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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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  《npj 计算材料学》是在线出版、完全开放获取的国际...
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Sequential piezoresponse force microscopy and the ‘small-data’ problem(顺序压电响应力显微镜和“小数据”问题) 
Harsh TrivediVladimir V. ShvartsmanMarco S. A. MedeirosRobert C. Pullar & Doru C. Lupascu
npj Computational Materials 4:28 (2018)
doi:s41524-018-0084-9
Published online:21 june 2018
Abstract| Full Text | PDF OPEN

摘要:“大数据”对于材料领域来说不仅意味着数据集体量庞大,也指代其种类繁杂。这是在材料学及化学中进行组合搜索时遇到的常见问题。然而,由于测量中变量的步长总是受限的,因此这些数据集只能算是“小”规模的。这种限制导致了高阶统计方法的失效,而无监督学习方法的选择也只能局限于那些基于利用低阶统计的方法。作为一个有趣的案例,本研究利用可变磁场压电响应力显微镜(PFM)研究了多铁复合材料,其中压电响应的磁场依赖性,因实验限制只能得到粗步长测量数据。从该数据中有效地提取出上述与局域磁电效应相关的磁场依赖性是本研究要解决的核心问题。通过利用密度聚类方法在数据中预先标记出可能的模式,我们评估了主成分分析(PCA)作为一种简单的非监督学习技术的表现。以这种组合分析为基础,我们着重研究了在上述案例中,如何使用基于非中心二次矩的PCA,才能提取出有关局域材料响应及其空间分布的信息   

Abstract:The term big-data in the context of materials science not only stands for the volume, but also for the heterogeneous nature of the characterization data-sets.This is a common problem in combinatorial searches in materials science, as well as chemistry.However, these data-sets may well be ‘small’ in terms of limited step-size of the measurement variables.Due to this limitation, application of higher-order statistics is not effective, and the choice of a suitable unsupervised learning method is restricted to those utilizing lower-order statistics. As an interesting case study, we present here variable magnetic-field Piezoresponse Force Microscopy (PFM) study of composite multiferroics, where due to experimental limitations the magnetic field dependence of piezoresponse is registered with a coarse step-size. An efficient extraction of this dependence, which corresponds to the local magnetoelectric effect, forms the central problem of this work.We evaluate the performance of Principal Component Analysis (PCA) as a simple unsupervised learning technique, by pre-labeling possible patterns in the data using Density Based Clustering (DBSCAN).Based on this combinational analysis, we highlight how PCA using non-central second-moment can be useful in such cases for extracting information about the local material response and the corresponding spatial distribution. 

Editorial Summary

Scanning probe microscopy: machine-assisted material insights(扫描探针显微镜:机器学习辅助材料探微) 

无监督机器学习方法可以从小型且有噪声的数据集中识别材料响应的重要特征。扫描探针显微镜的功能成像模式,可以显示材料性能随外场作用(如温度或磁场)变化反映材料性质的变化。然而,由于自由度数目很多,表征全参数空间所需的测量次数十分巨大。来自德国杜伊斯堡-埃森大学和葡萄牙阿威罗大学的Harsh Trivedi及其同事证明,数据科学技术能够从较少的测量结果中提取重要规律。他们发现,基于密度聚类和主成分分析算法所得到的关键特征,成功地捕获了两种材料之间的磁电响应差异。该技术可以更广泛地应用于功能材料的分析,既方便又便宜。

Unsupervised learning methods can identify the important features of a material’s response from small and noisy datasets. Functional imaging modes for scanning probe microscopy map the changes in material properties in response to external factors such as temperature or magnetic fields.However, the large number of degrees of freedom means the number of measurements required to characterize the full parameter space can be prohibitively high.Harsh Trivedi and co-workers from the University of Duisburg-Essen, Germany and the Univerity of Aveiro, Portugal have demonstrated that data science techniques are able to extract insights from fewer measurements.They found that the key features identified by density-based clustering and principal component analysis algorithms successfully captured the difference in magnetoelectric response between two materials.Broader application of these techniques could reduce the cost and difficulty of functional materials analysis.

 

Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images (通过深度学习原子分辨的图像以描绘电子束诱导相变过程中的介观相演化) 
Rama K. VasudevanNouamane LaanaitErik M. FerragutKai WangDavid B. GeoheganKai XiaoMaxim ZiatdinovStephen JesseOndrej Dyck & Sergei V. Kalinin
npj Computational Materials 4:30 (2018)
doi:s41524-018-0086-7
Published online:28 june 2018
Abstract| Full Text | PDF OPEN

摘要:了解电子束辐照下的相转变需要实时绘制相的结构及其演变。迄今为止,这主要通过手动操作来实现,需要繁琐的逐帧分析,枯燥乏味且极易出错。本研究采用深层卷积神经网络(DCNN)来自动确定原子分辨的图像中出现的布拉维晶格的对称性。我们训练了一个DCNN,使之可以从给定输入图像的2D快速傅立叶变换中识别出布拉维晶格的类别。进一步采用蒙特卡洛分析确定了预测概率,并且展示了基于模拟的及真实的扫描隧道显微镜和扫描透射电子显微镜的原子分辨图像得到的结果。最终图层输出的简化表示方法可以将DCNN中的分类可视化,其结果符合物理直觉。然后,我们将训练好的网络应用于研究WS2的电子束诱导的相转变,该网络可以跟踪和确定空洞的生长速率。我们强调了结果的两个关键方面:1)DCNNs可以被训练用来识别出衍射图案,这些图案与典型的“真实图像”情况明显不同;2)提供了一种内置不确定性量化的方法,可以对原子分辨图像中的相结构进行实时分析   

Abstract:Understanding transformations under electron beam irradiation requires mapping the structural phases and their evolution in real time. To date, this has mostly been a manual endeavor comprising difficult frame-by-frame analysis that is simultaneously tedious and prone to error. Here, we turn toward the use of deep convolutional neural networks (DCNN) to automatically determine the Bravais lattice symmetry present in atomically resolved images. A DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier transform of the input image. Monte-Carlo dropout is used for determining the prediction probability, and results are shown for both simulated and real atomically resolved images from scanning tunneling microscopy and scanning transmission electron microscopy. A reduced representation of the final layer output allows to visualize the separation of classes in the DCNN and agrees with physical intuition. We then apply the trained network to electron beam-induced transformations in WS2, which allows tracking and determination of growth rate of voids. We highlight two key aspects of these results: (1) it shows that DCNNs can be trained to recognize diffraction patterns, which is markedly different from the typical “real image” cases and (2) it provides a method with in-built uncertainty quantification, allowing the real-time analysis of phases present in atomically resolved images. 

Editorial Summary

Neural networks: Crystalline vision (神经网络:晶体探幽) 

从大量的电子显微数据集中寻找晶体类型颇耗时间;受计算机视觉的启发,神经网络可能有助于解决该问题。然而,神经网络的应用受到诸如晶体取向具有任意性等问题的限制。本研究中,美国橡树岭国家实验室的Rama Vasudevan和波多黎各、印度的科研人员一起,训练了一个神经网络,能从电子显微镜数据中识别2D晶格。进一步的蒙特卡罗分析还可以给出预测概率和统计偏差。该网络具有85%的预测正确率,二次预测正确率可达75%。应用该网络研究了电子轰击下的WS2结构,发现其三方相结构在轰击下消失。这种神经网络有望成为一种分析实时电子显微镜数据的强大工具,应用于材料的优化和设计。

Finding crystal type in large electron microscope data sets is time intensive; inspired by computer vision, a neural network may help. However, application is limited due to issues like arbitrary crystal orientation.Here, lead by Rama Vasudevan at Oak Ridge National Laboratory and colleagues in Puerto Rico, the US, and India, a team trained a neural network to recognize 2D crystal lattices from electron microscope data.Additional Monte Carlo analysis gives probabilistic prediction and statistical deviation. This network correctly predicts crystal lattice 85% of the time, with 75% of second predictions correct. Incorrect predictions have large deviations, providing a heuristic for edge cases. The network was used to investigate WS2 structure under electron bombardment, finding rhombohedral structure disappears. This could be a robust tool to analyse real-time electron microscope data for materials optimisation and design.

 

Machine learning modeling of superconducting critical temperature(超导转变温度的机器学习模型) 
Valentin StanevCorey OsesA. Gilad KusneEfrain RodriguezJohnpierre PaglioneStefano Curtarolo & Ichiro Takeuchi
npj Computational Materials 4:29 (2018)
doi:s41524-018-0085-8
Published online:28 june 2018
Abstract| Full Text | PDF OPEN

摘要:超导自一个多世纪以前被发现以来就一直是研究的焦点。目前,该现象的部分特征仍然很难理解,其中超导特性与材料的化学组分/结构性质之间的关系是一个主要的问题。为解决这一问题,本研究开发了几种机器学习方案,用于模拟SuperCon数据库中12,000多种已知超导体的超导转变温度(Tc)。首先根据材料的转变温度Tc是否高于10K这一标准将其分为两类,并训练一个分类模型来预测该标记。该模型采用基于化学组分的粗粒度特征,显示出强大的预测能力,其样本外准确度可达到92%。为预测铜基、铁基和低Tc化合物等材料转变温度分别建立回归模型。这些模型同样显示了较好的预测能力,同时通过学习获得的预测因子为探索不同材料体系的超导机理提供了线索。为提升这些模型的准确度和可解释性,采用AFLOW在线存储库中的材料数据在模型中加入了新的特征。最后,将分类和回归模型结合成一个集成管道,应用于无机晶体结构数据库中搜索潜在的新型超导体。我们发现了30多个非铜基、铁基氧化物为潜在的超导材料   

Abstract:Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database.Materials are first divided into two classes based on their Tcvalues, above and below 10?K, and a classification model predicting this label is trained.The model uses coarse-grained features based only on the chemical compositions.It shows strong predictive power, with out-of-sample accuracy of about 92%.Separate regression models are developed to predict the values of Tc for cuprate, iron-based, and low-Tc compounds.These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials.To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories.Finally, the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify >30 non-cuprate and non-iron-based oxides as candidate materials. 

Editorial Summary

Superconductivity: machine learning predicts superconducting transition temperature (超导:机器学习预测超导转变温度) 

该研究开发了机器学习方案准确地模拟了12,000多种化合物的超导转变温度。一个由马里兰大学帕克校区Valentin Stanev教授领导、杜克大学及美国国家标准局的研究人员参与的研究团队,开发了几种机器学习方案,对超过12,000种已知超导体和候选材料的超导转变温度(Tc)进行建模。他们首先基于化学成分训练了一个分类模型,以Tc是高于还是低于10 K为标准对已知超导体进行分类。然后,他们开发了回归模型来预测各种化合物的Tc值。这些模型的准确性通过借用AFLOW在线存储库中的材料数据得到了进一步提升。他们将分类和回归模型组合成一个集成管道,搜索了整个无机晶体结构数据库并预测出30多种新的潜在超导体。

Machine learning schemes are developed to model the superconducting transition temperature of over 12,000 compounds with good accuracy. A team led by Valentin Stanev from the University of Maryland at College Park and including researchers from Duke University and NIST develops several machine learning schemes to model the critical temperature (Tc) of over 12,000 known superconductors and candidate materials.They first train a classification model based only on the chemical compositions to categorize the known superconductors according to whether their Tc is above or below 10?K. Thenthey develop regression models to predict the values of Tc for various compounds.The accuracy of these models is further improved by including data from the AFLOW Online Repositories.They combine the classification and regression models into a single-integrated pipeline to search the entire Inorganic Crystallographic Structure Database and predict more than 30 new candidate superconductors.

Fine-grained optimization method for crystal structure prediction (晶体结构预测的细粒度优化方法) 
Kei TerayamaTomoki YamashitaTamio Oguchi & Koji Tsuda
npj Computational Materials 4:32 (2018)
doi:s41524-018-0090-y
Published online:10 july 2018
Abstract| Full Text | PDF OPEN

摘要:基于第一原理计算的晶体结构预测通常是通过对随机生成的初始结构进行弛豫来实现的。结构的弛豫需要多个优化步骤。对所有初始结构进行充分的弛豫是非常耗时的,但要事先确定哪一个初始结构会产生最优解是非常困难的。本研究提出了一种基于二次逼近(Quadratic Approximation)的晶体结构预测优化方法——超前预测法(Look Ahead),该方法对每个候选结构进行优化分配。此法允许我们用最少的局部优化步骤来识别最稳定的结构。我们使用已知系统Si、NaCl、Y2Co17、Al2O3和GaAs进行模拟。结果表明,与随机搜索相比,超前预测法可以显著降低计算成本。该方法可用于基于第一性原理计算的各种局部优化控制,在有限的计算资源下获得最佳结果   

Abstract:Crystal structure prediction based on first-principles calculations is often achieved by applying relaxation to randomly generated initial structures. Relaxing a structure requires multiple optimization steps.It is time consuming to fully relax all the initial structures, but it is difficult to figure out which initial structure leads to the optimal solution in advance.In this paper, we propose a optimization method for crystal structure prediction, called Look Ahead based on Quadratic Approximation, that optimally assigns optimization steps to each candidate structure.It allows us to identify the most stable structure with a minimum number of total local optimization steps.Our simulations using known systems Si, NaCl, Y2Co17, Al2O3, and GaAs showed that the computational cost can be reduced significantly compared to random search.This method can be applied for controlling all kinds of local optimizations based on first-principles calculations to obtain best results under restricted computational resources. 

Editorial Summary

Atomic structures: local optimization accelerates prediction(原子结构:局部优化加速了预测) 

加速预测原子晶体结构是预测新材料物理性质的基础。来自东京大学的Kei Terayama和Koji Tsuda教授等人设计了一种新的晶体结构预测和加速优化方法:按此方法先生成大量候选结构,依据其能量高低进行评分,最后优先对得分最低的结构进行局部优化。他们发现,与随机搜索方法相比,获得七个已知系统的晶体结构所需的步骤总数可减少20倍以上。这种基于控制局部优化步骤的晶体结构预测新方法也可以帮助我们识别新的分子。

Speeding up how to predict atomic crystal structures is fundamental to predicting a new material’s physical properties. A team led by Kei Terayama and Koji Tsuda from the University of Tokyo devised a new and accelerated optimization method for crystal structure prediction where a large number of candidate atomic structures are generated, scored according to their lowest energies, and finally the local optimization of the structures with the lowest score is prioritized. They found that the total number of steps necessary to obtain the crystal structures of seven known systems, depending on the system, can be reduced by more than twenty times compared to random searching methods.This new approach to crystal structure prediction based on controlling local optimization steps may also help us, for example, identify new molecules.

 

Computational design of bimetallic core-shell nanoparticles for hot-carrier photocatalysis(双金属核壳纳米粒子的热载流子光催化计算设计) 
Luigi RannoStefano Dal Forno & Johannes Lischner
npj Computational Materials 4:31 (2018)
doi:s41524-018-0088-5
Published online:06 july 2018
Abstract| Full Text | PDF OPEN

摘要:计算设计可以促进具有定制特性的新材料发现,但要将这种方法应用于直径大于几纳米的等离子体纳米颗粒,却很有挑战,因为原子级第一性原理计算不适用于这样的系统。本研究采用最近开发的材料特定方法进行计算,结合了电子有效质量理论和局部表面等离子体的准静态描述,可识别有望用于热电子光催化的双金属核-壳纳米颗粒。具体而言,我们计算了100种不同核壳纳米粒子的热载流子产生速率,发现具有碱金属核和过渡金属壳的材料系统,对水分子有强裂解性,且在水环境中有强稳定性。分析表明,这些系统的高效率与它们的电子结构有关,而电子结构在壳体中具有二维电子气。我们的计算还进一步证明了热载流子特性具有高度可调性,可用核心和壳体的尺寸进行灵敏的调控。从本研究得出的材料设计规律可用来指导改进太阳能转换装置   

Abstract:Computational design can accelerate the discovery of new materials with tailored properties, but applying this approach to plasmonic nanoparticles with diameters larger than a few nanometers is challenging as atomistic first-principles calculations are not feasible for such systems. In this paper, we employ a recently developed material-specific approach that combines effective mass theory for electrons with a quasistatic description of the localized surface plasmon to identify promising bimetallic core-shell nanoparticles for hot-electron photocatalysis.Specifically, we calculate hot-carrier generation rates of 100 different core-shell nanoparticles and find that systems with an alkali-metal core and a transition-metal shell exhibit high figures of merit for water splitting and are stable in aqueous environments.Our analysis reveals that the high efficiency of these systems is related to their electronic structure, which features a two-dimensional electron gas in the shell.Our calculations further demonstrate that hot-carrier properties are highly tunable and depend sensitively on core and shell sizes.The design rules resulting from our work can guide experimental progress towards improved solar energy conversion devices. 

Editorial Summary

Photocatalysis: hot-carrier generation in bimetallic core-shell nanoparticles (光催化:双金属核壳纳米粒子中的热载流子生成) 

对各种核-壳纳米粒子的计算筛选揭示,双金属系统是裂解水的理想选择。英国伦敦帝国理工学院的Luigi Ranno教授等进行了一项理论研究,将电子的有效质量理论与局部表面等离子体的准静态描述相结合。该方法可以计算来自数百个核-壳纳米颗粒的热载流子产生速率,并确定核-壳纳米颗粒是光催化应用的最佳候选物,能在水环境中保持稳定,满足了有效裂解水的关键要求。发现根据核和壳的尺寸在宽泛的范围内可对热载流子性质进行调节。值得注意的是,具有碱金属核和过渡金属壳的双金属纳米颗粒,在增强的热载流子产生速率和水分解能力方面,提供了最高的品质因数。

A computational screening of a variety of core-shell nanoparticles unveil that bimetallic systems are ideal for water splitting. A team led by Luigi Ranno at Imperial College London performed a theoretical study combining effective mass theory for electrons with a quasistatic description of localized surface plasmons.This approach allowed to compute the rates of hot-carrier generation from hundreds of core-shell nanoparticles, and to identify the optimal candidates for photocatalysis applications that are stable in aqueous environment, the latter being a crucial requirement for effective water splitting.The hot-carrier properties were found to be broadly tunable as a function of the core and shell sizes.Notably, bimetallic nanoparticles with an alkali-metal core and a transition-metal shell were found to provide the highest figures of merit in terms of enhanced hot-carrier generation rates and water splitting ability.

 

Learning local, quenched disorder in plasticity and other crackling noise phenomena (机器学习用于研究塑性及其他爆裂噪声现象中的局域淬火无序) 
Stefanos Papanikolaou
npj Computational Materials 4:27 (2018)
doi:10.1038/s41524-018-0083-x
Published online:07 june 2018
Abstract| Full Text | PDF OPEN

摘要:多体系统在远离平衡时会表现出强烈依赖于初始条件的行为。一个典型的例子是晶态及非晶态的塑性强烈依赖于材料的历史过程。在塑性模拟中,该历史过程可以由淬火、局域和无序的流变应力决定。尽管这种无序造成了纳米尺度塑性形变时常见的雪崩,但其泛函形式及标度性却并不清楚。本研究提出了一个普适的形式,用于从爆裂噪声模拟的外场响应(如,应力/应变)时序中获得局域无序的分布。本研究采用洄滞随机-场伊辛模型和弹性界面退钉扎模型(这两种模型曾被用来模拟晶态和非晶态塑性)验证了该方法的效率。我们发现,通过提高时间分辨率和增加样品数目,可以提升模拟淬火无序分布的精确度   

Abstract:When far from equilibrium, many-body systems display behavior that strongly depends on the initial conditions. A characteristic such example is the phenomenon of plasticity of crystalline and amorphous materials that strongly depends on the material history. In plasticity modeling, the history is captured by a quenched, local and disordered flow stress distribution.While it is this disorder that causes avalanches that are commonly observed during nanoscale plastic deformation, the functional form and scaling properties have remained elusive. In this paper, a generic formalism is developed for deriving local disorder distributions from field-response (e.g., stress/strain) timeseries in models of crackling noise.We demonstrate the efficiency of the method in the hysteretic random-field Ising model and also, models of elastic interface depinning that have been used to model crystalline and amorphous plasticity. We show that the capacity to resolve the quenched disorder distribution improves with the temporal resolution and number of samples. 

Editorial Summary

Stochastic yield: machine learning predicts disorder distributions(随机屈服:机器学习预测无序分布) 

机器学习可以从爆裂应力-应变曲线中估算纳米尺度的局域无序分布,即便其带有类似共存普适行为。美国西弗吉尼亚大学的Stefanos Papanikolaou教授将无监督机器学习方法与聚类算法相结合,以期从具有爆裂噪声随时间演化行为的应力-应变曲线中得到淬火局域的无序分布。他的方法在两种爆裂噪声模型中,成功实现了数据的聚类和分类,并从镍微柱单轴压缩实验的数据中成功得到了淬火无序的分布。将这些淬火无序分布的识别及分类扩展到不同材料、加载模式和样品加载历史中,有助于建立随机屈服分布的数据库,进而改进多尺度力学模型。

Machine learning can estimate nanoscale local disorder distributions from crackling stress–strain curves, even with similar coexisting universal behavior. Stefanos Papanikolaou at The West Virginia University in West Virginia used unsupervised machine learning coupled with clustering to derive locally quenched disorder distributions from stress-strain curves that exhibit crackling noise over time.This method was successful in clustering and classifying data in two different models of crackling noise as well as in deriving the quenched disorder distribution for the experimental uniaxial compression of nickel micropillars. Extending the identification and classification of these quenched distributions to different materials, loading modes, and sample loading histories may help produce a library of stochastic yield distributions that can improve multiscale mechanics models.

 

Ultra-low thermal conductivity of two-dimensional phononic crystals in the incoherent regime(二维声子晶体在非相干态下的超低热导率)
Guofeng XieZhifang JuKuikui ZhouXiaolin WeiZhixin GuoYongqing Cai & Gang Zhang
npj Computational Materials 4:21 (2018)
doi:10.1038/s41524-018-0076-9
Published online:16 April 2018
Abstract| Full Text | PDF OPEN

摘要:二维硅声子晶体因其可重复的低热导率和优良电性能,在热电应用领域引起了广泛的研究兴趣。工作在高温环境下的热电器件,相干声子的干涉效应受到强烈抑制,因此非相干的声子输运机制对于调控热导率是非常重要的。本研究在微扰理论的基础上,从纳米结构表面键序缺陷的角度出发,提出了一种新的声子散射过程。将这种频率强依赖性散射率纳入声子玻尔兹曼输运方程,理论计算重现了多孔硅纳米结构的超低热导率实验值。我们发现,热导率的显着降低,不仅源于经典边界散射对低频声子的阻碍,还源于表面键序缺陷散射对高频声子的严重抑制。我们的理论不仅揭示了多孔表面对声子输运的作用机制,还为表面工程调控纳米结构的热导率提供了计算工具   

Abstract:Two-dimensional silicon phononic crystals have attracted extensive research interest for thermoelectric applications due to their reproducible low thermal conductivity and sufficiently good electrical properties. For thermoelectric devices in high-temperature environment, the coherent phonon interference is strongly suppressed; therefore phonon transport in the incoherent regime is critically important for manipulating their thermal conductivity.On the basis of perturbation theory, we present herein a novel phonon scattering process from the perspective of bond order imperfections in the surface skin of nanostructures.We incorporate this strongly frequency-dependent scattering rate into the phonon Boltzmann transport equation and reproduce the ultra low thermal conductivity of holey silicon nanostructures.We reveal that the remarkable reduction of thermal conductivity originates not only from the impediment of low-frequency phonons by normal boundary scattering, but also from the severe suppression of high-frequency phonons by surface bond order imperfections scattering. Our theory not only reveals the role of the holey surface on the phonon transport, but also provide a computation tool for thermal conductivity modification in nanostructures through surface engineering. 

Editorial Summary

Phononic crystals: Surface scattering (声子晶体:表面散射) 

声子表面散射过程可以解释声子晶体的超低热导率。二维硅声子晶体因其孔的周期性排布能显着降低热导率,因而有望应用于热电领域。来自中国湖南科技大学、湘潭大学的谢国锋教授以及新加坡高性能计算研究所的张刚教授等,基于纳米结构表面的键序缺陷,引入声子散射机制为实验数据构建了模型。由于表面原子的键长变短键能变强,所以对晶格振动系统的势能产生了微扰,并抑制了高频声子。同时,低频声子被经典边界散射机制所抑制。作者认为是这两种机制导致了超低热导率,并预测可通过让声子晶体中的孔壁粗糙化来进一步降低热导率。二维硅声子晶体因其可重复的低热导率和优良电性能,在热电应用领域引起了广泛的兴趣,但其超低热导率机制尚不清楚。湖南科技大学、湘潭大学的谢国锋教授以及新加坡高性能计算研究所的张刚教授等,发现超低热导率既源于经典边界散射对低频声子的阻碍,又源于表面键序缺陷散射对高频声子的严重抑制。

A phonon scattering process on the surface of phononic crystals can explain their ultra-low thermal conductivity. Two-dimensional silicon phononic crystals are promising for thermoelectric applications, as the periodic arrangement of holes allows for significant reduction of their thermal conductivity. A team from Hunan University of Science and Technology and Xiangtan Universities in China, and the Institute of High Performance Computing in Singapore, manage to model the values reported experimentally by incorporating a phonon scattering mechanism, rooted to the bond imperfection on the surface of the nanostructure.As the bonds towards the surface grow shorter and therefore stronger, they perturb the local potential, and suppress the high-frequency phonons.Low-frequency phonons, on the other hand are suppressed by normal boundary scattering.The authors conclude that these two actions lead to the ultra-low thermal conductivity values, and predict that further reduction can be achieved by roughening the hole walls in the phononic crystal.

 

Statistical variances of diffusional properties from ab initio molecular dynamics simulations (从头算分子动力学模拟的扩散特性的统计方差) 
Xingfeng HeYizhou ZhuAlexander Epstein & Yifei Mo
npj Computational Materials 4:18 (2018)
doi:10.1038/s41524-018-0074-y
Published online:03 April 2018
Abstract| Full Text | PDF OPEN

摘要:从头算分子动力学(AIMD)模拟可以广泛用于研究材料的扩散机制和量化相应的材料扩散特性。然而,AIMD模拟通常局限在几百个原子的系统,且模拟时间也仅限于亚纳秒物理时域的范围,因此仅能涉及到有限的扩散几率,而无法描述其它扩散过程。这样致使AIMD模拟的扩散结果还往往受到统计误差的影响。本研究重新审视了通过AIMD模拟估算扩散系数和离子电导率的过程,并建立了具有最小拟合误差程序。此外,我们提出了相应的方法,能通过AIMD模拟到的扩散事目,来量化扩散系数和离子电导率的统计方差。由于扩散几率的采样必须达到足够的数目,因此AIMD模拟时间应足够长,而且只能在具有快速扩散的材料上进行研究。我们界定了应用AIMD模拟研究扩散特性所适用的材料范围和物理条件。本研究为量化AIMD模拟扩散结果的统计置信度以及正确应用这一强大技术奠定了基础   

Abstract:Ab initio molecular dynamics (AIMD) simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials. However, AIMD simulations are often limited to a few hundred atoms and a short, sub-nanosecond physical timescale, which leads to models that include only a limited number of diffusion events.As a result, the diffusional properties obtained from AIMD simulations are often plagued by poor statistics.In this paper, we re-examine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors.In addition, we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation.Since an adequate number of diffusion events must be sampled, AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion.We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties.Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique. 

Editorial Summary

Ionic diffusion: Mapping the uncertainty (离子扩散:描述不确定性) 

通过从头算分子动力学模拟计算离子扩散系数,往往受到统计误差的影响,且无法保证其结果的准确度。受限于目前的计算能力,该模拟方法只能为小型系统和短时尺度进行建模,这在一定程度上限制了扩散记录的采样数量。来自马里兰大学的莫一非教授等介绍了获得离子扩散系数的一种最佳方法,并给出了该方法下的统计误差。研究结果显示,扩散系数的线性行为只发生在模拟时间的中间时段,且扩散系数的方差与所有离子的总均方位移相关,随着位移的增加,统计误差逐渐降低。他们发现,精确的离子扩散计算只能用于超离子导体或高温下的离子扩散,而无法保证其它材料扩散系数的估算的可靠性。

The calculation of ionic diffusivity with ab initio molecular dynamics is often plagued by poor statistics; how accurate are the results? Due to computational limits only small systems and timescales can be modeled, limiting the number of diffusion events sampled.Here, Yifei Mo and colleagues at the University of Maryland outline best practice to obtain ionic diffusivity, as well as how to obtain statistical errors with this approach.They show linear behavior in diffusivity only happens for intermediate time intervals of the simulations.Moreover, variance of diffusivity is related to the total mean squared displacement of all ions, the statistical error reducing as the displacement increases. Accurate ionic diffusion calculations can only be performed for super-ionic conductors, or at high temperature, with implications for the reliability of calculations of diffusivity in other materials.

 

Spatial correlation of elastic heterogeneity tunes the deformation behavior of metallic glasses (弹性非均匀性的空间相关性对金属玻璃变形行为的调节) 
Neng WangJun DingFeng YanMark AstaRobert O. Ritchie &Lin Li
npj Computational Materials 4:19 (2018)
doi:10.1038/s41524-018-0077-8
Published online:06 April 2018
Abstract| Full Text | PDF OPEN

摘要:金属玻璃(MG)具有很高的强度,然而剪切带的出现极大降低了该类材料的拉伸塑性。本研究旨在提高MG内在异质性,以促进分布式流动,从而为改善单片MG的延展性提供新的可能。本文研究了非均质无定型构型引起的弹性各向异性对MG形变性能的影响,并利用多尺度模拟方法对体系延展性做了相关研究。通过原子尺度的计算模型发现,Cu64Zr36 MG在纳米尺度下呈现出了高度异质的高斯型剪切模量分布,而其中局部的柔性区域很大程度伴随着非弹性剪切形变的出现。对剪切变形区进行介观尺度的动力学模型计算发现,纳米尺度剪切形变转化为剪切带形变由局域剪切模量的空间异质性决定。为获得最佳拉伸塑性,作者定义了弹性异质性的临界空间相关长度。该临界长度与剪切带形成的转变节点相关,该临界值可从应力诱导的成核生长、结构决定的应变渗透与塑性流动的弹性软点饱和度等性能进行表征。这一发现对于深化理解空间异质性与MG形变过程的关系具有重要意义。本方法可通过调控材料本征的异质性来增强高强度金属合金的拉伸塑性,从而促进新型延性单片MG的设计和开发   

Abstract:Metallic glasses (MGs) possess remarkably high strength but often display only minimal tensile ductility due to the formation of catastrophic shear bands. Purposely enhancing the inherent heterogeneity to promote distributed flow offers new possibilities in improving the ductility of monolithic MGs.Here, we report the effect of the spatial heterogeneity of elasticity, resulting from the inherently inhomogeneous amorphous structures, on the deformation behavior of MGs, specifically focusing on the ductility using multiscale modeling methods.A highly heterogeneous, Gaussian-type shear modulus distribution at the nanoscale is revealed by atomistic simulations in Cu64Zr36 MGs, in which the soft population of the distribution exhibits a marked propensity to undergo the inelastic shear transformation. By employing a mesoscale shear transformation zone dynamics model, we find that the organization of such nanometer-scale shear transformation events into shear-band patterns is dependent on the spatial heterogeneity of the local shear moduli.A critical spatial correlation length of elastic heterogeneity is identified for the simulated MGs to achieve the best tensile ductility, which is associated with a transition of shear-band formation mechanisms, from stress-dictated nucleation and growth to structure-dictated strain percolation, as well as a saturation of elastically soft sites participating in the plastic flow. This discovery is important for the fundamental understanding of the role of spatial heterogeneity in influencing the deformation behavior of MGs. We believe that this can facilitate the design and development of new ductile monolithic MGs by a process of tuning the inherent heterogeneity to achieve enhanced ductility in these high-strength metallic alloys. 

Editorial Summary

Metallic Glasses: The role of soft spots (金属玻璃:软点的作用) 

该研究模拟揭示了弹性软点与金属玻璃形变过程之间的关系。无定形材料通常具有低的拉伸塑性和抗疲劳强度,即该类材料通常具有弱应变活化剪切带,且会导致材料迅速失效。目前,来自阿拉巴马大学、劳伦斯伯克利国家实验室和加利福尼亚大学的一个研究小组计算研究了金属玻璃(特别是Cu64Zr36)本征的空间异质性与剪切带形成之间的关系。随着材料异质性之间空间关联性的增强,以及弹性软点和硬点区域的扩大,作者们观察到在材料软点处,软点数量的降低削弱了材料延展性,且应力诱导的成核生长和剪切带增长开始向应变渗透转变。作者发现,协同调控软点数量和软点空间分布可以帮助改善金属玻璃的延展性。

Simulations shed light on the connection between elastically soft spots and the deformation of metallic glasses.Amorphous materials generally suffer from low ductility and fatigue strength: relatively low strain activates shear bands that quickly lead to failure. Now a team from the University of Alabama, Lawrence Berkeley National Laboratory and the University of California, investigate computationally the connection between the intrinsic heterogeneity of metallic glasses (and in particular Cu64Zr36) and the formation of shear bands. As the spatial correlation of the heterogeneity increases, and the regions of elastically soft and hard spots expand, the authors observe a transition from stress-driven nucleation and growth of the shear bands (resulting in increased ductility) to strain percolation, initiated in the soft spots (leading to reduced ductility as the number of soft spots has decreased). Combined with ways to control the number and distribution of soft spots, this insight could help improve the ductility of metallic glasses.

 

Improved phase field model of dislocation intersections (位错相交的改进相场模型) 
Songlin ZhengDongchang Zheng,Yong Ni & Linghui He
npj Computational Materials 4:20 (2018)
doi:10.1038/s41524-018-0075-x
Published online:11 April 2018
Abstract| Full Text | PDF OPEN

摘要:本研究揭示相交位错之间的长程弹性相互作用和短程芯结构反应,对于理解晶态固体中基于位错的应变硬化机制来说至关重要。相场模型通过使用连续统细观弹性理论来描述弹性相互作用,并将γ-表面结合到晶体能中,以使位错芯结构反应成为可能,因而在位错动力学建模中显示出巨大的潜力。由于在以前的相场模型中,晶体能近似表示为各滑移平面的层间势的线性叠加,所以它不能完全考虑滑移相交时滑移位错之间的反应。在本研究中,通过耦合两个相交滑移面的层间势来更新晶体能,提出了一种改进的模拟位错相交的相场模型,将其应用于共线位错相互作用研究,并与之前采用离散位错动力学模拟的结果进行比较。结果发现,仅改进的相场模型可以描述共线湮灭,并发现共线湮灭强烈地影响了多滑移系统中的位错锁结构形成和塑性流动。研究结果显示当前的改进对相场模拟位错相交是必要的   

Abstract:Revealing the long-range elastic interaction and short-range core reaction between intersecting dislocations is crucial to the understanding of dislocation-based strain hardening mechanisms in crystalline solids.Phase field model has shown great potential in modeling dislocation dynamics by both employing the continuum microelasticity theory to describe the elastic interactions and incorporating the γ-surface into the crystalline energy to enable the core reactions. Since the crystalline energy is approximately formulated by linear superposition of interplanar potential of each slip plane in the previous phase field model, it does not fully account for the reactions between dislocations gliding in intersecting slip planes.In this study, an improved phase field model of dislocation intersections is proposed through updating the crystalline energy by coupling the potential of two intersecting planes, and then applied to study the collinear interaction followed by comparison with the previous simulation result using discrete dislocation dynamics.Collinear annihilation captured only in the improved phase field model is found to strongly affect the junction formation and plastic flow in multislip systems. The results indicate that the improvement is essential for phase field model of dislocation intersections. 

Editorial Summary

Phase field: Predicting dislocation interactions (相场:预测位错相互作用) 

改进的相场建模方法可以更好地预测金属晶体的位错相互作用和塑性流动。中国科学技术大学倪勇教授团队对相场模拟中的晶体能量项作了改进,以便在立方金属中滑移面相交时能考虑滑移位错间相互作用。在新的相场模拟中,晶体能修改后成功地描述了应变硬化中最强的位错相互作用:共线相互作用过程中的位错湮灭现象。这种位错湮灭只有在改进的相场模型中被预测到。看来,对相场建模相关的能量项所作的改进,可以帮助我们更好地理解位错的行为并预测材料变形。

An improved phase field modeling approach can better predict dislocation interactions and plastic flow in metallic crystals. A team led by Young Ni at the University of Science and Technology of China in Hefei, China modified the crystalline energy term in phase field simulations to take into account interactions between dislocations gliding in intersecting slip planes in cubic metals.In simulations, the modified crystalline term successfully led to the annihilation of dislocations for the strongest type of interactions contributing to strain hardening, collinear interactions.This dislocation annihilation was only seen with the improved phase field model and not the conventional phase field model. Improvements in the expression of the energies associated with phase field modeling may help us better understand dislocation behavior and predict material deformation.

 

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