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          《npj 計算材料學》是在線出版、完全開放獲取的國際學術期刊。發表結合計算模擬與設計的材料學一流的研究成果。本刊由中國科學院上海硅酸鹽研究所與英國自然出版集團(Nature Publishing Group,NPG)以伙伴關系合作出版。
          主編為陳龍慶博士,美國賓州大學材料科學與工程系、工程科學與力學系、數學系的杰出教授。共同主編為陳立東研究員,中國科學院上海硅酸鹽研究所研究員高性能陶瓷與超微結構國家重點實驗室主任。
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        Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm        
        Annick De Backer, Sandra Van Aert, Christel Faes, Ece Arslan Irmak, Peter D. Nellist & Lewys Jones   
        npj Computational Materials 8:216 (2022)
        doi.org/10.1038/s41524-022-00900-w
        Published online: 12 October  2022
        Abstract| Full Text | PDF OPEN

        Abstract: We introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses.

        摘要: 本文介紹了一種貝葉斯遺傳算法。這種方法可以使用實驗所得的Z-襯度圖像,一次投影就可以重建單質納米晶粒的原子模型。以環形暗場掃描模式所得的透射電子顯微圖像可以預測某個被投影的原子列所含有的原子數目,并進一步用來搭建初始三維模型。這種有限精度的原子計數結果與近鄰原子列之間的質量關系(neighbor-mass ralations)組成了本算法中貝葉斯定理所需的先驗信息,以便用于后繼的結構能量最小化過程。研究結果表明這種算法在可靠地重建處于動態變化中,對電子束敏感,從而其圖像所用的入射電子劑量相當低的納米晶結構上具有廣闊的應用前景。 

        Editorial Summary

        How to build structures using priori knowledge and experience ? 

        Monotype metal nanocrystals are important catalysts. Their structures as well as the dynamic structure change are closely related to their catalytic performance. Atomic resolution annular dark field scanning transmission electron microscopy (ADF STEM) can be used to characterize the structures of such materials by combining modeling methods. Although some modeling methods based on atom-counting and energy minimization have been developed, it is still a challenge to find the truly experimental structure, not just the global energy minimum from a purely computational calculation. In this study, a new statistical modeling algorithm is proposed. This method makes full use of single projection image, which is benefit to reconstruct surface structures of nanoparticles more reliably. By using the ADF STEM images of Pt nanocrystals from simulation and experiment, respectively, Professor Sandra's team from EMAT and NANOlab Center of Excellence at University of Antwerp in Belgium, together with the team of Professor Lewys from Advanced Microscopy Laboratory of Center for Research on Adaptive Nanostructures and Nanodevices (CRANN), and School of Physics of Trinity College Dublin at The University College Dublin in Ireland, have introduced Bayes' theorem of probability and statistics into the processing and structural reconstruction of the images. The number of atoms in finite precision for a given atomic column can be inferred by the scattering cross-section distribution based on the images. And then the atom-counting values, together with the neighbor-mass relation extracted from the known crystal structure knowledge, are used as prior information required by Bayes' theorem to calculate the probability of the assumed number of atoms. Looping through all atomic columns in this way yields an initial model used for the followed genetic algorithm search, which tries for the best matching model based on energy minimization. The Bayesian genetic algorithm can overcome the problem of obtaining theoretical rather than experimental structures by using genetic algorithms alone. The introduction of neighbor-mass relations can also improve the accuracy of modeling in comparison to only considering atom-counting. The study not only contributes to reliable reconstructions of experimental structures for beam-sensitive monotype nanoparticles, but also can be beneficial to beam-insensitive nanoparticles difficult for reliable structure reconstruction before, because their images are obtained under high electron dose or multiple projections but containing too much noise during experiments and environmental distortions. 

        編輯概述

        如何在結構建模中利用已有的知識與經驗?

        單質金屬納米晶是重要的催化劑。它們的結構及其動態變化與催化性能密切相關。聯合建模的手段,分辨率在原子級別的環形暗場掃描透射電子顯微成像可用于表征這類材料的結構。雖然目前已經發展了基于原子計數與能量最小化的建模方法,但是如何求取實驗結構,而不只是理論計算的全局最穩定結構仍然是一個挑戰。本研究提出了一種新的基于統計學的建模算法。這種方法充分利用實驗得到的單軸投影圖像,有助于真實地重建納米顆粒的表面結構。來自比利時安特衛普大學材料科學電子顯微中心與卓越納米實驗中心的Sandra教授團隊同來自愛爾蘭自適應納米結構與納米機器研究中心先進顯微實驗室和都柏林大學三一學院物理系的Lewys教授團隊合作,分別以Pt納米晶的模擬和實驗數據為例,將概率統計學的貝葉斯定理引入到電子顯微圖像的處理與結構重建中,根據圖像推測散射交叉截面分布,進而獲得給定原子列所含的、仍帶有偏差的原子個數。這些原子個數值與基于晶體結構提取的近鄰質量關系一起作為應用貝葉斯定理所需的先驗知識,用來計算該原子列含有所假定原子數的概率。如此遍歷所有原子列就得到可用于遺傳算法搜索的初始模型,隨后仍然基于能量最小化進一步搜索最佳匹配的模型。這種貝葉斯遺傳算法避免了單獨使用遺傳算法時經常給出理論的而不是實驗結構的問題,而且近鄰質量關系的引入也提高了原先單獨考慮原子計數時建模的準確性。該研究不但有助于電子束敏感單質納米晶粒實驗結構的重建,而且也可以用于對電子束不敏感,比如可以在強電子束和多方向投影下獲得圖像,但是圖像包含過多的實驗與環境噪聲的納米晶粒結構的可靠表征。

        Switchable half-metallicity in A-type antiferromagnetic NiI2 bilayer coupled with ferroelectric In2Se3        
        Yaping Wang, Xinguang Xu, Xian Zhao, Weixiao Ji, Qiang Cao, Shengshi Li & Yanlu Li    
        npj Computational Materials 8:218 (2022)
        doi.org/10.1038/s41524-022-00894-5
        Published online: 23 October  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Electrically controlled half-metallicity in antiferromagnets is of great significance for both fundamental research and practical application. Here, by constructing van der Waals heterostructures composed of two-dimensional (2D) A-type antiferromagnetic NiI2 bilayer (bi-NiI2) and ferroelectric In2Se3 with different thickness, we propose that the half-metallicity is realizable and switchable in the bi-NiI2 proximate to In2Se3 bilayer (bi-In2Se3). The polarization flipping of the bi-In2Se3 successfully drives transition between half-metal and semiconductor for the bi-NiI2. This intriguing phenomenon is attributed to the joint effect of polarization field induced energy band shift and interfacial charge transfer. Besides, the easy magnetization axis of the bi-NiI2 is also dependent on the polarization direction of the bi-In2Se3. The half-metallicity and magnetic anisotropy energy of the bi-NiI2 in heterostructure can be effectively manipulated by strain. These findings provide not only a feasible strategy to achieve and control half-metallicity in 2D antiferromagnets, but also a promising candidate to design advanced nanodevices.

        摘要: 反鐵磁體中的電控半金屬性對基礎研究和實際應用都具有重要意義。本文將二維雙層A型反鐵磁NiI2 材料(bi-NiI2)與不同厚度的鐵電In2Se3 材料進行組合,構建了多種范德瓦爾斯異質結,最終在靠近雙層In2Se3 (bi-In2Se3)的bi-NiI2上實現了可切換的半金屬性。bi-In2Se3的極化翻轉成功地驅使bi-NiI2在半金屬和半導體之間進行轉變,這種有趣的現象是極化場誘導的能帶位移和界面電荷轉移共同作用的結果。此外,bi-NiI2的易磁化軸也依賴于bi-In2Se3的極化方向。應變可以有效地調控異質結中bi-NiI2的半金屬性和磁各向異性能。這些發現不僅為實現和控制二維反鐵磁體的半金屬性提供一種可行的策略,而且為設計先進的納米器件提供了一種極具潛力的選擇。 

        Editorial Summary

        The half-metallicity is achievable for the combination of antiferromagnetism and ferroelectricity 

        Complex oxides exhibit rich physical phenomena such as Mott insulator, multiferroics and high-temperature superconductivity. And searching for topological states have become one of the most active projects in condensed matter physics. Along (111) direction of perovskite oxide, because of the transition metal atom resides on a buckled honeycomb lattice, these systems are predicted to realize the quantum spin Hall. However, it is very difficult to prepare perovskite oxide grown in (111) direction experimentally, so there has been no significant progress.  A team led by Prof. Hanghui Chen from NYU Shanghai and Prof. Gang Li form ShanghaiTech University proposed a stacking method for the construction of (SrMO3)1/(SrM’O3)1 oxide superlattice in the (001) direction by using a variety of different transition metal perovskite oxides. They found strong topological insulators and Dirac semi-metals in the (001) oxide superlattice through first principles calculations and model analysis. The design principle of this study is to achieve non-banal topological properties through the band inversion of the d orbitals of two different transition metal atoms and a particular parity property of (001) superlattice geometry. Through calculation and analysis, it is found that the superlattice represented by (SrTaO3)1/(SrIrO3)1 has Z2 index of (1; 001) are strong topological insulators. The (SrMoO3)1/(SrIrO3)1 superlattice exhibits multiple coexisting topological insulator and topological Dirac semi-metal states. This study provides a novel and feasible direction for finding topological states in complex oxides. 

        編輯概述

        當反鐵磁遇到鐵電:讓半金屬性成為可能

        反鐵磁材料由于具有強抗干擾能力、無雜散場、超快動力學等優點,有望為下一代自旋電子學器件帶來革命性的進步,調控實現反鐵磁材料的半金屬性或產生完全自旋極化的電流是將反鐵磁材料應用于自旋電子學中的關鍵。目前,通過電場調控能夠實現上述目標,但是由于完全自旋極化的傳導電子會隨著電場的撤銷隨之消散,由此得到的半金屬性是易失的,這對存儲和邏輯器件而言并非理想選擇。該研究提出了一種可行的調控方案,通過耦合材料固有的鐵電性調控反鐵磁材料的電子結構,能夠獲得非易失性的半金屬性。來自山東大學晶體材料國家重點實驗室的趙顯教授、李妍璐教授研究團隊和濟南大學自旋電子學研究所的李勝世團隊,設計構建了由二維A型反鐵磁NiI2雙層材料(bi-NiI2)與不同厚度的二維鐵電In2Se3材料組成的范德瓦爾斯異質結,通過第一性原理計算方法預測了與雙層In2Se3耦合的bi-NiI2能夠實現半金屬性的產生與切換。當bi-In2Se3的鐵電極化由向上變為向下時,bi-NiI2會經歷從半金屬到半導體的轉變,且bi-NiI2的易磁軸會由面內轉變為面外,這一現象是極化場誘導的能帶移動與層間電荷轉移共同作用的結果。此外,異質結中bi-NiI2的半金屬性和磁各向異性可以通過應變來進行有效的調控?;谠摦愘|結,作者提出了一種鐵電存儲器件,其數據讀取過程是將鐵電層的極化態轉變為反鐵磁的導電態來進行檢測的。該研究為二維反鐵磁材料中非易失性電控半金屬性提供了一種切實有效的方法,將極大地推動反鐵磁自旋電子學的發展。

        Emergent topological states via digital (001) oxide superlattices         
        Zhiwei Liu, Hongquan Liu, Jiaji Ma, Xiaoxuan Wang, Gang Li & Hanghui Chen     
        npj Computational Materials 8:208 (2022)
        doi.org/10.1038/s41524-022-00894-5
        Published online: 29 September  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Oxide heterostructures exhibit many intriguing properties. Here we provide design principles for inducing multiple topological states in (001) (AMO3)1/(AM’O3)1 oxide superlattices. Aided by first-principles calculations and model analysis, we show that a (SrMO3)1/(SrM’O3)1 superlattice (M = Nb, Ta and M’ = Rh, Ir) is a strong topological insulator with Z2 index (1;001). More remarkably, a (SrMoO3)1/(SrIrO3)1 superlattice exhibits multiple coexisting topological insulator (TI) and topological Dirac semi-metal (TDS) states. The TDS state has a pair of type-II Dirac points near the Fermi level and symmetry-protected Dirac node lines. The surface TDS Dirac cone is sandwiched by two surface TI Dirac cones in the energy-momentum space. The non-trivial topological properties arise from the band inversion between d orbitals of two dissimilar transition metal atoms and a particular parity property of (001) superlattice geometry. Our work demonstrates how to induce non-trivial topological states in (001) perovskite oxide heterostructures by rational design.

        摘要: 氧化物異質結構具有許多有趣的特性。在此,我們提供了在(001) (AMO3)1/(AM’O3)1氧化物超晶格中產生多拓撲態的設計原理。通過第一性原理計算和模型分析,我們得出的結果顯示出(SrMO3)1/(SrM’O3)1超晶格(M = Nb, Ta and M’ = Rh, Ir)是Z2指數為(1;001)的強拓撲絕緣體。更特別的是,(SrMoO3)1/(SrIrO3)1超晶格表現出拓撲絕緣體(TI)和狄拉克拓撲半金屬(TDS)多態共存現象。TDS態在費米能級附近具有一對II型狄拉克點和受對稱性保護的狄拉克“節線”。在能量-動量空間中表面TDS狄拉克錐被兩個表面TI狄拉克錐夾在中間。非平庸的拓撲性質是由兩個不同過渡金屬原子的d軌道能帶反轉以及(001)超晶格幾何構造特別的宇稱性質產生的。我們的工作展示了如何通過合理的設計在(001)鈣鈦礦氧化物異質結構中產生非平庸的拓撲態。 

        Editorial Summary

        Searching for topological states in complex oxides 

        Complex oxides exhibit rich physical phenomena such as Mott insulator, multiferroics and high-temperature superconductivity. And searching for topological states have become one of the most active projects in condensed matter physics. Along (111) direction of perovskite oxide, because of the transition metal atom resides on a buckled honeycomb lattice, these systems are predicted to realize the quantum spin Hall. However, it is very difficult to prepare perovskite oxide grown in (111) direction experimentally, so there has been no significant progress. 
        A team led by Prof. Hanghui Chen from NYU Shanghai and Prof. Gang Li form ShanghaiTech University proposed a stacking method for the construction of (SrMO3)1/(SrM’O3)1 oxide superlattice in the (001) direction by using a variety of different transition metal perovskite oxides. They found strong topological insulators and Dirac semi-metals in the (001) oxide superlattice through first principles calculations and model analysis. The design principle of this study is to achieve non-banal topological properties through the band inversion of the d orbitals of two different transition metal atoms and a particular parity property of (001) superlattice geometry. Through calculation and analysis, it is found that the superlattice represented by (SrTaO3)1/(SrIrO3)1 has Z2 index of (1; 001) are strong topological insulators. The (SrMoO3)1/(SrIrO3)1 superlattice exhibits multiple coexisting topological insulator and topological Dirac semi-metal states. This study provides a novel and feasible direction for finding topological states in complex oxides. 

        編輯概述

        氧化物中的拓撲態:要如何尋覓?

        復雜氧化物表現出非常豐富的物理現象,包括莫特絕緣性、多鐵性和高溫超導。另一方面,尋找非平庸拓撲態已經成為凝聚態物理領域最熱門的課題之一。因過渡金屬原子在(111)方向特有的蜂巢結構,此類鈣鈦礦氧化物體系被預言可實現量子自旋霍爾態。但由于在實驗上制備(111)方向生長的鈣鈦礦氧化物非常困難,因此一直沒有明顯進展。來自上海紐約大學的陳航暉教授團隊和來自上??萍即髮W的李剛教授,基于多種不同過渡金屬鈣鈦礦氧化物,提出了一種在(001)方向堆疊構建(SrMO3)1/(SrM’O3)1氧化物超晶格的方法。他們通過第一性原理計算以及模型分析,在氧化物超晶格(001)晶向上發現了強拓撲絕緣體和狄拉克半金屬。作者的設計原則是,通過兩種不同的過渡金屬原子d軌道的能帶反轉,以及(001)氧化物超晶格幾何構造特別的宇稱性質,來實現非平庸的拓撲性質。經過計算和分析發現,以(SrTaO3)1/(SrIrO3)1為代表的超晶格是具有Z2指數為(1;001)的強拓撲絕緣體。(SrMoO3)1/(SrIrO3)1超晶格更是存在強拓撲絕緣體和拓撲狄拉克半金屬的共存相。作者的研究為在復雜氧化物中尋找拓撲態提供了一種新穎可行的方向。

        Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks         
        Zhenze Yang & Markus J. Buehler     
        npj Computational Materials 8:198 (2022)
        doi.org/10.1038/s41524-022-00879-4
        Published online: 17 September  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Structural defects are abundant in solids, and vital to the macroscopic materials properties. However, a defect-property linkage typically requires significant efforts from experiments or simulations, and often contains limited information due to the breadth of nanoscopic design space. Here we report a graph neural network (GNN)-based approach to achieve direct translation between mesoscale crystalline structures and atom-level properties, emphasizing the effects of structural defects. Our end-to-end method offers great performance and generality in predicting both atomic stress and potential energy of multiple systems with different defects. Furthermore, the approach also precisely captures derivative properties which strictly observe physical laws and reproduces evolution of properties with varying boundary conditions. By incorporating a genetic algorithm, we then design de novo atomic structures with optimum global properties and target local patterns. The method would significantly enhance the efficiency of evaluating atomic behaviors given structural imperfections and accelerates the design process at the meso-level.

        摘要: 結構缺陷在固體中很豐富,對宏觀材料性能至關重要。然而,缺陷-屬性鏈接通常需要大量的實驗或模擬工作,并且由于納米級設計空間的廣度,通常包含有限的信息。在本文紅,我們報告了一種基于圖像神經網絡 (GNN) 的方法來實現介觀尺度晶體結構和原子級特性之間的直接轉換,強調結構缺陷的影響。我們的端到端方法在預測具有不同缺陷的多個系統的原子應力和勢能方面提供了出色的性能和通用性。此外,該方法還精確地捕獲了嚴格遵守物理定律的衍生屬性,并再現了具有不同邊界條件的屬性演變。通過結合遺傳算法,我們隨后重新設計具有最佳全局屬性和目標局部模式的原子結構。該方法將顯著提高在給定結構缺陷的情況下評估原子行為的效率,并加速介觀層次的設計過程。 

        Editorial Summary

        Graph neural network: bridging the gap between atomic structural defects and mesoscale properties

        The structural defects of materials are inevitable, and are crucial to the performance of a variety of materials. Not only the defects themselves, but also their atomic level distribution will affect the local and global properties of the crystal. At present, multi-scale modeling methods from quantum level to continuum level have been developed to calculate the effects of structural defects and reveal the mechanism behind experimental observations. However, due to the heterogeneity introduced by material defects, the design space of defect entities usually contains a large number of possible structures, and simulation may be expensive and time-consuming, especially when the system size increases dramatically. The emergence of machine learning (ML) methods, especially deep learning (DL), is a possible solution. However, at present, these machine learning based models either focus on small crystal structures or only predict single attributes. In order to overcome these difficulties, Professor Markus J. Buehler's team from the Atomic and Molecular Mechanics Laboratory of Massachusetts Institute of Technology introduced a general method to directly convert the crystal structure represented by a graph with spatial information into atomic level attributes, such as atomic stress field or potential energy distribution. The performance of this method is demonstrated by testing models on several large crystal systems, including 2D graphene and 3D aluminum systems with different types of structural defects and target atomic properties. The proposed method achieves high accuracy, and captures the physical information extracted from atomic prediction in all data sets studied as a potential alternative to expensive molecular simulation. The model is further combined with an optimization algorithm to screen designs with low stress concentrations and specific local stress patterns. This method shows the high precision, versatility and diversity of the transformation between structure and attribute on the atomic scale. The ideas presented here can also be applied to other applications in scientific and engineering problems, such as the magnetic field of a spin system, the electronic density in molecules, and the mechanical state of the structure, etc.

        編輯概述

        微觀介觀交流忙,圖像神經網絡來幫忙

        材料的結構缺陷是不可避免的,并且對多種材料的性能至關重要。不僅缺陷本身,而且它們的原子級分布都會影響晶體的局部和全局性質。目前,已經開發了從量子水平到連續體水平的多尺度建模方法,以計算結構缺陷的影響并揭示實驗觀察背后的機制。然而,由于材料缺陷引入的異質性,缺陷實體的設計空間通常包含大量可能的結構,模擬都可能既昂貴又耗時,尤其是在系統規模激增時。機器學習 (ML) 方法的出現,尤其是深度學習 (DL),是一個可能的解決方案,然而,目前這些基于機器學習的模型要么專注于小晶體結構,要么只預測單一屬性。為了克服這些困難,來自美國麻省理工學院原子與分子力學實驗室的 Markus J. Buehler教授團隊引入了一種通用方法,將由具有空間信息的圖形表示的晶體結構直接轉換為原子級屬性,例如原子應力場或勢能分布。通過在多個大型晶體系統上測試模型來展示該方法的性能,包括具有不同類型結構缺陷和目標原子特性的 2D 石墨烯和 3D 鋁系統。所提出的方法實現了高精度,并在研究的所有數據集中捕獲了從原子預測中提取的物理信息,作為昂貴的分子模擬的潛在替代方案。該模型進一步與優化算法相結合,以篩選具有低應力集中和特定局部應力模式的設計。本方法在原子尺度上顯示了結構和屬性之間轉換的高精度、通用性和多樣性。這里提出的想法也可以應用于科學和工程問題中的其他應用,例如自旋系統的磁場、分子中的電子密度以及架構結構的機械狀態等。

        Intrinsic hard magnetism and thermal stability of a ThMn12-type permanent magnet         
        Tumentsereg Ochirkhuyag, Soon Cheol Hong & Dorj Odkhuu     
        npj Computational Materials 8:193 (2022)
        doi.org/10.1038/s41524-022-00821-8
        Published online: 09 September  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Herein, we theoretically demonstrate that simple metal (Ga and Al) substitutional atoms, rather than the conventional transition metal substitutional elements, not only stabilize the ThMn12-type SmFe12 and Sm(Fe,Co)12 phases thermodynamically but also further improve their intrinsic magnetic properties such that they are superior to those of the widely investigated SmFe11Ti and Sm(Fe,Co)11Ti magnets, and even to the state-of-the-art permanent magnet Nd2Fe14B. More specifically, the quaternary Sm(Fe,Co,Al)12 phase has the highest uniaxial magnetocrystalline anisotropy (MCA) of about 8?MJ?m?3, anisotropy field of 18.2?T, and hardness parameter of 2.8 at room temperature and a Curie temperature of 764?K. Simultaneously, the Al and Ga substitutional atoms improve the single-domain size of the Sm(Fe,Co)12 grains by nearly a factor of two. Numerical results of MCA and MCA-driven hard magnetic properties can be described by the strong spin-orbit coupling and orbital angular momentum of the Sm 4f-electron orbitals.

        摘要: 在本文中,我們從理論上證明,簡單的金屬(Ga 和 Al)取代原子,而不是傳統的過渡金屬取代元素,不僅在熱力學上穩定了 ThMn12 型 SmFe12 和 Sm(Fe,Co)12 相,而且進一步提高了它們的固有磁性。性能使其優于廣泛研究的 SmFe11Ti 和 Sm(Fe,Co)11Ti 磁體,甚至優于最先進的永磁體 Nd2Fe14B。具體而言,四元 Sm(Fe,Co,Al)12 相具有最高的單軸磁晶各向異性 (MCA),約為 8?MJ?m-3,各向異性場為 18.2?T,室溫下的硬度參數為 2.8,居里溫度為 764?K。同時,Al 和 Ga 取代原子將 Sm(Fe,Co)12 晶粒的單疇尺寸提高了近兩倍。 MCA 和 MCA 驅動的硬磁特性的數值結果可以通過 Sm 4f 電子軌道的強自旋軌道耦合和軌道角動量來描述。

        Editorial Summary

        Simplicity is beauty: simple metal stronger than transition metal

        ThMn12 alloy is a kind of potential high performance permanent magnet with intrinsic hard magnetic properties, and has a broad application prospect. In particular, the thermodynamically stable large-scale production of SmFe12 single crystal has a large demand for industrial applications (such as motors and generators), but it is very difficult to obtain single crystal phase of ThMn12 structure at present. In order to stabilize the structure of ThMn12, a third alternative metal element, including Ti or V, is essential. However, the doping of these transition metal (TM) elements seriously reduces the intrinsic magnetism. On the other hand, in order to maximize the permanent hard magnetic properties, the grain size of SmFe12 based magnets must be close to the single domain (SD) size (~51-54 nm). However, it is quite difficult to prepare nanometer sized ThMn12 type SmFe12 in actual samples. This problem must be solved to make full use of SmFe12 as a practical high-performance permanent magnet. Professor Dorj Odkhuu from Incheon University in South Korea and the collaborators of Ulsan University proposed a possible solution. Through the first principle density functional theory (DFT), density functional perturbation theory (DFPT) and Monte Carlo (MC) simulation of the system, it was found that simple metal (SM) Al and Ga replacement atoms, compared with traditional TM replacement elements, thermodynamically stabilized the ThMn12 type Sm(Fe, Co)12 structure, At the same time, the grain size of SD was improved and the magnetism was enhanced. The intrinsic hard magnetic properties of the quaternary Sm (Fe, Co, Al)12 and Sm (Fe, Co, Ga)12 compounds proposed in this study at high temperatures are superior to the widely studied SmFe11Ti and Sm (Fe, Co)11Ti compounds. This work still solves the main problems of the structure and thermal instability of ThMn12 from the mechanism, thus providing theoretical guidance for the practical application of SmFe12 based high-performance permanent magnets.

        編輯概述

        主族勝于過渡:既穩定又細微的高性能永磁體

        ThMn12型合金具有固有硬磁特性,是一類潛在的高性能永磁體,應用前景廣闊。目前熱力學穩定的ThMn12型SmFe12 單晶在電機工業方面需求量極大,但樣品難以穩定。為此此人們對其摻雜了包括 Ti 或 V在內的第三種金屬元素,可摻雜如Ti或V這樣的過渡金屬元素又會嚴重降低ThMn12型合金的固有磁性。同時,為最大限度地提高永久硬磁性能,SmFe12基磁體的晶粒尺寸還必須接近納米級的單疇(SD)尺寸(~51-54 nm)。遺憾的是,目前制備納米級ThMn12型SmFe12單晶還相當困難。SmFe12用作高性能永磁體似乎道阻且長。來自韓國仁川大學的Dorj Odkhuu教授等,通過第一性原理密度泛函理論 (DFT)、密度泛函微擾理論 (DFPT) 和蒙特卡羅 (MC) 模擬發現,與傳統的 過渡金屬置換元素相比,簡單的金屬Al 和 Ga 置換原子在熱力學上不僅穩定了 ThMn12 型 Sm(Fe,Co)12 結構,同時還改善了 SD 晶粒尺寸、增強了磁性。作者提出的四元 Sm(Fe,Co,Al)12 和 Sm(Fe,Co,Ga)12 化合物在高溫下的固有硬磁性能優于正被廣泛探索的 SmFe11Ti 和Sm(Fe,Co)11Ti,從機理上解決了 ThMn12的結構和熱不穩定性的主要問題,從而為SmFe12基高性能永磁體的實際應用提供了理論指導。

        Superconductivity and topological aspects of two-dimensional transition-metal monohalides         
        Wen-Han Dong, Yu-Yang Zhang, Yan-Fang Zhang, Jia-Tao Sun, Feng Liu & Shixuan Du    
        npj Computational Materials 8:185 (2022)
        doi.org/10.1038/s41524-022-00871-y
        Published online: 30 August  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Two-dimensional (2D) superconducting states have attracted much recent interest, especially when they coexist with nontrivial band topology which affords a promising approach towards Majorana fermions. Using first-principles calculations, we predict van der Waals monolayered transition-metal monohalides MX (M = Zr, Mo; X = F, Cl) as a class of 2D superconductors with remarkable transition temperature (5.9–12.4 K). Anisotropic Migdal-Eliashberg theory reveals that ZrCl have a single superconducting gap Δ ~ 2.14 meV, while MoCl is a two-gap superconductor with Δ ~ 1.96 and 1.37 meV. The Z2 band topology of 2D MX is further demonstrated that MoF and MoCl are candidates for realizing topological superconductivity. Moreover, the Dirac phonons of ZrCl and MoCl contribute w-shape phononic edge states, which are potential for an edge-enhanced electron-phonon coupling. These findings demonstrate that 2D MX offers an attractive platform for exploring the interplay between superconductivity, nontrivial electronic and phononic topology.

        摘要: 二維超導態最近引起了人們的極大興趣,而二維超導與非平庸拓撲態的共存為研究馬約拉那費米子提供了一種有前景的方法。通過第一性原理計算,我們預測范德華單層的過渡金屬單鹵化物MX(M = Zr, Mo; X = F, Cl)是一類超導轉變溫度為5.9–12.4 K的二維超導體?;诟飨虍愋悦赘襁_爾-埃利亞什伯格理論的研究表明,ZrCl具有單超導能隙Δ ~ 2.14 meV,而MoCl是具有Δ ~ 1.96和1.37 meV的雙能隙超導體。進一步計算表明二維MX家族具有Z2能帶拓撲,且MoF和MoCl是拓撲超導體的候選材料。此外,ZrCl和MoCl的狄拉克聲子貢獻了w型的聲子邊界態,可能導致邊界增強的電子-聲子耦合。這些發現表明二維MX家族為探索超導性、非平庸電子和聲子拓撲之間的相互作用提供了一個有吸引力的平臺。

        Editorial Summary

        Two-dimensional transition-metal monohalides: rich superconducting and topological states

        Due to the development of thin film fabrication technics, 2D superconductors have received continuous attention from the scientific community in recent years. 2D superconducting materials have revealed rich physics, such as Ising pairing, quantum critical effect, and interface-induced high superconducting transition temperature Tc. On one hand, van der Waals materials have great advantages in practical applications due to their weak interaction with the substrate and transferability, but the known intrinsic 2D van der Waals superconductors have limited types and most of them exhibit low Tc. On the other hand, the coexistence of superconductivity and topology conduces to the exploration of topological superconductivity, boundary-enhanced electron phonon coupling, and topological phonon mediated superconductivity, etc. It is fundamentally interesting and important to study 2D van der Waals superconductors with high Tc and topological properties.
        Based on first principles calculations, Professor Shixuan Du's team from the Institute of Physics, Chinese Academy of Sciences and Professor Feng Liu from the University of Utah have coordinated to predict a class of 2D van der Waals materials with rich superconducting and topological properties, i.e., transition-metal monohalides MX (M = Zr, Mo; X = F, Cl). The authors discovered that the strong electron-phonon coupling and Tc (5.9-12.4 K) of MX family are caused by acoustic soft modes, and these soft phonon modes originate from mechanism of either Fermi surface nesting or latent lattice instability. The differences of MX family in Fermi surface compositions lead to the characteristics of either single superconducting gap or two superconducting gaps. With respect to the electronic and phononic topologies, MX family contains both candidates of intrinsic topological superconductors and 2D Dirac phonon contributed w-shaped edge states. In addition, the Janus structure with breaking inversion symmetry is expected to reveal chiral phonon related enhancement of superconductivity. This work provides a new idea for the study of 2D superconductivity, topological states and chiral phonons in a single material platform. 

        編輯概述

        二維過渡金屬單鹵化物:豐富的超導和拓撲態

        由于薄層材料制備技術的發展,二維超導體近年來受到科學界的持續關注,為揭示伊辛配對、量子臨界效應以及界面產生的高溫超導機制等提供了研究平臺。一方面,范德華材料由于層間相互作用弱且易轉移而在實際應用中極具優勢,但目前已知的本征二維范德華超導體種類有限且大多Tc較低。另一方面,超導性和拓撲性的共存有助于探究拓撲超導、邊界顯著增強的電子-聲子耦合以及拓撲聲子介導的超導等方面。研究同時具備較高超導轉變溫度(Tc)和拓撲性質的新型二維范德華超導體在凝聚態物理與材料科學領域都具有重要性。來自中國科學院物理研究所的杜世萱教授團隊與美國猶他大學的劉鋒教授合作,通過第一性原理計算預言了一類具有豐富超導和拓撲性質的二維范德華材料:過渡金屬單鹵化物MX(M = Zr, Mo; X = F, Cl)。研究表明,MX家族由聲學支軟模導致較強的電聲耦合和Tc(5.9-12.4 K),而這些聲子軟模來源于費米面嵌套或潛在晶格不穩定性。MX家族費米面構成的差異使得其呈現出單超導能隙或雙超導能隙特征。此外,MX家族都具有非平庸的電子拓撲不變量Z2 = 1,且MoF 和MoCl為拓撲超導體的候選材料。ZrCl和MoCl在布里淵區邊界存在二維狄拉克聲子,相應的聲子邊界態呈現出特殊的w型色散,表明在一維鋸齒形納米帶中存在潛在的邊界增強電聲耦合。有趣的是,打破空間反演的Janus結構Zr2FCl(M2XY)顯示出類旋子式(roton-like)的聲子軟化和超導增強。該工作為研究單一材料平臺中的二維超導、拓撲態以及手性聲子提供了新思路。

        Excitation and detection of coherent sub-terahertz magnons in ferromagnetic and antiferromagnetic heterostructures         
        Shihao Zhuang and Jia-Mian Hu.     
        npj Computational Materials 8:167 (2022)
        doi.org/10.1038/s41524-022-00851-2
        Published online: 11 August  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Excitation of coherent high-frequency magnons (quanta of spin waves) is critical to the development of high-speed magnonic devices. Here we computationally demonstrate the excitation of coherent sub-terahertz (THz) magnons in ferromagnetic (FM) and antiferromagnetic (AFM) thin films by a photoinduced picosecond acoustic pulse. Analytical calculations are also performed to reveal the magnon excitation mechanism. Through spin pumping and spin-charge conversion, these magnons can inject sub-THz charge current into an adjacent heavy-metal film which in turn emits electromagnetic (EM) waves. Using a dynamical phase-field model that considers the coupled dynamics of acoustic waves, spin waves, and EM waves, we show that the emitted EM wave retains the spectral information of all the sub-THz magnon modes and has a sufficiently large amplitude for near-field detection. These predictions indicate that the excitation and detection of sub-THz magnons can be realized in rationally designed FM or AFM thin-film heterostructures via ultrafast optical-pump THz-emission-probe spectroscopy.

        摘要: 高頻磁振子(自旋波的量子)的激發對于高速磁振子器件的發展至關重要。在此工作中,我們通過計算證明了光致皮秒聲波脈沖對鐵磁和反鐵磁薄膜中亞太赫茲磁振子的激發,并分析了磁振子的激發機制。通過自旋泵浦效應和自旋電荷轉換,這些磁振子可以將亞太赫茲電荷電流注入到相鄰的重金屬薄膜,進而發射電磁波。利用考慮了聲波、自旋波和電磁波互相耦合的動態相場模型,我們證明了所發射的電磁波保留了所有亞太赫茲磁振子模式的頻譜信息,并且其近場強度足以被檢測到。這些預測表明,亞太赫茲磁振子的激發和檢測可以通過超快光泵太赫茲頻譜儀在鐵磁或反鐵磁薄膜異質結構中實現。

        Editorial Summary

        How can we see nanometer spin waves?

        In experiments, ultrafast time-resolved magneto-optical Kerr (TR-MOKE) microscopy is commonly used to probe the time-dependent change of magnetization in ferromagnets. However, because the wavelength of the sub-terahertz (0.1-1×1012 Hz) spin wave is close to the penetration depth of the probe laser pulse, it is difficult to detect by this commonly used method. This work demonstrates that, using spin-charge current conversion in heavy metal films, sub-terahertz spin waves can be detected via electromagnetic waves emitted by the alternating charge currents. Prof. Jiamian Hu and his PhD student Shihao Zhuang from the Department of Materials Science and Engineering, University of Wisconsin, USA, obtained the frequencies of the standing spin waves in ferromagnetic and antiferromagnetic thin films using analytical calculation. Through dynamical phase-field simulations, it is demonstrated that a single picosecond acoustic pulse can excite sub-terahertz spin waves in ferromagnetic and antiferromagnetic thin films. The excited magnetic moments pump spin currents into the adjacent heavy metal thin film. Via the inverse spin Hall effect, the spin currents in the heavy metal films are converted into alternating charge currents which emit electromagnetic waves. The study found that the emitted electromagnetic waves retain the spectral information of all excited sub-terahertz spin waves and are strong enough to be detected. This will provide a basis for the excitation and detection of terahertz spin waves and their applications in high-speed magnon devices. In addition, the computational model used in this study considers the full coupling between acoustic, spin, and electromagnetic waves for the first time, and can be used to accurately model physics of ultrafast magnon-phonon-photon coupling in more complex ferromagnetic or antiferromagnetic thin-film-based heterostructures (such as superlattices) as well as other physical processes and devices that involve phonon-magnon-photon coupling such as cavity magnonics and mechanical antennas.

        編輯概述

        怎樣才能”看見”納米自旋波?

        在實驗中,人們通常利用時間分辨的磁光克爾效應(TR-MOKE)顯微鏡來探測鐵磁體中磁化強度隨時間的變化。然而,因為亞太赫茲(0.1-1×1012 Hz)自旋波的波長與探測激光脈沖的穿透深度相近, 其難以被此常用的方法所探測到。該研究證明了,利用重金屬薄膜中的自旋電流轉換,亞太赫茲自旋波可以通過交變電流所產生的電磁波間接地被探測到。來自美國威斯康星大學材料科學與工程系的胡嘉冕教授及其博士生莊世豪利用理論解析得到了在鐵磁和反鐵磁薄膜中自旋波駐波的頻率。并通過模擬計算,證明了單個皮秒聲波脈沖可以在鐵磁和反鐵磁薄膜中激發出亞太赫茲的自旋波。被激發的磁矩會向相鄰重金屬薄膜泵入自旋流。由于逆自旋霍爾效應,重金屬薄膜中的自旋流會被轉化成交變的電荷電流并發射電磁波。該研究發現所發射的電磁波保留了所有被激發的亞太赫茲自旋波的頻譜信息并且其強度足夠被檢測到,這將為太赫茲自旋波的激發和探測,及其在高速磁振子器件中的應用提供理論依據。另外,該研究所用的計算模型首次考慮了聲波、自旋波和電磁波之間的互相耦合,并且可被用于更復雜的鐵磁或反鐵磁薄膜基異質結構(例如超晶格)以及其他涉及聲子-磁振子-光子的物理過程和設備中(例如諧振腔和天線),以準確模擬超快磁振子-聲子-光子耦合物理過程。

        Superior printed parts using history and augmented machine learning         
        Meng Jiang, Tuhin Mukherjee, Yang Du & Tarasankar DebRoy    
        npj Computational Materials 8:184 (2022)
        doi.org/10.1038/s41524-022-00866-9
        Published online: 23 August  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Machine learning algorithms are a natural fit for printing fully dense superior metallic parts since 3D printing embodies digital technology like no other manufacturing process. Since traditional machine learning needs a large volume of reliable historical data to optimize many printing variables, the algorithm is augmented with human intelligence derived from the rich knowledge base of metallurgy and physics-based models. The augmentation improves the computational efficiency and makes the problem tractable by enabling the algorithm to use a small set of data. We provide a verifiable quantitative index for achieving fully dense superior parts, facilitate material selection, uncover the hierarchy of important variables that affect the density, and present easy-to-use visual process maps. These findings can improve the quality consistency of 3D printed parts that now limit their greater industrial adaptation. The approach used here can be applied to solve other problems of 3D printing and beyond.

        摘要: 因為 3D 打印體現了與其他制造工藝不同的數字技術,所以機器學習算法非常適合打印完全致密的優質金屬零件。傳統的機器學習需要大量可靠的歷史數據來優化很多打印變量,本工作算法被人類智能增強,這些智能源自豐富的冶金知識庫和基于物理模型。該增強通過使算法能夠運用小數據集,提高了計算效率并使問題易于處理。我們提供了一個可驗證的量化指標,來獲得完全致密的優質零件,加速材料選擇,揭示影響密度的重要變量的層次,并提供易于使用的可視化工藝圖。這些發現可以提高 3D 打印零件的質量一致性,打印零件的質量不一致性限制了它們更大的工業適應性。本工作使用的方法可用于解決 3D 打印及其他方面的其他問題。

        Editorial Summary

        Superior printed parts:augmented machine learning with human intelligence

        The quality inconsistency of 3D printed parts limits their greater industrial applications. Machine learning can uncover the correlation between various process variables and lack of fusion, but traditional machine learning needs a large volume of reliable historical data to optimize many printing variables. This work employed the augmented machine learning with human intelligence to tackle a small set of data to achieve superior printed parts by reducing the lack of fusion voids.  A research group led by Meng Jiang from State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, China, Department of Materials Science and Engineering, The Pennsylvania State University, USA, implemented the augmented machine learning strategy and synergistically combined a mechanistic model and historical experimental data to reveal the conditions necessary to reduce the lack of fusion void formation in laser powder bed fusion (PBF-L). They identified five important mechanistic variables according to the rich knowledge base of metallurgy and physics-based models. Based on these variables, decision tree and linear regression predicted the lack of fusion with 93% and 90% accuracy, respectively. In addition, they derived the same hierarchical importance of the mechanistic variables on the lack of fusion by using three feature selection indexes, information gain, information gain ratio, and Gini index. Especially, they provided a verifiable lack of fusion index for achieving fully dense superior parts and presented easy-to-use visual process maps. This strategy can improve the quality consistency of 3D printed parts, facilitate materials selection, support the discovery of new printable alloys, and is equally attractive to solve important problems of other manufacturing processes. 

        編輯概述

        優質打印零件:人類智能增強機器學習

        3D打印零件的質量存在不一致性,這限制其進一步工業應用。機器學習可以揭示工藝參數和未熔合關系,但是傳統機器學習需要大量可靠的歷史數據優化很多打印變量。該研究運用人類智能的增強機器學習處理小數據集,通過減少未熔合空隙獲得優質打印零件。來自中國哈爾濱工業大學先進焊接與連接國家重點實驗室和美國賓夕法尼亞州立大學材料科學與工程系的Jiang等,運用增強機器學習策略,協同結合機理模型和歷史實驗數據,揭示了減少激光粉末床熔合 (PBF-L) 中未熔合空隙形成的必要條件。他們根據冶金知識庫和物理模型的人類智能,確定了5個影響未熔合缺陷的重要機理變量?;谶@些變量,決策樹和線性回歸預測未熔合的準確率分別達到 93% 和 90%。此外,他們運用信息增益、信息增益比和基尼指數三個特征選擇指標,得到相同的未熔合機制變量的層次重要性。尤為重要的是,他們提出一個可驗證的未熔合指數來獲得完全致密的優質零件,并提供易于使用的可視化工藝圖。該研究策略可以提高 3D 打印零件的質量一致性,有助于材料選擇,支持新的可印刷合金的研發,并且對于解決其他制造工藝的重要問題同樣具有吸引力。

        Generative design of stable semiconductor materials using deep learning and density functional theory         
        Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Indika Perera & Jianjun Hu     
        npj Computational Materials 8:164 (2022)
        doi.org/10.1038/s41524-022-00850-3
        Published online: 4 August  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Semiconductor device technology has greatly developed in complexity since discovering the bipolar transistor. In this work, we developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GAN), classifiers, and high-throughput first-principles calculations. We used CubicGAN, a GAN-based algorithm for generating cubic materials and developed a classifier to screen the semiconductors and studied their stability using first principles. We found 12 stable AA′′MH6 semiconductors in the F-43m space group including BaNaRhH6, BaSrZnH6, BaCsAlH6, SrTlIrH6, KNaNiH6, NaYRuH6, CsKSiH6, CaScMnH6, YZnMnH6, NaZrMnH6, AgZrMnH6, and ScZnMnH6. Previous research reported that five AA′′IrH6 semiconductors with the same space group were synthesized. Our research shows that AA′′MnH6 and NaYRuH6 semiconductors have considerably different properties compared to the rest of the AA′′MH6 semiconductors. Based on the accurate hybrid functional calculations, AA′′MH6 semiconductors are found to be wide-bandgap semiconductors. Moreover, BaSrZnH6 and KNaNiH6 are direct-bandgap semiconductors, whereas others exhibit indirect bandgaps.

        摘要: 雙極晶體管問世以來,半導體器件技術的復雜性有了很大的發展。在本工作中,我們開發了一種計算管道,通過結合生成對抗網絡 (GAN)、分類器和高通量第一性原理計算來發現穩定半導體。我們使用一種基于 GAN 的算法的CubicGAN算法生成立方材料,并開發一個分類器來篩選半導體,然后使用第一原理研究其穩定性。我們在 F-43m 空間群中發現了 12 種穩定的 AA′′MH6 半導體,包括 BaNaRhH6、BaSrZnH6、BaCsAlH6、SrTlIrH6、KNaNiH6、NaYRuH6、CsKSiH6、CaScMnH6、YZnMnH6、NaZrMnH6、AgZrMnH6 和 ScZnMnH6。前人研究報道合成了五種同一空間群的 AA′′IrH6 半導體。我們研究表明:AA′′MnH6 和 NaYRuH6 半導體與其他 AA′′MH6 半導體相比具有顯著不同的性質。精確的雜化泛函計算發現 AA′′MH6 半導體是寬帶隙半導體。其中,BaSrZnH6 和 KNaNiH6 是直接帶隙半導體,其余是間接帶隙半導體。

        Editorial Summary

        Stable semiconductor materials: generative design

        Semiconductor materials are essential components of modern devices, such as electronic, photovoltaic and optoelectronic devices. However, semiconductors with various properties are required for different industrial applications. Therefore, computational approaches for exploring semiconductors are essential to enhance future technologies. This work developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GAN), classifiers, and high-throughput first-principles calculations. A team led by Prof. Jianjun Hu from Department of Computer Science and Engineering, University of South Carolina, USA, developed a binary classifier to filter the semiconductors/Insulators (nonmetals) from the dynamically stable quaternary Cubic materials discovered using the CubicGAN model, and then studied the stability using first principles. This work found 12 stable cubic AA′′MH6 semiconductors in the F-43m space group. The DFT calculation results indicate that: 1) Compared with other AA′′MH6 materials, AA′′MnH6 and NaYRuH6 have higher Cii (i = 1, 2, 3) elastic constants, bulk modulus, shear modulus, and Young’s modulus. 2) At temperatures less than 200 K, AA′′MnH6 and NaYRuH6 have lower specific thermal capacity (Cv). 3)The highest Cv at 300 K found in this work is from BaSrZnH6 (127.96 JK?1mol?1). 4) All AA′′MH6 materials are wide-bandgap semiconductors. Among them, BaSrZnH6 and KNaNiH6 are direct-bandgap semiconductors, others exhibit indirect bandgaps. Moreover, the most important elemental and electronic properties were explored. This work will be useful in the development of optical and high-temperature power devices.

        編輯概述

        穩定半導體材料: 生成設計

        半導體材料是電子、光伏和光電子器件等現代設備的重要組成部分。然而,不同工業應用需要具有不同性質的半導體材料。因此,探索半導體的計算方法對于增強未來技術至關重要。本研究提出一種結合生成對抗網絡 (GAN)、分類器和高通量第一性原理計算的計算通道,實現了穩定半導體材料的生成設計。來自美國南卡羅來納大學計算機科學與工程系的Jianjun Hu教授團隊開發了一個二元分類器,從CubicGAN模型生成立方材料過濾半導體/絕緣體(非金屬),然后使用第一原理研究其穩定性。他們的研究在 F-43m 空間群中發現了 12 種穩定的 AA′′MH6 立方半導體材料。DFT計算結果表明:1)相對于其他 AA''MH6 材料的力學性能,AA''MnH6 和 NaYRuH6具有更高的 Cii (i = 1, 2, 3) 彈性常數、體積模量、剪切模量和楊氏模量;2)低于 200 K時, AA''MnH6 和 NaYRuH6 具有更低的比熱容 (Cv) ;3)300 K 時,BaSrZnH6具有最高比熱容 (127.96 JK-1mol-1)。4)所有AA′′MH6 半導體都是寬帶隙半導體。其中,BaSrZnH6 和 KNaNiH6 是直接帶隙半導體,其余是間接帶隙半導體。此外,本工作還研究了最重要的元素和電子性質。該研究有助于開發光學和高溫功率器件。

        Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes         
        Dan Guevarra, Lan Zhou, Matthias H. Richter, Aniketa Shinde, Di Chen, Carla P. Gomes & John M. Gregoire     
        npj Computational Materials 8:57 (2022)
        doi.org/10.1038/s41524-022-00747-1
        Published online: 5 April  2022
        Abstract| Full Text | PDF OPEN

        Abstract: Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO4-like phase with both Cu2V2O7-like and CuV2O6-like phases, creating avenues for understanding complex photoelectrocatalysts.

        摘要: 材料的性能可以通過調節高階組分空間中的組分來實現調控。對于具有復雜晶相行為的空間,將材料性能作為組分和相分布的函數來進行模擬仍然是一個巨大的挑戰。我們提出了材料結構-性能分解 (MSPF) 作為一種自動建模此類數據并識別晶相協同相互作用的方法。 MSPF 是一種可解釋的機器學習算法,它通過深度推理網絡 (DRNet) 將晶相映射與數據集中基于矩陣分解模擬每個晶相的典型性能相結合。 MSPF 證明了用于太陽能燃料發電的 Bi-Cu-V 氧化物光陽極,它包含 25 種不同的晶相組合,并相應地表現出復雜的組分-結構-光活性關系。將測量的光活性與非相互作用相的學習模型進行比較,確定了晶相協同相互作用,以指導進一步的光活性優化和理解。MSPF 確定了類 BiVO4 相與類 Cu2V2O7 和類 CuV2O6 相的協同相互作用,為理解復雜的光電催化劑提供了新的途徑。

        Editorial Summary

        Materials structure–property factorization for identification of synergistic phase interactions

        Enhancing materials research via integration with artificial intelligence (AI) comprises a recent transformation in the evolution of materials science. Such efforts span the research lifecycle from experiment planning to data analysis, with early demonstrations of AI-assisted data processing naturally occurring in image analysis since many machine-learning (ML) algorithms were initially developed to automate pattern recognition in images. To further automate mapping of structure-dependent properties, the community has made a concerted effort in crystal structure phase mapping. While phase mapping is a route to accelerate generation of phase diagrams in high-order composition spaces, most immediately the results are needed to interpret variations in measured materials properties, i.e., the underlying composition–structure–property relationships. Moreover, phase mapping can seed a variety of further investigations. A team led by Prof. Gregoire from California Institute of Technology, USA, presented materials structure–property factorization (MSPF) as an approach to automate modeling of the properties as a function of composition and phase distribution, enabling identification of synergistic phase interactions. They used comprehensive high throughput experimentation to provide the performance data and demonstrated MSPF for the identification of optimal solar fuels photoanodes via measurement of photoelectrochemical (PEC) performance. In the example dataset, the Bi-Cu-V oxide composition library contains 25 unique combinations of phases with an average of about 13 samples per phase field. They introduced MSPF to model the average performance contribution of each phase as well as the interactions among phases, especially when the composition-structure-property relationships are too complex to be readily interpreted by manual analysis. These results demonstrate that the intuitive approach of adding small amounts of copper vanadates to the best known photoanode phase (BiVO4) is far less effective than the strategy discovered in the present work, wherein the relatively low-performance copper vanadate photoanodes are dramatically improved upon the addition of a small amount of BiVO4. MSPF identifies the phase combinations that merit further investigation to reveal the underlying mechanism of performance enhancement. This identification of emergent properties in complex, multi-phase materials is critical to accelerated exploration and understanding of high-performance materials in high-order composition spaces.

        編輯概述

        “庖丁解?!薄?結構-性能關系解析新方法

        通過與人工智能 (AI) 集成來加強材料研究是材料科學發展的最新轉變。這些努力跨越了從實驗計劃到數據分析的研究生命周期。早期的人工智能輔助數據處理首先出現在圖像分析中,因為許多機器學習 (ML) 算法最初開發用來自動化圖像模式識別。為了進一步自動化建立結構-性能之間的映射,這一領域的研究人員在晶體結構相映射方面做出了共同努力。雖然晶相映射可以加速高階組分空間中生成相圖的途徑,但最直接的需求是解釋測量材料特性中的一些變化,即潛在的組分-結構-性能關系。而且,晶相映射的研究可以像種子一樣發展壯大,促進其它各種相關研究。自美國加州理工學院的Gregoire教授等人提出了一種材料結構-性能分解 (MSPF) 的方法,可以根據組分和晶相分布自動對性能進行建模,從而能夠識別協同的晶相相互作用。他們通過全面的高通量實驗來提供晶相識別的性能數據,證明了可以使用MSPF和光電化學 (PEC) 性能來識別最佳太陽能燃料光陽極。在示例數據集中,他們將Bi-Cu-V 氧化物組分庫設置為 25 個獨特的晶相組合,每個相平均約有 13 個樣本,引入 MSPF 來模擬每個晶相的平均性能貢獻以及晶相之間的相互作用,特別是當組分-結構-性能關系過于復雜而無法通過手動分析輕松解釋時。結果表明,在已知最好的光陽極相 (BiVO4) 中添加少量釩酸銅的直觀方法遠不如目前工作中發現的策略有效,也就是在相對低性能的釩酸銅光陽極中添加少量BiVO4。MSPF 確定了值得進一步研究的晶相組合,有利于進一步揭示性能增強的潛在機制。這種對復雜多相材料中新性能的識別,以及加速探索、理解高階成分空間中的高性能材料至關重要。

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