxation effects as well as accurately representing interactions in multicomponent systems. The potentials are trained on DFT supercell calculations and thus allow to systematically include the impact of local lattice distortions, by which they found that the solid solution is stable down to room temperature and transforms into a newly found, layered semiordered metastable state. This highlights the important role of local relaxations for the stabilization of the solid-solution where atomic relaxations are not constraint (limited) by symmetry as compared to competing ordered configurations. An ensemble of potentials can further analyze the uncertainty of the predictions. The proposed methodology, thus, makes it possible to accurately model multicomponent alloys (including HEAs) in the entire temperature range with high-computational efficiency and to search for new, hitherto unexplored multicomponent ordered states.

Identifying Pb-free perovskites for solar cells by machine learning (通過機器學習識別太陽能電池的無鉛鈣鈦礦)
Jino ImSeongwon LeeTae-Wook KoHyun Woo KimYunKyong Hyon & Hyunju Chang 
npj Computational Materials 5:37 (2019)
doi:s41524-019-0177-0
Published online:26 March 2019
Abstract| Full Text | PDF OPEN

摘要:近期,隨著計算能力的進步,生成的大型材料數據集通過數據驅動的方法能夠解決材料科學中包括發現新材料在內的諸多問題?;餮笆且恢止芾澩笮褪菁?、預測未知材料屬性、揭示結構-屬性之間關系的主要工具。在目前最先進的機器學習算法中,梯度提升回歸樹(GBRT)提供了高精度的預測,以及基于各個特征權重的可詮釋分析。為了探索用于太陽能電池的無鉛鈣鈦礦,本研究將GBRT算法應用于候選鹵化物雙鈣鈦礦的電子結構數據集,預測形成熱和帶隙。所選特征的統計分析可以為設計和發現新型無鉛鈣鈦礦材料提供指導。   

Abstract:Recent advances in computing power have enabled the generation of large datasets for materials, enabling data-driven approaches to problem-solving in materials science, including materials discovery. Machine learning is a primary tool for manipulating such large datasets, predicting unknown material properties and uncovering relationships between structure and property. Among state-of-the-art machine learning algorithms, gradient-boosted regression trees (GBRT) are known to provide highly accurate predictions, as well as interpretable analysis based on the importance of features. Here, in a search for lead-free perovskites for use in solar cells, we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap. Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites. 

Editorial Summary

Identifying Pb-free perovskites for solar cells by machine learning通過機器學習探尋太陽能電池用無鉛鈣鈦礦

該研究基于機器學習探索無鉛雙鈣鈦礦太陽能電池材料的優化確定方法。來自韓國國立數學科學研究所Yun Kyong Hyon 和韓國化學技術研究所Hyunju Chang領導的團隊,通過梯度增強回歸樹(GBRT)算法和現有的電子結構的數據集,生成了高精度預測模型,用來預測一類無鉛雙鈣鈦礦太陽能電池材料A2B1+B3+X66的形成熱(ΔHF)和帶隙(Eg),并評估材料的每個特征對于這些特性的權重?;諶ㄖ?,他們提取了關鍵特征以確定鹵化物雙鈣鈦礦的ΔHFEg值,從而全面理解特征和特性之間的關系。研究揭示了所提取的特征與化學(ΔHF)、物理(Eg)特性之間的相關性,以及通過機器學習模型探尋最優無鉛鹵化物雙鈣鈦礦太陽能電池材料的實用方法。該文近期發表于npj Computational Materials 5:37(2019).

A machine learning-based investigation to identify Pb-free double perovskite solar cell materials is reported. A team co-led by Yun Kyong Hyon & Hyunju Chang from the National Institute for Mathematical Sciences, and Korea Research Institute of Chemical Technology, respectively, Korea, established highly accurate predictive models for the heat of formation (ΔHF) and bandgap (Eg) of a Pb-free double perovskite, A2B1+B3+X66, for solar cell with importance scores for each feature of materials, by employing the gradient-boosted regression tree (GBRT) algorithm and a dataset of calculated electronic structures for the materials. Based on the scores, they extractted crucial features to determine the values ΔHF and Eg of halide double perovskites, enabling an overall understanding of the relationships between features and properties. This study revealed the relevance of extracted features to the chemical and physical aspects of ΔHF and Eg, and practical approaches of the ML model toward finding optimal candidates of Pb-free halide double perovskites solar cell materials. This paper was recently published in npj Computational Materials 5:37(2019).

Genetic algorithms for computational materials discovery accelerated by machine (機器學習加速的遺傳算法用于新材料發掘)
Paul C. JenningsSteen LysgaardJens Strabo HummelshojTejs Vegge & Thomas Bligaard 
npj Computational Materials 5:46 (2019)
doi:s41524-019-0181-4
Published online:10 April 2019
Abstract| Full Text | PDF OPEN

摘要:機器學習方法對于新材料發現的推動作用越來越顯著,而其依賴于預先存在的數據集。在缺乏數據集的情況下,可以使用遺傳算法實現無偏的數據生成。本研究提出采用訓練的機器學習模型作為快速的能量預測工具,用于分析遺傳算法的收斂性,實現機器學習加速的遺傳算法,將二者的優勢結合起來。為展示該方法加速材料發現的能力,為納米催化劑體系為例,采用其搜索了穩定的、組分變化的、幾何相似的納米合金顆粒。在該算例中,機器學習加速方法相比傳統的暴力遺傳算法,所需的能量計算的數量減少了50倍。這使得基于密度泛函理論計算在給定結構中搜索所有同倫的空間和不同組分的二元合金顆粒變得可行。   

Abstract:Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations. 

Editorial Summary

Machine learning: Accelerating genetic algorithms for materials discovery機器學習:加速遺傳算法用于新材料發掘

遺傳算法作為一種典型的優化算法是新材料發掘的重要工具,其精度依賴于材料的勢能面描述的準確性。而采用精確的密度泛函能量計算的遺傳算法計算量巨大,這限制了其在新材料發掘方面的應用。來自丹麥技術大學和斯坦福大學的分析亚冠阿尔艾因vs阿尔希拉尔團隊采用機器學習模型作為快速的能量預測工具,將機器學習與遺傳算法耦合,在加速搜索方面表現出顯著的優勢。以納米合金催化劑為例,他們采用該方法搜索了穩定的、組分變化的、幾何相似的Pt-Au二元納米合金顆粒。在該算例中,機器學習加速方法相比傳統的暴力遺傳算法,所需的能量計算的數量減少了50倍。該方法使得基于精確密度泛函能量計算的遺傳算法應用于新材料發掘成為可能。

Genetic algorithms (GAs) are metaheuristic optimization algorithms which is promising for material discovery. The accuracy of this method relies on the accuracy of description of the potential energy surface. Though the energy calculation by density functional theory (DFT) is accurate, its high computational cost limited the applications in GA for explorating the large space of materials. A team led by Tejs Vegge from the Technical University of Denmark and Stanford University, used trained machine learning model as inexpensive energy calculator to accelerate GA. Taking the nanoalloy catalysts as an example, the approach is utilized to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, and a 50-fold reduction in the cost of energy calculation is obtained compared to a traditional “brute force” genetic algorithm. This approach makes the DFT-based GA in materials discovery possible.

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