首 頁
滾動信息 更多 >>
npj Computational Materials獲得第一個SCI影響因... (2018-09-07)
英文刊《npj Computational Materials(計算材料學... (2017-05-15)
開放的數據庫 (2015-12-17)
上海硅酸鹽所與Nature出版集團聯合創辦的中國首本n... (2015-11-27)
Nature Publishing Group and Shanghai Institute ... (2015-09-24)
快捷服務
最新文章 研究綜述
過刊瀏覽 作者須知
期刊編輯 審稿須知
相關鏈接
· 在線投稿
會議信息
友情鏈接
  中國科學院上海硅酸鹽研究所
  無機材料學報
  OQMD數據庫
mm.jpg
期刊介紹
  《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。
  辦刊目的與報道范圍
  《npj 計算材料學》是在線出版、完全開放獲取的國際...
【查看詳細】
近期文章 更多 >>

Unavoidable disorder and entropy in multi-component systems (多組分系統中不可避免的無序和熵)
Cormac ToherCorey OsesDavid Hicks & Stefano Curtarolo
npj Computational Materials 5:69(2019)
doi:s41524-019-0206-z
Published online:10 July 2019

Abstract| Full Text | PDF OPEN

摘要:對改進材料功能的更高需求正在推動人們尋找更復雜的多組分材料。盡管材料的組分空間有所增加,但含有四種或四種以上的有序化合物是很罕見的。本研究通過對AFLOW數據庫的統計分析,揭示了焓和熵的增加與化合物數量的增加之間的競爭關系。在熵增益超過焓增益時,就會在物質的數目上找到一個臨界值。一旦超過該值,焓可忽略不計,而完全無序或部分無序將不可避免地出現   

Abstract:The need for improved functionalities is driving the search for more complicated multi-component materials. Despite the factorially increasing composition space, ordered compounds with four or more species are rare. Here, we unveil the competition between the gain in enthalpy and entropy with increasing number of species by statistical analysis of the AFLOW data repositories. A threshold in the number of species is found where entropy gain exceeds enthalpy gain. Beyond that, enthalpy can be neglected, and disorder—complete or partial—is unavoidable. 

Editorial Summary

Multi-component systems: Unavoidable disorder and entropy想煮一鍋有序的八寶粥:可能門都沒有

當晶體的性能無太多顯著特點時,其無序體系可能擁有更加良好的性能,這將為技術的革新帶來希望。由美國杜克大學機械工程與材料科學系的Stefano Curtarolo領導的研究小組,介紹了他們理論探索的成果。他們認為他們需要通過新穎的思路去探尋新材料。(I)用焓優化方法來搜索多組分系統是徒勞的。隨著化合物數量N的增加,發現新材料的概率將會下降,而即使找到有序的材料,它也會被諸多無序結構覆蓋。高熵促進發生在4種混合物態中(或~5 +不同的化學鍵中)。(II)低溫下的遲滯動力學可能是件好事。無序是難以避免的,高溫合成的高熵固溶體可以在低溫相分離中保存下來,同時提供有價值的應用技術。(IIIMaddox的詛咒只是一個通常的情況。這實際上是件好事,因為它需要強制統計分析焓與熵的相互作用。無序體系的建模不能再因為困難和麻煩而被忽視了。復雜多組分材料的研究必將由包含無序的體系來帶動,無序是不可避免和不可忽略的

Disordered systems can lead to remarkable properties—unexpected from the homologous crystalline counterparts—enabling revolutionary technologies. A research group led by Stefano Curtarolo from the Department of Mechanical Engineering and Materials Science, Duke University, USA, introduced the achievement of their theoretical exploration. They believed that they just need to search for materials with a different mindset. (I) The search for multi-component systems, performed with enthalpy optimization, is futile. With increasing N (the number of species), the discovery probability decreases, and, even if an ordered material is found, it would be overwhelmed by engulfing disorder. High-entropy promotion occurs at ~4 mixing species (~5+ with different bond types). (II) Sluggish kinetics at low temperatures can be a blessing. Disorder is hard to cure, and high entropy solid-solutions synthesized at high-temperature can survive low-temperature phase separation while providing valuable technological applications. (III) Maddox’s scandal was only an apparent curse. It was actually a blessing as it forced statistical analysis of the enthalpy versus entropy interplay.  Modeling can no longer ignore disorder—often neglected due to compelling difficulties. Advances in complex multi-component materials research will be driven by embracing disorder. It is unavoidable.

The study of magnetic topological semimetals by first principles calculations (磁性拓撲半金屬的第一性原理計算研究)
Jinyu ZouZhuoran He & Gang Xu
npj Computational Materials 5:96(2019)
doi:s41524-019-0237-5
Published online:04 October 2019

Abstract| Full Text | PDF OPEN

摘要:磁性拓撲半金屬是一種時間反演破缺,在費米面附近具有孤立簡并點或簡并線的拓撲量子材料。其典型的拓撲性質是體-邊界對應,即在表面具有費米弧或鼓膜態。根據簡并度及其在動量空間的分布特點,拓撲半金屬一般分為外爾半金屬,狄拉克半金屬,節點線半金屬,三重簡并點半金屬等。在本篇綜述中,我們從第一性原理計算的角度介紹了近年來磁性拓撲半金屬的進展。首先回顧了早期預言的磁性外爾半金屬如燒綠石銥氧化物和HgCr2Se4,以及最近提出的Heusler化合物,Kagome層狀材料,蜂巢格子等。其次討論了磁性狄拉克半金屬的最新進展,特別是受第三類磁空間群保護的CuMnAs和受第四類磁空間群保護的EuCd2As2。之后介紹了包含自旋軌道耦合效應并穩定的磁性節點線半金屬Fe3GeTe2LaClLaBr)。最后,我們展望了磁性拓撲半金屬未來的發展和研究方向   

Abstract:Magnetic topological semimetals (TSMs) are topological quantum materials with broken time-reversal symmetry (TRS) and isolated nodal points or lines near the Fermi level. Their topological properties would typically reveal from the bulk-edge correspondence principle as nontrivial surface states such as Fermi arcs or drumhead states, etc. Depending on the degeneracies and distribution of the nodes in the crystal momentum space, TSMs are usually classified into Weyl semimetals (WSMs), Dirac semimetals (DSMs), nodal-line semimetals (NLSMs), triple-point semimetals (TPSMs), etc. In this review article, we present the recent advances of magnetic TSMs from a computational perspective. We first review the early predicted magnetic WSMs such as pyrochlore iridates and HgCr2Se4, as well as the recently proposed Heusler, Kagome layers, and honeycomb lattice WSMs. Then we discuss the recent developments of magnetic DSMs, especially CuMnAs in Type-III and EuCd2As2 in Type-IV magnetic space groups (MSGs). Then we introduce some magnetic NLSMs that are robust against spin–orbit coupling (SOC), namely Fe3GeTe2 and LaCl (LaBr). Finally, we discuss the prospects of magnetic TSMs and the interesting directions for future research. 

Editorial Summary

Magnetic Topological Semimetal Materials磁性拓撲半金屬材料

本篇綜述介紹了近年來在磁性拓撲半金屬材料預測方面的進展。來自華中科技大學的徐剛研究組從磁性空間群與第一性原理計算的角度,縱覽了磁性拓撲半金屬包括外爾半金屬、狄拉克半金屬和節點線半金屬材料預測和設計的最新進展。他們首先回顧了早期預言的磁性外爾半金屬如燒綠石銥氧化物和HgCr2Se4,以及最近提出的Heusler化合物,Kagome層狀材料,蜂巢格子等;其次討論了受第三類磁空間群保護的CuMnAs和受第四類磁空間群保護的EuCd2As2兩種磁性狄拉克半金屬;然后介紹了在自旋軌道耦合作用下依然穩定的磁性節點線半金屬Fe3GeTe2LaClLaBr)。最后展望了磁性拓撲半金屬未來的發展和研究方向

This article reviewed the recently prospected magnetic topological semimetal (MTSM) materials. A team led by Gang Xu from Huazhong University of Science and Technology, presented the recent advances of MTSMs including Weyl semimetals (WSMs), Dirac semimetals (DSMs) and nodal-line semimetals (NLSMs), from the perspective of magnetic space group and first principles calculations. They firstly reviewed the early predicted magnetic WSMs such as pyrochlore iridates and HgCr2Se4, as well as the recently proposed Heusler, Kagome layers, and honeycomb lattice WSMs. Then they discussed the recent developments of magnetic DSMs, especially CuMnAs in Type-III and EuCd2As2 in Type-IV magnetic space groups (MSGs). Then they introduced some magnetic NLSMs that are robust against spin–orbit coupling (SOC), namely Fe3GeTe2 and LaCl (LaBr). Finally, they also discussed the prospects of magnetic TSMs and the interesting directions for future research.

Strong phonon localization in PbTe with dislocations and large deviation to Matthiessen’s rule (含位錯結構的PbTe材料中聲子的強局域化振動和馬西森定律的大偏差)
Yandong Sun, Yanguang Zhou, Jian Han, Wei Liu, Cewen Nan, Yuanhua Lin, Ming Hu and Ben Xu
npj Computational Materials 5:97(2019)
doi:s41524-019-0232-x
Published online:04 October 2019

Abstract| Full Text | PDF OPEN

摘要:位錯可以顯著降低材料的熱導率進而提高熱電材料的品質因子。但是不同能量的聲子通過單個位錯結構時的行為變化卻不為人知。本文中我們使用非平衡分子動力學方法,通過消除邊界散射以及位錯直接相互作用的影響,研究了PbTe中位錯和聲子的散射。我們獲得了頻率分布熱流,聲子本征振動模式以及頻率分布聲子平均自由程。具有1015m-2位錯密度的PbTe材料的熱導率下降了62%。含位錯PbTe中聲子的強局域化振動被證實。并且,通過對比我們計算得到的聲子平均自由程和傳統理論模型的結果,我們發現傳統的理論模型不能夠描述全頻率空間聲子的散射行為,與熟知的馬西森規則有很大的偏差。我們的結果為發展基于PbTe的熱電材料提供了重要的指導,并且為通過位錯來提高熱電材料性能提供了新思路   

Abstract:Dislocations can greatly enhance the figure of merit of thermoelectric materials by prominently reducing thermal conductivity. However, the evolution of phonon modes with different energies when they propagate through a single dislocation is unknown. Here we perform non-equilibrium molecular dynamics simulation to study phonon transport in PbTe crystal with dislocations by
excluding boundary scattering and strain coupling effect. The frequency-dependent heat flux, phonon mode analysis, and frequency-dependent phonon mean free paths (MFPs) are presented. The thermal conductivity of PbTe with dislocation density on the order of 1015m-2 is decreased by 62%. We provide solid evidence of strong localization of phonon modes in dislocation sample. Moreover, by comparing the frequency-dependent phonon MFPs between atomistic modeling and traditional theory, it is found that the conventional theories are inadequate to describe the phonon behavior throughout the full phonon spectrum, and large deviation to the well-known semi-classical Matthiessen’s rule is observed. These results provide insightful guidance for the development of PbTe based thermoelectrics and shed light on new routes for enhancing the performance of existing thermoelectrics by incorporating dislocations
.
 

Editorial Summary

Molecular Dynamic Simulation : Preliminary Exploration of the Phonon Dislocation Scattering分子動力學模擬:位錯聲子散射初探

本文研究了明星熱電材料PbTe中位錯對于聲子的散射,發現位錯通過使聲子振動局域化加強來散射聲子,可以極大降低材料的熱導率。來自清華大學材料學院的徐賁老師、美國南卡羅萊納大學胡明老師領導的團隊老師,使用非平衡分子動力學方法研究了PbTe中單個位錯和聲子的散射過程,計算結果表明PbTe4 × 1015 m-2密度的位錯導致其熱導率下降了62%;通過截面頻譜熱流分析,獲得了PbTe中被位錯散射聲子的頻率;通過擬合得到PbTe聲子的平均自由程并且和傳統理論結果對比,發現了理論結果的不足之處。創新之處在于,他們的研究消除了邊界散射和位錯之間互相作用兩個因素的干擾,獲得位錯對于聲子的真實散射強度。研究的結果對于通過引入位錯調控熱電材料熱導率具有重要的指導意義。研究由清華大學博士生孫彥東與加州大學洛杉磯分校周彥光共同展開

The phonon-dislocation scattering in PbTe which is the most famous thermoelectric materials has been studied. The dislocations can greatly reduce the thermal conductivity by scattering the phonons to be more localized. A team led by Prof. Ben Xu from Tsinghua University studies the phonon dislocation scattering process in PbTe using non-equilibrium molecular dynamics simulation. They show that a 4 × 1015 m-2 denstiy dislocation can lead to the thermal conductivity decrease for 62%. The frequencies of the phonons most likely been scattered by the dislocation are obtained. The phonon mean free paths (MFPs) are presented and been compared with the traditional theory. It is found that the conventional theories are inadequate to describe the phonon behavior throughout the full phonon spectrum. The innovation in their study is that they eliminate the phonon scattering from boundary and the strain coupling, and obtain a real phonon-dislocation scattering rate. These results provide insightful guidance for the development of PbTe based thermoelectrics and shed light on new routes for enhancing the performance of existing thermoelectrics by incorporating dislocations.

Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking (智能機器通過維堆疊學習/發現有意義的物理和化學貢獻)
Lee A. GriffinIaroslav GaponenkoShujun Zhang & Nazanin Bassiri-Gharb
npj Computational Materials 5:89(2019)
doi:s41524-019-0222-z
Published online:12 August 2019

Abstract| Full Text | PDF OPEN

摘要:盡管功能材料表征技術的顯著發展帶來了不斷增加的海量數據,但對于材料的物理、化學屬性與其性能間的關聯仍然知之甚少。以大量材料數據為基礎的降維技術已被用來揭示難以探尋的材料行為機制。本研究提出了一種方法,可通過維度疊加將物理和化學約束引入到用于降維分析中。與傳統的、非關聯的技術相比,該方法以物理或化學相關性為出發點,沿特定維數方向堆疊數據,由此可對所有反映材料行為的測量參數進行直接和同步的評估。將該方法應用于(1-xPMN-xPT固溶體中的納米級機電馳豫響應,并直接比較電場依賴和化學組成依賴部分的貢獻。結果識別出了具有疇玻璃態的類極化行為和類馳豫行為,并跟蹤了這些行為在相圖中的持續演變。本研究提出的維度疊加技術,依據相關系統的基礎物理理論,可對任何多維數據集作有效分析,為其在多學科中的應用開辟了一系列可能性   

Abstract:Despite remarkable advances in characterization techniques of functional materials yielding an ever growing amount of data, the interplay between the physical and chemical phenomena underpinning materials’ functionalities is still often poorly understood. Dimensional reduction techniques have been used to tackle the challenge of understanding materials’ behavior, leveraging the very large amount of data available. Here, we present a method for applying physical and chemical constraints to dimensional reduction analysis, through dimensional stacking. Compared to traditional, uncorrelated techniques, this approach enables a direct and simultaneous assessment of behaviors across all measurement parameters, through stacking of data along specific dimensions as required by physical or chemical correlations. The proposed method is applied to the nanoscale electromechanical relaxation response in (1-x)PMN-xPT solid solutions, enabling a direct comparison of electric field- and chemical composition-dependent contributors. A poling-like, and a relaxation-like behavior with a domain glass state are identified, and their evolution is tracked across the phase diagram. The proposed dimensional stacking technique, guided by the knowledge of the underlying physics of correlated systems, is valid for the analysis of any multidimensional dataset, opening a spectrum of possibilities for multidisciplinary use. 

Editorial Summary

Smart machine learning: through dimensional stacking智能機器學習:維度堆疊方法

該研究提出了一種將物理和化學約束引入到機器學習中的方法。來自美國佐治亞理工學院材料科學與工程學院的Nazanin Bassiri-Gharb領導的團隊,巧妙地使用維度堆疊方法,從多維數據集中提取出了有意義的化學和物理特征。在傳統的數據降維前采用維度堆疊將物理和化學約束引入分析中,可以提供更為準確的材料屬性參數與性能間的關系。為證明該方法的有效性,將其應用到PMN-PT弛豫鐵電體機電響應的納米尺度研究中。結果表明,相比簡單的統計分析和傳統降維方法,僅有該方法可以在全相圖空間中準確地定量描述不同物理屬性參數對于機電響應的貢獻。本研究提出的維度疊加方法,可廣泛用于復雜組分的功能材料的響應分析,以及新功能材料的設計

A method for applying physical and chemical constraints to machine learning techniques is presented. A team led by Nazanin Bassiri-Gharb from the School of Materials Science and Engineering, Georgia Institute of Technology, USA, extracted meaningful chemical and physical characteristics from multidimensional datasets through informed use of dimensional stacking. Stacking the slices along appropriate dimensions before dimension reduction is applied could implicitly impose physical and chemical constraints, thus offers a quantitative and lossless comparison of behaviors across measurement parameters. To demonstrate the versatility of this approach, the authors applied it to nanoscale investigations of electromechanical relaxation of (1-x)PMN-xPT relaxor-ferroelectrics across the phase diagram along with simple statistical analysis and conventional dimension reduction to identify the multiple contributors to the observed electromechanical response at different ferroelectric end member contents. It shows that only the dimensional stacking technique enabled a direct and quantitative comparison of the evolution of the different contributors across the phase diagram. The dimensional stacking technique should prove of particular interest for the analysis of the functional response of all materials of complex compositions, as well as design of new functional materials.

Quantum topology identification with deep neural networks and quantum walks (使用深度神經網絡和量子行走識別量子拓撲)
Yurui MingChin-Teng LinStephen D. Bartlett & Wei-Wei Zhang
npj Computational Materials 5:88(2019)
doi:s41524-019-0224-x
Published online:27 August 2019

Abstract| Full Text | PDF OPEN

摘要:拓撲有序材料可以作為新的量子技術的平臺,例如容錯量子計算機。要實現這一期望,需要有效和通用的方法來發現和分類材料中新的拓撲相。本研究證實,用外存儲器增強的深度神經網絡可以利用量子行走中形成的密度分布來有效地識別拓撲相性質及其相變過程。在所測試的拓撲有序模型上,用該方法的拓撲相識別精度達到97.4%,對數據的噪聲具有較強的魯棒性。此外,我們還證明了我們訓練的DNN能夠識別擾動模型的拓撲相,并預測相應拓撲相變的偏移,而不需要事先學習任何關于擾動的信息。這些結果表明,我們提出的方法具有普遍適用性,并可用于識別各種量子拓撲材料   

Abstract:Topologically ordered materials may serve as a platform for new quantum technologies, such as fault-tolerant quantum computers. To fulfil this promise, efficient and general methods are needed to discover and classify new topological phases of matter. We demonstrate that deep neural networks augmented with external memory can use the density profiles formed in quantum walks to efficiently identify properties of a topological phase as well as phase transitions. On a trial topological ordered model, our method’s accuracy of topological phase identification reaches 97.4%, and is shown to be robust to noise on the data. Furthermore, we demonstrate that our trained DNN is able to identify topological phases of a perturbed model, and predict the corresponding shift of topological phase transitions without learning any information about the perturbations in advance. These results demonstrate that our approach is generally applicable and may be used to identify a variety of quantum topological materials. 

Editorial Summary

Quantum topology: deep neural networks and quantum walks量子拓撲的識別:深度神經網絡聯合量子行走

本研究報道了一種利用量子行走識別量子材料拓撲相的通用自動化方法,用于探測相和深度神經網絡(DNN)并分析其演化過程。來自澳大利亞悉尼大學工程量子系統中心的Wei-Wei Zhang教授領導的團隊,利用粒子在系統哈密頓量驅動下演化過程中形成的粒子密度分布,證明了具有外部存儲功能的新型DNN能夠識別具有自旋軌道耦合的二維晶格模型中的拓撲相和相變。該方法識別精度高達97.4%,對輸入數據具有較強的魯棒性。雖然該模型僅使用特定二維自旋軌道晶格哈密頓量的數據進行訓練,但它能夠對擾動模型的相位進行高精度地分類,而不需要任何關于擾動的細節。因此,與DNN結合的量子行走是一種強大而通用的工具,可有效地發現和分析新的拓撲量子系統,從而設計出可靠的量子技術

A universal automated method for identifying topological phases of quantum materials through quantum walks to probe the phase and deep neural network (DNN) and to analyse the evolution is reported. A team led by Prof. Wei-Wei Zhang from the Centre for Engineered Quantum Systems, School of Physics, University of Sydney, Australia, demonstrated that the novel DNN with external memory is able to identify the topological phases and phase transitions for a two-dimensional lattice model with spin–orbit coupling, using particle density profiles which formed during a particle’s evolution driven by the system’s Hamiltonian. Their method demonstrates high identification accuracy of 97.4%, and robust to noise on the input data. Finally, although the model was trained using data only from a specific two-dimensional spin–orbit lattice Hamiltonian, it is able to classify the phases of a perturbed model with high accuracy, without any details about the perturbation. As such, quantum walks combined with DNN are a powerful and generic tool for the efficient discovery and analysis of novel topological quantum systems, and therefore the design of robust quantum technologies.

Predicting superhard materials via a machine learning informed evolutionary structure search (通過機器學習輔助的進化結構搜索來預測超硬材料)
Patrick AveryXiaoyu WangCorey OsesEric GossettDavide M. ProserpioCormac ToherStefano Curtarolo & Eva Zurek
npj Computational Materials 5:89(2019)
doi:s41524-019-0226-8
Published online:02 September 2019

Abstract| Full Text | PDF OPEN

摘要:超硬材料的計算預測將可實現各種化合物的計算機設計,并可用于各式各樣的技術應用。本研究發現,在各種材料的實驗維氏硬度(Hv)和三種宏觀硬度模型計算的硬度之間有著良好的一致性,剪切和/或體模量是這三種宏觀硬度模型的主要參數,通過以下兩種方法獲得:iAFLOW-AELAFLOW自動彈性庫)的第一原理計算模型,ii)以AFLOW數據庫中的材料數據為樣本,訓練的機器學習(ML)模型。由于可以快速估算HvML值,它們可以與進化搜索結合使用來預測穩定的超硬材料。該方法是在XTALOPT進化算法中實現的。每個晶體都被最小化到最接近的局部最小值,它的維氏硬度是由Teter剪切模量的線性關系來計算的。能量/焓和HvMLTeter都被用來確定結構的擬合度。將該方法應用于碳體系,發現了43個新的超硬相。拓撲分析表明,預測出的結構相比金剛石的強度還略大,該相中含有大量金剛石和/或六方碳結構   

Abstract:The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, good agreement was found between experimental Vickers hardnesses, Hv, of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materials within the AFLOW repository. Because HvML values can be quickly estimated, they can be used in conjunction with an evolutionary search to predict stable, superhard materials. This methodology is implemented in the XTALOPT evolutionary algorithm. Each crystal is minimized to the nearest local minimum, and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter. Both the energy/enthalpy and HvMLTeter are employed to determine a structure’s fitness. This implementation is applied towards the carbon system, and 43 new superhard phases are found. A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite. 

Editorial Summary

Predicting superhard materials: A easy way for hard work超硬材料預測:肯硬骨頭的簡便方法

利用基于數據庫和數據傳輸的宏觀硬度模型與機器學習結合,可以預測多種晶體材料的維克斯硬度(Hv)。來自紐約州立大學布法羅分校化學系的Eva Zurek教授領導的團隊,通過AFLOWRESTful接口獲得了Hv與剪切彈性模量之間的線性關系,并計算維氏硬度Hv。其預測結果與第一原理計算的結果非常一致,與實驗結果也吻合得很好。這些技術使人們可基于機器學習的彈性性質快速計算給定晶體結構的合理硬度值,并可利用這些預測的硬度值來計算每個超硬相的擬合度。該技術是在進化算法(EA)中實現的,并隨后應用于碳體系,以尋找穩定和超硬相。在他們的搜索中發現了79種動力學穩定、低能量、具有Hv > 40 GPa的不同拓撲結構,其中43種拓撲結構之前未曾報道

The Vickers hardnesses, Hv, of a wide variety of crystalline materials now can be predicted by using a macroscopic hardness model in conjunction with machine learning (ML) based on data base and data transmission. A team led by Prof. Eva Zurek from department of chemistry, State University of New York at Buffalo, USA, computed the Vickers hardness via a linear relationship between Hv and shear modulus through the RESTful API available on AFLOW (Automatic FLOW). They found the predictions are in excellent agreement with results obtained from first-principles calculations and both are in good agreement with experiment. These developments make it possible to quickly calculate reasonable hardness values for a given crystal structure using ML-based elastic properties, and these hardness estimates can subsequently be employed to calculate an individual’s fitness in a CSP algorithm designed for the prediction of superhard phases. This technique is implemented within an evolutionary algorithm (EA), and is subsequently applied towards the carbon system to search for stable and superhard phases. Seventy-nine dynamically stable, low energy, distinct topologies with Hv>40GPa are found in their searches, of which forty-three topologies have not been reported previously.

Unconventional topological phase transition in non-symmorphic material KHgX (X=As, Sb, Bi)(非對稱材料中的非常規拓撲相變KHgXX = AsSbBi)
Sebastian E. AmentHelge S. SteinDan GuevarraLan ZhouJoel A. HaberDavid A. BoydMitsutaro UmeharaJohn M. Gregoire & Carla P. Gomes
npj Computational Materials 5:77(2019)
doi:s41524-019-0213-0
Published online:19 July 2019

Abstract| Full Text | PDF OPEN

摘要:數據采集的自動化使采集的速度已經超越了人類處理數據的能力。人工智能為自動化的數據解釋提供了新的可能性,可生成大型、高質量的數據集。降低背景信號是長期存在的挑戰,特別是多個背景信號源同時存在情形。從測量到的信號中自動提取出感興趣的信號將大大加快數據解釋。本研究提出了一種無監督的概率學習方法,該方法能分析大數據集從而識別多個背景源,確定任何給定數據點所包含的感興趣信號的概率。基于X射線衍射和拉曼光譜數據,我們證明了該方法的有效性。其可適用于任何類型的數據,其中感興趣的信號是背景信號的正加法即可。雖然模型可以包含先驗的知識,但它并不需要信號的知識,因為背景信號的形狀、噪聲水平和感興趣的信號可以通過概率矩陣分解框架同時學習得到。通過無監督概率學習自動識別信號,避免了人類偏差的注入,加速了大型數據集中的信號提取,可廣泛適用于物理科學及其他領域中   

Abstract:Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond. 

Editorial Summary

Machine learning: multi-component background signal detection無監督學習:濫竽充數者無處遁形

該研究針對測量譜建立了背景信號的識別、去除和統計評估的原理性方法,其中每個測量信號都是多個背景信號源的非負貢獻的組合。來自美國加州理工學院和康奈爾大學的John M. GregoireCarla P. Gomes共同領導的團隊,提出一種基于無監督概率學習方法來識別并去除測量譜中的背景,該過程可以完全自動實現而無需輸入任何預先知識。上述名為多成分背景學習的方法基于一種全新的概率產生模型,通過大數據學習可以同時獲得背景信號和有用信號的信息,由此對測量譜中每一個數據點是否包含有用信號的概率給出判斷。以XRD和拉曼譜這兩種最為常見的材料表征方法為例,借助已有的數據集分析證明了上述方法的可用性。該方法可以被推廣用于更為廣泛的測量譜數據的自動識別

A principled approach to the identification, removal and statistical evaluation of background signals is established for any measurement type where each measured signal is a combination of non-negative contributions from multiple sources. A team co-led by John M. Gregoire and Carla P. Gomes from the California Institute of Technology and the Cornell University, respectively, USA, proposed an unsupervised probabilistic learning approach to identify and remove multiple background sources from large data collections automatically without any prior knowledge. The approach named Multi-Component Background Learning leverages the power of big data by inferring background and signals of interest from an entire dataset of spectrograms. This approach relies on a novel probabilistic generative model of the spectroscopic data where the background and the level of spectroscopic activity are simultaneously learned from the data, which leads to the probability that any given data point contains a signal of interest. The validity of this approach was justified by using large data sets from XRD and Raman spectroscopy, the two common techniques in materials characterizations, and can be extended in other type of measurements for the automation of data interpretation.

High-performance bifunctional polarization switch chiral metamaterials by inverse design method (高性能雙功能手性超材料的逆向設計)
Chuanbao Liu, Yang Bai, Ji Zhou, Qian Zhao, Yihao Yang, Hongsheng Chen, and Lijie Qiao

摘要:多功能極化控制在現代光子學中發揮著至關重要的作用,然而設計兼具寬頻帶、高透射率等優異特性的多功能極化控制器至今仍是一個巨大挑戰。本研究采用基于模型理論范式的逆向設計方法,設計出具有多層級聯結構的手性超材料,可對正反向傳播的電磁波產生不同的極化控制功能,并通過計算仿真與實驗測試驗證了該理論設計具有寬頻、高透射的優異特性。針對不同的極化控制功能,利用散射矩陣理論推演得到所需的超材料結構組成及其序構。首先,設計了一種雙功能手性超材料,對于正向傳播的線極化電磁波表現為四分之一波片功能,實現線極化到圓極化電磁波的轉化;對于反向傳播的線極化電磁波表現為45°極化旋轉器功能。得益于類法布里-泊羅(Fabry–Perot-like)干涉效應,該雙功能手性超材料表現出寬頻、高透射優異特性。其次,基于類似的設計方法,構建了異常四分之一波片,可將正向傳輸的xy極化電磁波或反向傳輸的yx極化電磁波,在寬頻范圍內高效地轉化為左旋和右旋圓極化電磁波。該研究將多種功能集成于單一結構超材料之中,使其在電磁波的高效極化控制方面具有重要的應用價值   

Abstract:Multifunctional polarization controlling plays an important role in modern photonics, but their designs toward broad bandwidths and high efficiencies are still rather challenging. Here, by applying the inverse design method of model-based theoretical paradigm, we design cascaded chiral metamaterials for different polarization controls in oppositely propagating directions and demonstrate their broadband and high-efficiency performance theoretically and experimentally. Started with the derivation of scattering matrix towards specified polarization control, a chiral metamaterial is designed as a meta-quarter-wave plate for the forward propagating linearly polarized wave, which converts the x- or y-polarized wave into a nearly perfect left- or right-handed circularly polarized wave; intriguingly, it also serves as a 45° polarization rotator for the backward propagating linearly polarized waves. This bifunctional metamaterial shows a high transmission as well as a broad bandwidth due to the Fabry-Perot-like interference effect. Using the similar approach, an abnormal broadband meta-quarter-wave plate is achieved to convert the forward x- and y-polarized or the backward y- and x-polarized waves into left- and right-handed circularly polarized waves with high transmission efficiencies. The integration of multiple functions in a single structure endows the cascaded chiral metamaterials with great interests for the high-efficiency polarization-controlled applications. 

Editorial Summary

Metamaterials inverse design: multifunctional polarization controls based on model-based theoretical paradigm超材料的逆向設計:基于模型理論范式開發多功能極化控制超材料

本研究報道了一種基于模型理論范式的超材料逆向設計方法,可針對不同的極化控制功能,推演得到所需的超材料結構組成及序構,產生多層級聯手性超材料。來自北京科技大學的白洋教授團隊采用散射矩陣理論逆向設計出多種雙功能手性超材料,可對正反向傳播的電磁波產生不同的極化控制功能。首先,設計了一種雙功能手性超材料,對正向傳播的線極化電磁波表現為四分之一波片功能,實現線極化到圓極化電磁波的轉化;而對于反向傳播的線極化電磁波表現為45°極化旋轉器功能。其次,基于類似的設計方法,構建了異常四分之一波片,可將正向傳輸的xy極化電磁波或反向傳輸的yx極化電磁波,在寬頻范圍內高效地轉化為左旋和右旋圓極化電磁波。計算仿真與實驗測試結果均證明所設計的手性超材料不僅完好地實現了設計功能,還具有寬頻、高透射的優異特性。該研究成功地展示了一種從功能到結構的超材料逆向設計方法,為加速新功能超材料的設計開發提供了一條有效的新途徑

An inverse design method of model-based theoretical paradigm for metamaterials is reported. Aiming at specific polarization controls, the required components and spatial distribution can be derived, resulting in multilayer cascading chiral metamaterials. A team led by Prof. Yang Bai from the University of Science and Technology Beijing, China, utilized the theory of scattering matrix to inversely design multifunctional chiral metamaterials which demonstrated different polarization controls for oppositely propagating electromagnetic waves. Firstly, a chiral metamaterial was designed as a meta-quarter-wave plate of converting the forward propagating linearly polarized wave into a nearly perfect circularly polarized wave; intriguingly, it also served as a 45° polarization rotator for the backward propagating linearly polarized waves. Secondly, using the similar approach, an abnormal broadband meta-quarter-wave plate was achieved to convert the forward x- and y-polarized or the backward y- and x-polarized waves into left- and right-handed circularly polarized waves. Finally, the simulated and experimental results confirmed the desired polarization controls from theoretical derivation, and showed a high transmission efficiency in a broad bandwidth. This work not only successfully demonstrated an inverse design method from function to structure, but offered an effective route to accelerate the discovery of metamaterials with new functions.

Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods (基于薄膜材料庫-高通量表征-計算的組合合成方法發現新材料)
Alfred Ludwig
npj Computational Materials 5:70(2019)
doi:s41524-019-0205-0
Published online:10 July 2019

Abstract| Full Text | PDF OPEN

摘要:從納米顆粒到薄膜再到塊體的材料合成——基于薄膜合成和高通量表征的組合,以及與高通量計算和材料信息學結合,這樣的想法給實驗科學家提供了應用多材料系統發現新材料的想法。通過薄膜材料庫的組合合成,可以有效地制備多元材料系統以及組分梯度,其涵蓋所有驗證/證偽假設和預測所需的所有材料成分。自動化、高質量、高通量表征方法能夠全面確定薄膜材料庫中所含材料的組分、結構和(多)功能特性。創建的多維數據集支持數據驅動的材料探索方法,支持對新識別的材料進行有效優化。此外,這些數據集是多功能并存圖的基礎,囊括了組成、加工、結構和性質之間的相關性,可用于未來材料的設計   

Abstract:This perspective provides an experimentalist’s view on materials discovery in multinary materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin-film synthesis and high-throughput characterization in connection with high-throughput calculations and materials informatics. Complete multinary materials systems as well as composition gradients which cover all materials compositions necessary for verification/ falsification of hypotheses and predictions are efficiently fabricated by combinatorial synthesis of thin-film materials libraries. Automated high-quality high-throughput characterization methods enable comprehensive determination of compositional, structural and (multi)functional properties of the materials contained in the libraries. The created multidimensional datasets enable data-driven materials discoveries and support efficient optimization of newly identified materials, using combinatorial processing. Furthermore, these datasets are the basis for multifunctional existence diagrams, comprising correlations between composition, processing, structure and properties, which can be used for the design of future materials. 

Editorial Summary

Discovery of new materials: high-throughput characterization-materials libraries-computation新材料發現:三結合的組合合成

雖然薄膜材料庫(MLs)的組合沉積方法已經很成熟,但仍然需要繼續進一步自動化、高質量化的高通量表征方法來加速材料的發現。德國波鴻魯爾大學機械工程學院材料研究所材料發現與界面主席Alfred Ludwig,提出使用薄膜材料庫的組合合成和高通量表征,結合計算方法來發現新材料。此外,他認為薄膜材料數據庫不僅需要對一種屬性進行表征,還應針對盡可能多的功能屬性進行全面的描述和開發。建立MLs是非常必要且實用的,如具有良好表征的材料數據庫可用于外部研究小組的個性化研究,并進一步作(高通量)測定和分析。研究數據管理的實施對于實驗組和計算組之間的合作至關重要

A notion that whereas combinatorial deposition methods for thin-film materials libraries (MLs) are well-established, the further automatization of high-quality high-throughput characterization methods still need to be continued to accelerate materials discovery, is put forward. A powerful expert, Alfred Ludwig, from the Chair for Materials Discovery and Interfaces, Institute for Materials, Faculty of Mechanical Engineering, Ruhr University Bochum, Germany, shared his perspective on using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods to discover new materials. Furthermore, he thought that schemes where MLs are not only characterized for one property, but rather comprehensively for as many as possible functional properties need to be developed. Setting up a library of MLs would be useful, e.g., for the use of well-characterized MLs for (high-throughput) measurements and further analysis by external groups for their specific purposes. The implementation of research data management is of highest importance to enable cooperation between experimental and computational groups.

Charge disproportionation and site-selective local magnetic moments in the post-perovskite-type Fe2O3 under ultra-high pressures(后鈣鈦礦型Fe2O3 在超高壓下的電荷歧化和局部選擇性局部磁矩)
Ivan LeonovGregory Kh. Rozenberg & Igor A. Abrikosov
npj Computational Materials 5:90(2019)
doi:s41524-019-0225-9
Published online:04 September 2019

Abstract| Full Text | PDF OPEN

摘要:典型的三維Mott絕緣體赤鐵礦Fe2O3是地球下地幔的基本氧化物成分之一,在下地幔礦物學中具有重要的作用。其高壓-高溫環境下的行為,如電子特性、狀態方程和相穩定性,對理解地球內部的性質和演化具有至關重要的意義。本研究使用密度泛函和動態平均場理論(DFT + DMFT)方法研究了Fe2O3超高壓下的電子結構、磁態和晶格穩定性。在Mott轉變區,Fe2O3表現出一系列復雜的電子、磁性和結構轉變。特別是在75 GPa以上的壓力下,Fe2O3會向金屬相轉變,并具有后鈣鈦礦晶體結構和位置選擇的局部磁矩。我們的研究表明,位置選擇的相變伴隨著Fe離子的電荷歧化,即Feδ,其中δ約為0.050.09,表明電子相關性和晶格之間存在復雜的相互作用。后續結果表明,Fe2O3中的位置選擇的局部磁矩可以維持在200–250 GPa的超高壓下(即足夠高于地幔中芯-幔邊界處的超高壓)。該相的研究對于理解地球下地幔的運動速度和密度異常具有重要的意義   

Abstract:The archetypal 3d Mott insulator hematite, Fe2O3, is one of the basic oxide components playing an important role in mineralogy of Earth’s lower mantle. Its high pressure–temperature behavior, such as the electronic properties, equation of state, and phase stability is of fundamental importance for understanding the properties and evolution of the Earth’s interior. Here, we study the electronic structure, magnetic state, and lattice stability of Fe2O3 at ultra-high pressures using the density functional plus dynamical mean-field theory (DFT+DMFT) approach. In the vicinity of a Mott transition, Fe2O3 is found to exhibit a series of complex electronic, magnetic, and structural transformations. In particular, it makes a phase transition to a metal with a post-perovskite crystal structure and site-selective local moments upon compression above 75GPa. We show that the site-selective phase transition is accompanied by a charge disproportionation of Fe ions, with Feδ and δ~0.050.09, implying a complex interplay between electronic correlations and the lattice. Our results suggest that site-selective local moments in Fe2O3 persist up to ultra-high pressures of ~200–250GPa, i.e., sufficiently above the coremantle boundary. The latter can have important consequences for understanding of the velocity and density anomalies in the Earth’s lower mantle. 

Editorial Summary

Fe2O3 under ultra-high pressures: Property and evolution of the inner Earth超高壓之下的Fe2O3:凡夫向勇夫的演化

該研究證明,在Mott轉變區,電子關聯與晶格之間的相互作用會導致Fe2O3呈現復雜的電子結構和磁態,增強了Fe2O3的結構復雜性,并指出了高溫高壓下這些亞穩結構的重要性。分別來自俄羅斯M.N. 俄羅斯科學院米赫耶夫金屬物理研究所和國立科技大學“MISIS”材料建模與開發實驗室的Ivan LeonovIgor A. Abrikosov教授,利用密度泛函-動態平均場理論(DFT + DMFT)的方法,從微觀上解釋了在高壓下穆斯堡爾光譜中觀察到的高自旋(high-spin)態到低自旋(low-spin)態的共存,提出了一類新的Mott系統——具有位置選擇性的局部磁矩。這對理解地球下地幔和外核的性質和演化具有重要的影響。這項研究揭示了Fe2O3在高壓下具有復雜的電子結構和晶體結構(如其復雜的同分異構相和亞穩相),有可能影響現有的地球物理和地球化學模型

The interplay between electronic correlations and the lattice in the vicinity of a Mott transition results in the formation of complex electronic and magnetic states in Fe2O3 is demonstrated, which gives rise to a remarkable structural complexity of Fe2O3, implying a possible importance of metastable structures for understanding its high-pressure and high-temperature behavior. A team co-led by Ivan Leonov from the M.N. Miheev Institute of Metal Physics, Russian Academy of Sciences, Russia, and Igor A. Abrikosov from the Materials Modeling and Development Laboratory, National University of Science and Technology ‘MISIS’, Russia, using the density functional plus dynamical mean-field theory (DFT+DMFT) approachexplained microscopically the coexistence of the high spin and light spin states observed in Mossbauer spectroscopy at high pressures, suggesting the existence of a novel class of Mott systems—with site-selective local moments—which may have important impact for understanding the properties and evolution of the Earth’s lower mantle and outer core. The electronic and structural complexity of Fe2O3 under pressure revealed in their study, e.g., its complex allotropy and the presence of metastable phases may affect present geophysical and geochemical models.

版權所有 © 中國科學院上海硅酸鹽研究所
地址:上海市長寧區定西路1295號 郵政編碼:200050
快彩11选5中奖助手