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          期刊介绍
            《npj 计算材料学》是在线出版、完全开放获取的国际学术期刊。发表结合计算模拟与设计的材料学一流的研究成果。本刊由中国科学院上海硅酸盐研究所与英国自然出版集团(Nature Publishing Group,NPG)以伙伴关系合作出版。
            主编为陈龙庆博士,美国宾州大学材料科学与工程系、工程科学与力学系、数学系的杰出教授。
            共同主编为陈立东研究员,中国科学院上海硅酸盐研究所研究员高性能陶瓷与超微结构国家重点实验室主任。
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          Solving the electronic structure problem with machine learning (用机器学习解决电子结构问题)
          Anand ChandrasekaranDeepak KamalRohit BatraChiho KimLihua Chen & Rampi Ramprasad 
          npj Computational Materials 5:22 (2019)
          doi:s41524-019-0162-7
          Published online:18 February 2019
          Abstract| Full Text | PDF OPEN

          摘要:基于求解密度泛函理论(DFTKohn-ShamKS)方程的模拟,已成为现代材料和化学科学中研究和开发过程的重要组成部分。尽管具有很强的普适性,然而求解KS方程计算量很大,因而常规DFT计算一般只能限于几百个原子。本研究报道了一种基于机器学习的方案,可以绕过KS方程的直接求解但实现类似的功能,基于给定原子构型,可直接、快速、准确地预测材料或分子的电子结构。基于新型的旋转不变性表示,将格点周围的原子环境映射到该格点处的电子密度和局部态密度。使用预先计算得到的带有几百万的格点信息的DFT结果来训练的神经网络以得到该映射。本工作提出的方法可以精确获得KS DFT实际计算的结果,但比其快几个数量级。此外,机器学习预测方案与系统尺寸严格成线性关系   

          Abstract:Simulations based on solving the Kohn-Sham (KS) equation of density functional theory (DFT) have become a vital component of modern materials and chemical sciences research and development portfolios. Despite its versatility, routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation. Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation, and by-pass it to directly, rapidly, and accurately predict the electronic structure of a material or a molecule, given just its atomic configuration. A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point. This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points. The proposed paradigm allows for the high-fidelity emulation of KS DFT, but orders of magnitude faster than the direct solution. Moreover, the machine learning prediction scheme is strictly linear-scaling with system size. 

          Editorial Summary

          Machine Learning: Predicting Electronic Structures机器学习:快速精确预测电子结构 

          本研究开发了基于一种机器学习的方法,可以不直接求解密度泛函理论Kohn-ShamKS)方程,精确预测电子电荷密度和态密度。来自佐治亚理工学院的Rampi Ramprasad领导的团队,报道了一种基于机器学习的方案,可有效地实现KS方程的功能,利用新的旋转不变表示,将格点周围的原子环境映射到该格点处的电子密度和局部态密度。使用预先计算得到的带有几百万的格点信息的DFT结果来训练的神经网络来获得该映射。利用研究方法的可以精确模拟实际求解KS方程的结果,但是速度快几个数量级

          A machine learning model that can learn the behavior of the Kohn-Sham (KS) equation of density functional theory (DFT). After training with DFT results, the electron charge density and density of states can be predicted based only on atomic configuration information. A team led by Rampi Ramprasad from Georgia Institute of Technology, reported a machine learning-based approach that effectively assimilates functions of the KS equation, mapping the atomic environment around the grid-points to the electron density and local density at the grid-points by the new rotation-invariant expression. Their proposed typical KS DFT can be high fidelity simulations, several orders of magnitude faster than direct solutions. In addition, using modern graphical processing unit (GPU) architecture, parallelized batch training and prediction schemes, their machine learning prediction methods can be scaled linearly by system size. Other derived properties such as energy, force, dipole moment, etc., can be calculated by the model, leading to a practical and effective DFT simulator, whose accuracy is completely controlled by the theoretical level used to create the original data, and the size and diversity of the training dataset.

          Prediction of Weyl semimetal and antiferromagnetic topological insulator phases in Bi2MnSe4Bi2MnSe4Weyl半金属和反铁磁拓扑绝缘相的预测)
          Sugata ChowdhuryKevin F. Garrity & Francesca Tavazza 
          npj Computational Materials 5:33 (2019)
          doi:s41524-019-0168-1
          Published online:07 March 2019
          Abstract| Full Text | PDF OPEN

          摘要:具有强自旋-轨道耦合和磁相互作用的3D材料为实现具有时间反演对称性破缺的各种稀有和潜在有用的拓扑相提供了机会。在本研究中,我们使用第一性原理计算表明,最近合成的材料Bi2MnSe4显示了自旋轨道诱导的能带反转,这也在非磁性拓扑绝缘体Bi2PbSe4中观测到,与磁相互作用,共同导致出现数个拓扑相。在块体中,Bi2MnSe4的铁磁相在费米能级上的能带交叉具有对称保护,根据自旋的方向,可以导致节点线或Weyl半金属。由于存在时间反演对称性和部分平移对称性的组合,基态层状反铁磁相是一种反铁磁拓扑绝缘体。该相的表面本质上打破了时间反演对称性,以至于可以观测到半整数量子反常霍尔效应。此外,我们还发现,在薄膜中,对于足够厚的板,Bi2MnSe4可为一个带隙高达58 meVChern绝缘体。这种化学计量磁性材料的性能组合使Bi2MnSe4成为可以展现鲁棒拓扑行为的优秀候选   

          Abstract:Three-dimensional materials with strong spin–orbit coupling and magnetic interactions represent an opportunity to realize a variety of rare and potentially useful topological phases with broken time-reversal symmetry. In this work, we use first principles calculations to show that the recently synthesized material Bi2MnSe4 displays a combination of spin–orbit-induced band inversion, also observed in non-magnetic topological insulator Bi2PbSe4, with magnetic interactions, leading to several topological phases. In bulk form, the ferromagnetic phase of Bi2MnSe4 has symmetry protected band crossings at the Fermi level, leading to either a nodal line or Weyl semimetal, depending on the direction of the spins. Due to the combination of time reversal symmetry plus a partial translation, the ground state layered antiferromagnetic phase is instead an antiferromagnetic topological insulator. The surface of this phase intrinsically breaks time-reversal symmetry, allowing the observation of the half-integer quantum anomalous Hall effect. Furthermore, we show that in thin film form, for sufficiently thick slabs, Bi2MnSe4 becomes a Chern insulator with a band gap of up to 58meV. This combination of properties in a stoichiometric magnetic material makes Bi2MnSe4 an excellent candidate for displaying robust topological behavior. 

          Editorial Summary

          Bi2MnSe4: Prediction of Weyl Semi-Metal and Antiferromagnetic Topological Insulation Phases(Bi2MnSe4Weyl半金属和反铁磁拓扑绝缘相的预测 

          本研究发现Bi2MnSe4是与Bi2Se3结构相关的几种材料之一,具有自旋-轨道耦合诱导的能带反转,并出现时间反演对称性破缺的拓扑相。来自美国国家标准与技术研究所材料测量实验室的Sugata Chowdhury教授等,使用密度泛函理论和基于Wannier函数的紧束缚模型研究了Bi2MSe4(BMS, M=Pb, Mn)的电子性质,并预测了一系列拓扑非平凡相。他们发现由于Z点处的自旋轨道诱导的能带反转,Bi2MSe4成为时间反演不变拓扑绝缘体。将其能带反转与磁性相结合的研究发现,依据磁性有序的对称性和样品的厚度,Bi2MSe4可以通过时间反演对称性破缺转化到许多不同的拓扑相:节线系统、磁性Weyl半金属、反铁磁拓扑绝缘体或Chern绝缘体,以及半整数量子反常霍尔效应。其能带反转是连接所有这些非平凡拓扑相的基本驱动力。然而,磁序和维数控制着费米能级附近能带的对称性,从而可改变能带反转后半金属相或绝缘相的形成,以及与能带反转相关的拓扑不变量、拓扑表面和拓扑边缘特征的变化。该研究有望通过使用诸如外部磁场或温度等外场微扰来控制样品厚度和自旋有序性,从而实现实验观测到这些密切相关的各种相。Bi2MSe4因其强磁相互作用和显著的带隙,有望成为研究高温下化学计量化合物和时间反演对称性破缺拓扑相的重要材料

          Bi2MnSe4, one of several materials related to Bi2Se3 structure, showing spin-orbit-induced energy band reversal, and the appearance of time-reversed symmetrical broken topological phase was reported. Professor Sugata Chowdhury from the National Institute of Standards and Technology of the United States, using density functional theory and Wannier-based tight-binding model, studied the electronic properties of Bi2MSe4 (BMS, M = Pb, Mn) and predicted a series of topologically non-trivial phases. They found that Bi2MSe4 became a time-reversed invariant topological insulator due to the spin-band induced band reversal at Z point. Combining its band reversal with its magnetic properties, it is found that Bi2MSe4 can access many different topological phases with broken time-reversed symmetry depending on the symmetry of the magnetic ordering and the sample thickness: a nodal line system, magnetic Weyl semimetal, antiferromagnetic topological insulator, or Chern insulator, in addition to displaying the half-integer quantum anomalous Hall effect. Its band inversion is the fundamental driving force linking all these non-trivial topological phases. However, the magnetic ordering and dimensionality controls the symmetry of the bands near the Fermi level, which changes the formation of the semi-metal phase or the insulating phase after the energy band is inverted, and the topological invariants and topological surface and edge features related to band inversion. The study is expected to observe these closely related phases by manipulating sample thickness and spin order using perturbations such as external magnetic field or temperature. Due to its strong magnetic interaction and significant band gap, Bi2MSe4 is expected to be an important material for studying stoichiometric compounds and broken time-reversal symmetry topological phases at higher temperatures.

          Enhancing hydrogen evolution on the basal plane of transition metal dichacolgenide van der Waals heterostructures (过渡金属二硫化物范德华异质结基面上增强析氢)
          Faling LingWei KangHuirong JingWen ZengYankun ChenXiaoqing LiuYixin ZhangLin QiLiang Fang & Miao Zhou 
          npj Computational Materials 5:20 (2019)
          doi:s41524-019-0160-9
          Published online:18 February 2019
          Abstract| Full Text | PDF OPEN

          摘要:近年来,使用低维过渡金属二硫族化物(如MoS2)作为电化学析氢反应催化剂的研究激增。特别是,MoS2中的硫空穴可以激活其惰性基面,但这需要不切实际的高缺陷浓度(~9%)才能达到最佳活性。本研究通过第一性原理计算证明,组装范德华异质结可以提高MoS2低硫空穴浓度下的催化活性。我们将MoS2与各种二维纳米结构相结合,包括石墨烯、h-BN、磷烯、过渡金属二硫化物、MXenes及其衍生物,希望能微调氢原子吸附的自由能。我们惊奇地发现,MoS2/ MXene-OH异质结中,~2.5%的低硫空穴浓度即可获得最佳自由能。并且,这种范德华异质结具有高孔隙率和可调节性等优点。本研究证明了将组装二维范德华结构与缺陷工程相结合,能实现高效制氢的潜力   

          Abstract:Recent years have seen a surge in the use of low-dimensional transition metal dichacolgenides, such as MoS2, as catalysts for the electrochemical hydrogen evolution reaction. In particular, sulfur vacancies in MoS2 can activate the inert basal plane, but that requires an unrealistically high defect concentration (~9%) to achieve optimal activity. In this work, we demonstrate by first-principles calculations that assembling van der Waals heterostructures can enhance the catalytic activity of MoS2 with low concentrations of sulfur vacancies. We integrate MoS2 with various two-dimensional nanostructures, including graphene, h-BN, phosphorene, transition metal dichacolgenides, MXenes, and their derivatives, aiming to fine-tune the free energy of atomic hydrogen adsorption. Remarkably, an optimal free energy can be achieved for a low sulfur vacancy concentration of ~2.5% in the MoS2/MXene-OH heterostructure, as well as high porosity and tunability. These results demonstrate the potential of combining two-dimensional van der Waals assembly with defect engineering for efficient hydrogen production. 

          Editorial Summary

          范德华异质结:增强催化析氢 

          本研究证明了通过将MoS22D材料组装构建异质结用于析氢反应的可能性。来自重庆大学光电工程学院光电技术与系统教育部重点实验室的凌发令等人,利用基于密度泛函理论的第一性原理计算,预测了可通过异质结界面耦合对基面缺陷电子结构进行调控,以增强MoS2的催化能力。他们将MoS2与其他多种常见的2D材料组装而构建成范德华异质结。构建这种异质结的2D构件包括石墨烯、h-BN、单层黑磷、过渡金属硫化物和过渡金属碳化物/氮化物(MXenes)及其官能化衍生物MXene-XX = OHOF)。他们的研究表明,硫空位诱导的缺陷态特征(尤其是最低未占态的位置和密度),可通过异质结内的层间相互作用进行微调。MoS2/MXene-OH异质结的硫空穴浓度低到2.5%时,就可实现ΔGH=0eV的最佳H吸附,如此低的浓度极限前所未有,在实验中很容易实现。此外,这种异质结内的层状几何结构还能使有效硫空穴尽可能地暴露,这可用于高孔隙率的高效催化剂,为其实用化铺平了一条光明的大道。他们的发现具有很好的普适性,可为将来基于范德华异质结的析氢反应和其他重要化学反应的非均相催化剂研究和开发提供新的思路

          The possibility of employing heterostructure assembly by integration of MoS2 with 2D structures for hydrogen evolution reactions (HER) was demonstrated. A team led by Prof. Miao ZHOU from the Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education of Chongqing University, optimized the interface interaction and enhanced the catalytic ability of MoS2 by means of first-principles calculations based on density functional theory. They combined MoS2 with other common substances to form a variety of van der Waals heterojunctions. The 2D building blocks for the construction of such heterojunctions include graphene, h-BN, black phosphorene, transition metal disulfides and transition metal carbides/nitrides (MXenes) and their functionalized derivatives MXene-X (X = OH, O, F). Their research shows that the sulfur-vacancy-induced defect state characteristics, especially the position and density of the lowest unoccupied state, can be fine-tuned by interlayer interaction within the heterojunction. When the concentration of sulfur vacancy in the MoS2/ MXene-OH heterojunction is even as low as 2.5%, the optimal H adsorption with ΔGH = 0 eV can still be achieved. Such a low concentration limit is unprecedented and can be easily realized in experiments. In addition, the layered geometry within the heterojunction also allows the effective sulfur vacancies to be exposed as much as possible, which can be used for high-porosity and high-efficiency catalysts, paving the way for a practical avenue. Their findings are basically applicable, and can provide new insights for the future research and development of heterogeneous catalysts based on van der Waals heterojunctions towards HER and other important chemical reactions.

          Designing interfaces in energy materials applications with first-principles calculations (通过第一性原理计算设计能源材料应用中的界面)
          Keith T. Butler,Gopalakrishnan Sai Gautam & Pieremanuele Canepa 
          npj Computational Materials 5:19 (2019)
          doi:s41524-019-0160-9
          Published online:15 February 2019
          Abstract| Full Text | PDF OPEN

          摘要:能源相关应用的材料对可持续能源经济至关重要,它们依赖于形成复杂异质界面的材料组合。同时,计算材料科学在描述复杂界面方面的进展,对于深入研究能源材料及其性能至关重要。因此,我们对电池、光电和光催化领域中用以界面调节的物理量作了深入的综述,并重点介绍了密度泛函理论方法的计算。面向每种能源领域的应用,我们都强调为计算界面属性而开发的独特方法,并探索了跨学科应用其中一些方法的可能性,从而对界面设计进行了统一的概述。最后,我们确定了一系列新的挑战,以进一步完善在能源设备中界面的理论描述   

          Abstract:Materials for energy-related applications, which are crucial for a sustainable energy economy, rely on combining materials that form complex heterogenous interfaces. Simultaneously, progress in computational materials science in describing complex interfaces is critical for improving the understanding and performance of energy materials. Hence, we present an in-depth review of the physical quantities regulating interfaces in batteries, photovoltaics, and photocatalysts, that are accessible from modern electronic structure methods, with a focus on density functional theory calculations. For each energy application, we highlight unique approaches that have been developed to calculate interfacial properties and explore the possibility of applying some of these approaches across disciplines, leading to a unified overview of interface design. Finally, we identify a set of challenges for further improving the theoretical description of interfaces in energy devices. 

          Editorial Summary

          Deep Learning Analysis: Defect and Phase Evolution of STEM "Movie" 能源材料的界面设计:第一性原理计算 

          本综述介绍了如何利用第一性原理来研究能源材料哪些有用和重要的界面特性,以及如何理解和预测能源材料的参数。来自英国Rutherford Appleton实验室科学计算部SciMLKeith T. Butler教授、美国普林斯顿大学机械与航空航天工程系的Gopalakrishnan Sai Gautam教授、新加坡国立大学材料科学与工程系的Pieremanuele Canepa教授,共同回顾了利用第一性原理计算设计能源材料界面的研究进展。他们首先概述了铅笔纸理论,它是一种解释电子结构模拟计算材料性能的理论。随后,他们强调了这些概念在计算能源材料特性中的应用。他们提出了未来能源材料界面建模需要解决的问题和挑战,这些挑战的突破对于成功理解、设计和改进计算材料科学中的界面至关重要

          A review describes how first principles can be used to study which useful and important interface properties of energy materials, and how to understand and predict energy material parameters. Professor Keith T. Butler from SciML, Scientific Computing Department, Rutherford Appleton Laboratory, UK, Professor Gopalakrishnan Sai Gautam,  from Department of Mechanical and Aerospace Engineering, Princeton University, USA, and Professor Pieremanuele Canepa, from Department of Materials Science and Engineering, National University of Singapore, co-reviewed advances in applications with first-principles calculations to design material interfaces in energy materials. They first outlined the "pencil paper" theory, which is a theory that explains the electronic structure simulation to calculate the material properties. Subsequently, they highlighted the application of these concepts to calculate the characteristics of energy materials. They found a unique set of technologies developed in various fields to calculate the properties of their materials, many of which are expected to be applied to other energy materials. Finally, they laid out the challenges that need to be addressed for the future of predictive interface modelling in energy materials that are critical to the successful understanding, design, and improvement of interfaces in computational materials science..

          Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2  (WS2电子束诱导转换过程中缺陷和相演变的深度学习分析)
          Artem MaksovOndrej DyckKai WangKai XiaoDavid B. GeoheganBobby G. SumpterRama K. VasudevanStephen JesseSergei V. Kalinin & Maxim Ziatdinov 
          npj Computational Materials 5:12 (2019)
          doi:s41524-019-0152-9
          Published online:01 February 2019
          Abstract| Full Text | PDF OPEN

          摘要:扫描透射电子显微镜(STEM)的新发展使得在原子尺度上实时可视化材料中的固态转变,包括电子束和温度导致转变成为可能。然而,尽管高分辨率数据的采集能力在不断增强,从中获取转变过程的热力学和动力学,以及单个缺陷的动力学和相互作用等信息却十分有限。这是由于利用手动非原位的方式来分析数据存在局限性导致的。为了解决这个问题,本研究开发了一种用于动态STEM成像的深度学习框架,其经过训练可识别不同的晶格缺陷,并将该框架应用于分析WS2中的固态反应转换。经过训练的深度学习模型可在几秒钟内从原始STEM数据中提取数千个晶格缺陷,然后使用无监督聚类方法将其分为不同的类别。我们进一步扩展了这个框架以提取硫空位扩散参数,并分析了钼掺杂原子和硫空穴组成的复杂缺陷在不同组态之间转换的几率,深入了解点缺陷动力学和各种反应。这种方法具有普适性,将其应用于束流诱导的反应能够在原子尺度描绘固体中化学反应的路径。   

          扫描透射电子显微镜(STEM)的最新进展可以实时可视化材料中的固态变换,包括由电子束和温度引起的原子分辨率的固态变换。然而,尽管高分辨率数据采集的能力不断扩大,但推断的该过程动力学和热力学以及单一缺陷动力学和相互作用的有关信息是最小的。这是由于收集的数据的手动非原位分析存在固有局限性。为了解决这个问题,本研究开发了一种用于动态STEM成像的深度学习框架,该框架经过训练可发现晶格缺陷,并能将其应用于映射层状WS2中的固态反应和变换。经过训练的深度学习模型可在几秒钟内从原始STEM数据中提取数千个晶格缺陷,然后使用无监督聚类方法将其分为不同的类别。我们进一步扩展了这个框架以提取硫空穴扩散参数,并 分析了Mo掺杂剂和硫空穴组成的不同构象的缺陷配合物之间切换相关的转换概率,从而深入了解点缺陷动力学和各种反应。这种方法是通用的,并且其在束诱导反应中的应用,允许在原子水平上描画固体中的化学转化途径。

          Abstract:Recent advances in scanning transmission electron microscopy (STEM) allow the real-time visualization of solid-state transformations in materials, including those induced by an electron beam and temperature, with atomic resolution. However, despite the ever-expanding capabilities for high-resolution data acquisition, the inferred information about kinetics and thermodynamics of the process, and single defect dynamics and interactions is minimal. This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data. To circumvent this problem, we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS2. The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds, which are then classified into different categories using unsupervised clustering methods. We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy, providing insight into point-defect dynamics and reactions. This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level. 

          Editorial Summary

          Deep Learning Analysis: Defect and Phase Evolution of STEM "Movie" 深度学习分析:缺陷和相演变的“电影” 

          该研究开发了一个深度学习网络,可用于快速分析扫描透射电子显微镜(STEM)的动态数据,建立点缺陷相互作用和固态反应动力学的完整图像。来自美国橡树岭国家实验室的Sergei V. Kalinin(本刊副主编)和Maxim Ziatdinov共同领导的团队,提出了一种基于深度学习的方法,用于分析Mo掺杂WS2STEM图像中晶格结构的动态变换的“电影”。他们首先训练了一个深度学习神经网络使其能够在STEM图像中识别出不同的晶格点缺陷,经过训练的深度学习模型可在几秒钟内从原始STEM数据中提取数千个晶格缺陷。然后采用高斯混合模型,对检测到的缺陷结构进行无监督分类,找出分类结果与特定的物理结构的联系。该方法可以从STEM数据中识别主要点缺陷及其特征统计行为,分析所选种类缺陷的扩散,研究复杂缺陷不同组态间的转化路径及转换概率。他们的方法可在原子水平上研究材料中的点缺陷动力学和固态反应

          A deep-learning framework for rapid analysis of dynamic scanning transmission electron microscopy (STEM) imaging is proposed to map the solid state reactions and transformations in materials. A team co-led by Sergei V. Kalinin (Deputy Editor-in-Chief of this Journal) and Maxim Ziatdinov from the Oak Ridge National Laboratory, USA, proposed a deep-learning-based approach for analysis of the "movie" of the transformation of the lattice structures, which was observed in Mo-doped WS2 by STEM. They started by training a deep neural network to identify the lattice defect in STEM data and then extracted thousands of lattice defects from raw STEM data. They performed unsupervised classification of the extracted defect structures using a Gaussian mixture model and showed that the classification results can be linked to specific physical structures. This approach was then utilized to identify dominant point defects and their characteristic statistical behaviors, analyze the diffusion of selected defect species, and study transformation pathways for the defect complexes of Mo dopant and sulfur vacancy and the transition probabilities. This approach is capable of mapping the point-defect dynamics and solid state reactions in materials on the atomic level.

          Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride (通过深度学习预测构型杂化石墨烯-氮化硼构型的带隙)
          Yuan Dong, Chuhan Wu, Chi Zhang, Yingda Liu, Jianlin Cheng & Jian Lin 
          npj Computational Materials 5:26 (2019)
          doi:s41524-019-0165-4
          Published online:26 February 2019
          Abstract| Full Text | PDF OPEN

          摘要:众所周知,掺杂剂的原子级和纳米级空间取向(构型)在确定材料的电子特性方面起着至关重要的作用。然而,由于原子构型的可能范围很大,预测这种效应极富挑战。深度学习算法如何能够在具有任意超晶格构型的杂化硼氮石墨烯中实现带隙预测,本研究提出了一个研究案例。本研究为卷积神经网络开发了一种能将结构和带隙关联起来的材料描述符。通过从头算法计算的带隙和相应的结构被用来训练数据集。训练后的网络被用来预测带有各种构型的系统中的带隙。它们准确地预测了4×45×5超级单元的带隙,R2> 90%,均方根误差为~0.1 eV。以小超级单元中产生的数据作转移学习,可提高6×6超级单元的预测精度。这项工作为后续杂化石墨烯和其他2D材料的构型研究铺平了道路。此外,鉴于材料中构型的普遍存在,这项工作可能激发人们浓厚的兴趣,将深度学习算法应用于跨尺度不同材料的构型设计中   

          Abstract:It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials. However, predicting such effects is challenging due to the large range of atomic configurations that are possible. Here, we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations. A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks. Bandgaps calculated by ab initio calculations, and corresponding structures, were used as training datasets. The trained networks were then used to predict bandgaps of systems with various configurations. For 4×4 and 5×5 supercells they accurately predict bandgaps, with a R2 of >90% and root-mean-square error of ~0.1eV. The transfer learningTL was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6×6 supercells. This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials. Moreover, given the ubiquitous existence of configurations in materials, this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales. 

          Editorial Summary

          Bandgap prediction by deep learning: configurationally hybridized graphene -boron nitride 深度学习预测:杂化物构型的带隙 

          本研究开发了深度学习模型来预测具有任意超晶格构型的石墨烯-氮化硼杂化对的带隙。来自美国密苏里大学机械与航空航天工程系的Jian Lin教授和电气工程与计算机科学系的Jianlin Cheng教授等,设想用级联卷积网络(CNN),来预测石墨烯-氮化硼杂化对的电子特性。他们发现,经过结构信息和从头算密度泛函理论(DFT)计算的带隙训练后,这些CNN能够精确预测任何给定构型的石墨烯-氮化硼杂化对的带隙。预测的4×45×5超级单元的带隙精度R2> 90%,均方根误差为~0.1 eV。预测精度如此之高的主要原因是其所开发的材料描述符。这种描述符系统能够定性和定量地捕获构型状态的特征,其结构中的每个原子影响其相邻原子,以致这些局域化的原子簇共同确定了整个结构的带隙。考虑到自下而上化学合成的掺杂石墨烯的精确到原子尺度的结构已经在实验中获得,他们的方法为后续石墨烯和其他2D材料及其性质的研究提供了基础,也将引起人们更广泛的兴趣,将这些描述符系统和级联卷积网络模型用来解决材料领域其他机器学习所无法解决的问题

          Deep learning (DL) models to predict the bandgaps of hybrids of graphene and h-BN with arbitrary supercell configurations were developed. A team led by Prof. Jianlin Cheng and Prof. Jian Lin from the University of Missouri, USA, conceive to employ convolutional neural networks (CNNs) for predicting electronic properties of hybridized graphene and h-BN with randomly configured supercells. They discovered that after trained with structural information and the bandgaps calculated from ab initio density function theory (DFT), these CNNs enabled to precisely predict the bandgaps of hybridized graphene and boron nitride (BN) pairs with any given configurations. The predicted band gap accuracy of the 4×4 and 5×5 supercells is R2 > 90% and root mean square error is ~0.1 eV, for which the main reason arises from the developed material descriptor. Such a descriptive system enables to qualitatively and quantitatively capture the features of configurational states, where each atom in the structure affects its neighbor atoms so that these localized atomic clusters collectively determine bandgaps of the whole structure. Considering that atom-scale precise structures of doped graphene by bottom-up chemical synthesis have been experimental realized, this work provides a cornerstone for future investigation of graphene and other 2D materials as well as their associated properties. This work will bring up broader interests in applying the designed descriptive system and the CNN models for many materials related problems, which are not accessible to other machine learning algorithms.

          Transition from source- to stress-controlled plasticity in nanotwinned materials below a softening temperature (低于软化温度时纳米孪晶材料塑性的源-控向应力-控转变)
          Seyedeh Mohadeseh, Taheri Mousavi, Haofei Zhou, Guijin Zou & Huajian Gao 
          npj Computational Materials 5:2 (2019)
          doi:s41524-018-0140-5
          Published online:04 January 2019
          Abstract| Full Text | PDF OPEN

          摘要:纳米孪晶材料是一种具有高强度、良好延展性、断裂韧性大、抗疲劳性能好、蠕变稳定性好等优异性能的纳米结构材料。最近出现了一个明显的争议,即关于纳米孪晶材料的强度如何随着孪晶厚度的减小而变化。当孪晶厚度降低到临界值以下时,纳米孪晶Cu发生了从硬化到软化的转变,而在陶瓷和金刚石中则发生了连续硬化。本研究通过原子模拟和纳米孪晶PdCu系统的理论模型构建,发现存在一个软化温度,当低于该软化温度时,材料随孪晶厚度减小而不断硬化(如纳米孪晶陶瓷和金刚石),而高于该软化温度时,其强度先增加后降低,在临界孪晶厚度下,材料强度达到最大值,材料由硬化过渡到软化(如纳米孪晶Cu)。这一重要现象归因于在软化温度以下,塑性从“源-控”向“应力-控”的转变。同时,这一现象表明,即使在相同的纳米孪晶材料中,也可能存在不同的硬化行为,且在一定的温度下,不同的材料在不同的软化温度下也会表现出不同的硬化行为   

          Abstract:Nanotwinned materials have been widely studied as a promising class of nanostructured materials that exhibit an exceptional combination of high strength, good ductility, large fracture toughness, remarkable fatigue resistance, and creep stability. Recently, an apparent controversy has emerged with respect to how the strength of nanotwinned materials varies as the twin thickness is reduced. While a transition from hardening to softening was observed in nanotwinned Cu when the twin thickness is reduced below a critical value, continuous hardening was reported in nanotwinned ceramics and nanotwinned diamond. Here, by conducting atomistic simulations and developing a theoretical modeling of nanotwinned Pd and Cu systems, we discovered that there exists a softening temperature, below which the material hardens continuously as the twin thickness is reduced (as in nanotwinned ceramics and diamond), while above which the strength first increases and then decreases, exhibiting a maximum strength and a hardening to softening transition at a critical twin thickness (as in nanotwinned Cu). This important phenomenon has been attributed to a transition from source- to stress-controlled plasticity below the softening temperature, and suggests that different hardening behaviors may exist even in the same nanotwinned material depending on the temperature and that at a given temperature, different materials could exhibit different hardening behaviors depending on their softening temperature. 

          Editorial Summary

          Nanotwinned materials: Plasticity below a softening temperature 纳米孪晶材料:软化温度下的塑性转变(自拟) 

          该研究证明了纳米孪晶(nanotwined materials, nt)材料存在一个软化温度Ts,温度低于Ts时材料随着孪晶厚度的减小而持续硬化,而温度高于Ts时,强度先增加后减小,在临界孪晶厚度下,材料强度达到最大值,材料由硬化过渡到软化。来自美国 Brown UniversityHuajian Gao教授领导的团队,使用分子动力学(MD)对多晶nt-Pdnt-Cu样品进行了模拟,并建立了不受MD尺寸和时间尺度限制的基本理论模型,研究了孪晶厚度降低到临界值以下时硬度的变化。研究结果表明,在非常小的孪晶厚度(<λcrit)下,变形受孪晶晶界(twin boundaries, TBs)的迁移控制,这些TB与在TB-晶界(grain boundaries, GBs)交叉点成核的孪晶部分位错有关。虽然孪晶部分的成核受限于高于Ts的位错源的数量,但相同的成核过程在低于Ts时,则会受到TB-GB交叉点局部应力集中的限制,其峰值应力水平随着TB间距的减小而减小,导致连续硬化。因此,软化温度Ts划分了从位错源数-控制(源-控)向位错应力值-控制(应力-控)的TB迁移转变。该理论模型提示,原子键合越强,软化温度越高。他们所观察到的规律可适用于所有nt材料。

          There exists a softening temperature, Ts, for nano-twinned (nt) materials, below which the material hardens continuously as the twin thickness is reduced, while above which the strength first increases and then decreases, exhibiting a maximum strength and a hardening to softening transition at a critical twin thickness. A team led by Prof. Huajian Gao from Brown University in the United States established the basic phenomenon thorough molecular dynamics (MD) simulations of polycrystalline nt-Pd and nt-Cu samples, and by theoretical modeling that is not subjected to the usual limitations of MD in size and time scale. The change in hardness when the thickness of the nt-materials is reduced below the critical value. Their simulation and modeling results reveal that at very small twin thicknesses (<λcrit), the deformation is governed by the migration of twin boundaries (TBs) associated with twinning partial dislocations nucleated at TB–grain boundaries (TBs) intersections. While the nucleation of twinning partials is limited by the number of dislocation sources above Ts, below Ts the same nucleation process becomes limited by local stress concentration at the TB–GB intersections, whose peak stress level, decreases with reduced TB spacing, leading to continuous hardening. Thus, the softening temperature Ts demarcates a transition from source- to stress-controlled TBs migration. The theoretical model suggests that the stronger the atomic bonding, the higher the softening temperature, and that the observed behavior could be generic to all nt-materials.

          Strain engineering of electro-optic constants in ferroelectric materials (铁电材料电-光常数的应变工程)
          Charles PaillardSergei Prokhorenko & Laurent Bellaiche 
          npj Computational Materials 5:6 (2019)
          doi:s41524-018-0141-4
          Published online:08 January 2019
          Abstract| Full Text | PDF OPEN

          摘要:基于电-光效应可以利用电场调控光强变化成为可能,该效应对于当今信息和通信技术(例如电视显示器和光纤)十分重要。在薄膜中寻找大的电-光常数对于电-光器件的小型化和提高电-光效率十分关键。本研究证明了通过PbTiO3 薄膜的应变工程可以有选择性地改进某个电-光常数。我们预测了高达100pmV-1 的无应力电光常数,该数值既可以源于拉伸应变作用下相变边界处光学声子模式的软化,亦可源于压缩应变产生等效负压导致的大压电常数。具体地,可以通过在硅这一技术上十分重要的衬底材料上生长出PbTiO3,来获得大的r33-光系数,其数值可为常用的LiNbO3  电光材料的两倍   

          Abstract:Electro-optic effects allow control of the ow of light using electric fields, and are of utmost importance for today’s information and communication technologies, such as TV displays and fiber optics. The search for large electro-optic constants in films is essential to the miniaturization and increased efficiency of electro-optic devices. In this work, we demonstrate that strain-engineering in PbTiO3 films allows to selectively choose which electro-optic constant to improve. Unclamped electro-optic constants larger than 100pmV-1  are predicted, either by driving the softening of an optical phonon mode at a phase transition boundary under tensile strain, or by generating the equivalent of a negative pressure via compressive strain to obtain extremely large piezoelectric constants. In particular, a r33 electro-optic coefficient twice as large as the one of the commonly used LiNbO3 electro-optic material is found here when growing PbTiO3 on the technologically important Si substrate. 

          Editorial Summary

          Large electric-optic constant: Strain engineering in PbTiO3 film 

          基于铁电薄膜PbTiO3 的应变工程(拉伸或压缩)即可选择性地获得大的电-光常数。在薄膜中寻找大电-光常数对于电-光器件的小型化和提高电-光效率是必不可少的。来自美国阿肯色大学的LaurentBellaiche 领导的团队,采用精确的从头算技术,研究了应变对于PbTiO3这一经典铁电体电-光特性的影响,以企确定导致铁电PbTiO3薄膜中高电光性能的一般机制。计算结果显示,应变工程可以有选择性地改进某个电-光常数,具体增强机制有两种:其一为利用拉伸应变实现相变边界处光学声子模式的软化;其二为利用压缩应变产生等效负压导致大的压电系数。他们通过计算预测出了应变下的大于100pmV-1  的大电-光常数发现若以硅材料作生长PbTiO3薄膜的衬底,所获得的薄膜材料的r33-光系数可达57 pm/V,接近标准-材料LiNbO3的两倍!

          Large electric-optic constant can be obtained by strain engineering in ferroelectric film. Films with large electro-optic (EO) constants is essential for miniaturization of EO devices and improving EO efficiency. A team led by Laurent Bellaiche from the University of Arkansas in the United States, using precise ab initio techniques, explored a wide range of epitaxial strain conditions to determine EO effects in ferroelectric PbTiO3 films. They demonstrated that strain engineering of PbTiO3 films can be arbitrarily chosen to improve a certain EO constant. Two enhancing mechanisms were found, as the first driven force is softening of an optical phonon mode at a phase transition boundary under tensile strain, while the other is driven by the extremely large piezoelectric constants resulted from generating the equivalent of a negative pressure via compressive strain. EO constants larger than 100pmV-1 were predicted, and could attain 57 pm/V, almost twice the standard electro-optical material LiNbO3 if PbTiO3 film growing on the technologically important Si substrate.

          Multi-loop node line states in ternary MgSrSi-type crystals (三元MgSrSi型晶体中的多环节点线状态)
          Jinling LianLixian YuQi-Feng Liang, Jian Zhou, Rui Yu & Hongming Weng 
          npj Computational Materials 5:10 (2019)
          doi:s41524-019-0150-y
          Published online:21 February 2019
          Abstract| Full Text | PDF OPEN

          摘要:受镜像对称(mirror symmetry, m-NLs)、空间反演和时间反演对称的乘积S = PTs-NLs),或非简单空间群对称等保护的节点线能带交叉是拓扑半金属在布里渊区中的非平凡拓扑对象。本研究使用第一原理计算筛选了一系列的MgSrSi型三元晶体,并发现超过70个成员是节点线半金属。特别是在AsRhTi晶体中发现了一种新颖的多节线结构,其中一个s-NL在某个“链接点”处与一个m-NL稳健地接触,同时该s-NL还与另一个m-NL嵌套形成Hopf环链。与先前提出的由两个s-NL或两个m-NL形成的Hopf环链不同,由一个s-NL和一个m-NL形成的Hopf环链需要最小的三带模型来表征其基本电子结构。本研究还获得了AsRhTi晶体的不同表面上的相关拓扑表面态。在AsFeNbPNiNb中预测了更复杂和奇特的NLs多环结构。我们的研究可能有助于在真实材料中寻找奇异的多环节点线半金属   

          Abstract:Node line band-touchings protected by mirror symmetry (named as m-NLs), the product of inversion and time reversal symmetry S=PT (named as s-NLs), or nonsymmorphic symmetry are nontrivial topological objects of topological semimetals in the Brillouin Zone. In this work, we screened a family of MgSrSi-type crystals using first principles calculations, and discovered that more than 70 members are node-line semimetals. A new type of multi-loop structure was found in AsRhTi that a s-NL touches robustly with a m-NL at some “nexus point”, and in the meanwhile a second m-NL crosses with the s-NL to form a Hopf-link. Unlike the previously proposed Hopf-link formed by two s-NLs or two m-NLs, a Hopf-link formed by a s-NL and a m-NL requires a minimal three-band model to characterize its essential electronic structure. The associated topological surface states on different surfaces of AsRhTi crystal were also obtained. Even more complicated and exotic multi-loop structure of NLs were predicted in AsFeNb and PNiNb. Our work may shed light on search for exotic multi-loop node-line semimetals in real materials. 

          Editorial Summary

          Multi-loop node line state: in ternary MgSrSi-type crystal三元MgSrSi型晶体:多环节点线态 

          该研究发现了70多种MgSrSi型晶体化合物在其带结构中存在节点线能带交叉。拓扑半金属中导带和价带的带交叉是布里渊区的有趣拓扑对象,它赋予拓扑半金属独特的电子结构和电学性质。其中节点线(node-line, NL)半金属的带交叉形成闭环,但这仅是依据一些材料提出的概念,在实际材料中的直接证据则几乎没有。来自绍兴文理学院的梁奇锋教授和武汉大学的余睿教授领导的联合团队,使用第一原理计算,对MgSrSi三元晶体族的660种晶体作了筛选,发现有70多种化合物是节点线半金属。由于空间群中反射对称性和时间反演对称性的共存,AsRhTi表现出新颖的多环NL结构,其中由PT对称性保护的s-NL与由镜像对称保护的m-NL在一些“链接点”稳健地接触,同时该s-NL还与另一个m-NL嵌套形成一个Hopf-环链。这个Hopf-环链需要最小的三带k p模型来表征其基本电子结构。研究结果进一步展示了由非平凡NL结构所导致的拓扑表面态。拓扑表面态具有非平凡NL结构。他们的研究可能有助于在真实材料中寻找奇异的多环节点线半金属

          More than 70 kinds of MgSrSi-type crystals are node-line semimetals showing a variety of NL structures. The intersection of the conduction band and the valence band in the topological semimetal is an interesting topological object of the Brillouin zone, which endows unique electronic structure and electrical properties to the topological semimetal. Among them, the node-line (NL) semimetal band form a closed loop, but this is only a proposed concept based on some materials, and there is almost no direct evidence in real materials. A joint team led by Professor Qi-feng Liang from Shaoxing University and Professor Rui Yu from Wuhan University used the first principle calculation to screen the ternary crystal family of MgSrSi-type crystals which consists of 660 members, and discovered that more than 70 compounds were node line semimetals. A new type of multi-loop structure was found in AsRhTi that a s-NL touches robustly with a m-NL at some “nexus point”, and in the meanwhile a second m-NL crosses with the s-NL to form a Hopf-link. This Hopf-link requires a minimal three-band k p model to characterize its basic electronic structure. Their study further demonstrates that these non-trivial NL structures induce topological surface states on the crystal surfaces. Their research may help find exotic multi-loop node-line semimetals in real materials.

          Local-environment dependence of stacking fault energies in concentrated solid-solution alloys高浓度固溶体合金中堆垛层错能的局域环境依赖性
          Shijun Zhao, Yuri Osetsky, G. Malcolm Stocks & Yanwen Zhang 
          npj Computational Materials 5:13 (2019)
          doi:s41524-019-0150-y
          Published online:04 February 2019
          Abstract| Full Text | PDF OPEN

          摘要:基于3d过渡金属的高浓度固溶体合金(CSA)拥有非凡的机械性能和抗辐照性能,这些优异的性能都与其较低的堆垛层错能(SFE)相关。由于内在的无序原子排布,CSA中的SFE值取决于局部原子的空间分布。本研究基于经验势和第一原理计算,研究了NiCoNiFeNiCoCr的等高浓度CSASFE的分布。我们的研究表明,无序结构的CSASFE的分布取决于计算中采用的堆垛层错区域的大小。通过电子结构分析,我们发现CSASFE的变化与堆垛层错区域中的电荷密度再分布有关。我们进一步提出了一个化学键断裂和再形成的模型来描述这种局部SFE的变化,从而可以基于局部结构来研究和预测CSA中的SFE的分布。我们的结果还表明,对于NiCo,堆垛层错引起的扰动仅局限于层错附近的最近邻密排面,而在NiFeNiCoCr中,由于FeCr的部分填充的d电子的存在,层错的扰动可以延伸到第三近邻的密排面   

          Abstract:Concentrated solid-solution alloys (CSAs) based on 3d transition metals have demonstrated extraordinary mechanical properties and radiation resistance associated with their low stacking fault energies (SFEs). Owing to the intrinsic disorder, SFEs in CSAs exhibit distributions depending on local atomic configurations. In this work, the distribution of SFEs in equiatomic CSAs of NiCo, NiFe, and NiCoCr are investigated based on empirical potential and first-principles calculations. We show that the calculated distribution of SFEs in chemically disordered CSAs depends on the stacking fault area using empirical potential calculations. Based on electronic structure calculations, we find that local variations of SFEs in CSAs correlate with the charge density redistribution in the stacking fault region. We further propose a bond breaking and forming model to understand and predict the SFEs in CSAs based on the local structure alone. It is shown that the perturbation induced by a stacking fault is localized in the first-nearest planes for NiCo, but extends up to the third nearest planes for NiFe and NiCoCr because of partially filled d electrons in Fe and Cr. 

          Editorial Summary

          Concentrated solid-solution alloys: stacking fault energy and its local environment dependence高浓度固溶体合金:堆垛层错能与层错局部环境的关系 

          该研究发现高浓度固溶合金中的堆垛层错能与堆垛层错所在区域的电荷密度再分布相关。来自美国橡树岭国家实验室的Shijun Zhao(赵仕俊,现为香港城市大学机械工程系助理教授)等人,基于经验势和第一原理计算,研究了NiCoNiFeNiCoCr的等高浓度固溶合金中堆垛层错能的分布。他们发现堆垛层错的影响在NiCo中是局部的,而在NiFeNiCoCr中是相对长程的,证明了高浓度固溶体合金中的局部堆垛层错能可以用化学键临界点处的电荷密度再分布来表征,并进一步提出了一个化学键的断裂和再形成模型。依据该模型仅凭短程效应即可预测特定原子空间构型下的局部堆垛层错能,并可表征堆垛层错能对局部环境的依赖性。这些研究结果使局部原子空间排列及其堆垛层错能之间建立了明确的联系,对理解高浓度固溶体合金的局部性质和预测相关效应(如材料相和结构稳定性、原子转移、位错性质等)都非常重要

          Stacking fault energy (SFE) in concentrated solid-solution alloys (CSAs) is related to charge density redistribution in the region where stacking faults are located. A team from Oak Ridge National Laboratory, studied the SFE distribution of NiCo, NiFe and NiCoCr CSAs based on empirical and first-principles calculations. They found that the effect of stacking faults is localized in NiCo and relatively long-ranged in NiFe and NiCoCr, demonstrating that the local SFE can be described by charge density redistribution at the bond critical points. Based on electronic structure results, they further proposed a bond breaking and formation model to understand the SFE distributions in CSAs, by which the short-range effect can be used to predict the local SFE under a specific atomic configuration. These findings establish a clear link between local atomic configuration and SFE, which are important for understanding the local properties of CSAs and predicting the associated effects.

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