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  《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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Uncovering electron scattering mechanisms in NiFeCoCrMn derived concentrated solid solution and high entropy alloys (揭示NiFeCoCrMn衍生浓缩固溶体和高熵合金中的电子散射机制)
Sai Mu, German D. Samolyuk, Sebastian Wimmer, Maria C. Troparevsky, Suffian N. Khan, Sergiy Mankovsky, Hubert Ebert & George M. Stocks 
npj Computational Materials 5:1 (2019)
doi:s41524-018-0138-z
Published online:04 January 2019
Abstract| Full Text | PDF OPEN

摘要:虽然人们早就知道位错严重影响输运性能,但最近对一系列固溶体3d-过渡金属合金的测量结果显示,残余电阻率有两个数量级的差异。使用从头算方法,我们证明,虽然所有合金的载流子密度都与普通金属一样高,但电子平均自由程可以从~10 A(强散射极限)到~103 A(弱散射极限)变化。本研究描绘了导致这种不同行为的潜在电子散射机制。虽然位点对角线、自旋、潜在散射总是占据主导地位,但对于仅含FeCoNi的合金,多数自旋通道经历的无序散射可以忽略,从而提供了短路,而对于含Cr Mn的合金,两者都具有自旋通道,由于电子填充效应而经历了强无序散射。有些令人惊讶的是,其他一些散射机制——包括位移效应或尺寸效应,散射已被证明与诸如屈服强度之类的各种性质有强相关性——现在却被发现在大多数情况下都是相对较弱的相关性   

Abstract:Whilst it has long been known that disorder profoundly affects transport properties, recent measurements on a series of solid solution 3d-transition metal alloys reveal two orders of magnitude variations in the residual resistivity. Using ab initio methods, we demonstrate that, while the carrier density of all alloys is as high as in normal metals, the electron mean-free-path can vary from ~10A (strong scattering limit) to ~103A (weak scattering limit). Here, we delineate the underlying electron scattering mechanisms responsible for this disparate behavior. While site-diagonal, spin dependent, potential scattering is always dominant, for alloys containing only Fe, Co, and Ni the majority-spin channel experiences negligible disorder scattering, thereby providing a short circuit, while for Cr/Mn containing alloys both spin channels experience strong disorder scattering due to an electron filling effect. Somewhat surprisingly, other scattering mechanisms—including displacement, or size effect, scattering which has been shown to strongly correlate with such diverse properties as yield strength—are found to be relatively weak in most cases. 

Editorial Summary

Disordered alloys: Resistivity by smearing 无序合金:通过涂抹的电阻率 

该研究用高熵合金中特定合金元素引起的费米表面涂抹,解释了不同电阻率的测量。由美国橡树岭国家实验室的George Malcom Stocks领导的团队,使用从头算方法研究了Cantor-Wu族面心立方无序合金残余电阻率差异背后的电子散射机制。模拟首先再现了实验观察到的结果,即含有锰和铬的合金具有高的残余电阻率,而所有其他Cantor-Wu合金都只有低的残余电阻率。单位点电子散射,结合磁性引起的散射,说明这一现象是由于锰和铬引起费米表面因半填充的d带而导致的涂抹。深入理解无序合金中的电子传输,将有助于阐明它们更有特色的性质

Smearing of Fermi surfaces caused by specific alloying elements in high-entropy alloys explains disparate resistivity measurements. A team led by George Malcom Stocks at Oak Ridge National Laboratories in Tennessee, USA, used ab initio methods to investigate electron scattering mechanisms behind differences in residual resistivity of the Cantor-Wu family of face-centered cubic disordered alloys. The simulations first reproduced the experimental observation that alloys containing manganese and chromium had high residual resistivities, while all other Cantor-Wu alloys had low residual resistivities. Single-site electron scattering, in combination with scattering caused by magnetism, showed that this was due to manganese and chromium causing smearing of the Fermi surfaces due to their half-filled d-bands. Better understanding of electronic transport in disordered alloys may help elucidate their more exotic properties.

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强度高、延展性好、断裂韧性大、抗疲劳性强、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Pd和Cu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

纳米孪晶材料:软化温度下的塑性转变 

该研究证明了纳米孪晶材料存在一个软化温度,温度低于软化温度时材料随着孪晶厚度的减小而持续硬化,而温度高于软化温度时,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。来自美国布朗大学的高华健教授领导的团队,使用分子动力学对多晶纳米孪晶钯和纳米孪晶铜样品进行了模拟,并建立了不受分子动力学对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下,变形受孪晶界的迁移控制,这些孪晶界与在孪晶界-晶界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软化温度的位错源的数量,但相同的成核过程在低于软化温度时,则会受到孪晶界-晶界交叉点局部应力集中的限制,其峰值应力水平随着孪晶界间距的减小而减小,导致连续硬化。因此,软化温度软化温度划分了从位错源数-控制(源-控)向位错应力值-控制(应力-控)的孪晶界迁移转变。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孪晶材料

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.

The role of decomposition reactions in assessing first-principles predictions of solid stability(分解反应在评估第一性原理预测固体稳定性中的作用)
Christopher J. Bartel, Alan W. Weimer,Stephan Lany, Charles B. Musgrave & Aaron M. Holder 
npj Computational Materials 5:4 (2019)
doi:s41524-018-0143-2
Published online:04 January 2019
Abstract| Full Text | PDF OPEN

摘要:用密度泛函理论近似预测材料热力学性质时,其计算的可靠性通常采用对比计算的形成焓ΔHf与实测结果的差值来评估。然而,一种化合物会同时与其它化合物和构成元素的单质相产生热力学竞争,因此,这些竞争相的分解反应焓(ΔHd)决定了材料的热力学稳定性。本研究分析了56,791种化合物的相图,并将分解反应分为三类:1)仅生成单质相,2)仅生成化合物,3)既生成单质相又生成化合物。分析表明,分解成单质相的反应基本不是决定化合物稳定性的竞争反应,并且有约2/3的分解反应不涉及单质相。通过与实验所测的1012种固体化合物的形成焓相比,我们评估了广义梯度近似(GGAPBE)和meta-GGA密度泛函(SCAN)用以预测化合物稳定性的准确性。对于其中646种分解反应来说,与生成反应不同的是,PBE(理论和实验的平均绝对差(MAD= 70 meV /原子)和SCANMAD = 59 meV /原子)的表现相似,常用的利用元素参考能量进行校正的方案对生成反应的改善微不足道(~2 meV/atom)。此外,对于仅涉及生成化合物的231种分解反应(类型2),SCANPBE与实测结果之间的一致性在~35 meV /原子内,与实验不确定性的大小相当   

Abstract:The performance of density functional theory approximations for predicting materials thermodynamics is typically assessed by comparing calculated and experimentally determined enthalpies of formation from elemental phases, ΔHf. However, a compound competes thermodynamically with both other compounds and their constituent elemental forms, and thus, the enthalpies of the decomposition reactions to these competing phases, ΔHd, determine thermodynamic stability. We evaluated the phase diagrams for 56,791 compounds to classify decomposition reactions into three types: 1. those that produce elemental phases, 2. those that produce compounds, and 3. those that produce both. This analysis shows that the decomposition into elemental forms is rarely the competing reaction that determines compound stability and that approximately two-thirds of decomposition reactions involve no elemental phases. Using experimentally reported formation enthalpies for 1012 solid compounds, we assess the accuracy of the generalized gradient approximation (GGA) (PBE) and meta-GGA (SCAN) density functionals for predicting compound stability. For 646 decomposition reactions that are not trivially the formation reaction, PBE (mean absolute difference between theory and experiment (MAD)=70meV/atom) and SCAN (MAD=59meV/atom) perform similarly, and commonly employed correction schemes using fitted elemental reference energies make only a negligible improvement (~2 meV/atom). Furthermore, for 231 reactions involving only compounds (Type 2), the agreement between SCAN, PBE, and experiment is within ~35meV/atom and is thus comparable to the magnitude of experimental uncertainty. 

Editorial Summary

DFT预测:固体稳定性 

该研究发现,用高通量DFT方法计算化合物分解焓ΔHd可预测化合物的稳定性。来自美国科罗拉多大学的Charles B. MusgraveAaron M. Holder领导的团队,分析了Materials Project数据库中约56,000种化合物相图,将分解反应分为三种类型:1)仅生成单质相,2)仅生成化合物,3)既生成单质相又生成化合物,并对每种反应类型作了量化。他们发现ΔHf只在极少数情况下(数据库中仅约3%的化合物)才成为稳定性预测所需的定量参数,但ΔHd却是稳定性预测最相关的量化参数。他们将广义梯度近似(GGAPBE)和meta-GGASCAN密度函数预测与实测的ΔHdΔHf进行基准比较,发现在定性和定量上都存在差异。对于仅生成化合物的231种分解反应(类型2),SCANPBE计算数据与实测数据(~35 meV / atom)保持相当好的一致性,实验结果和理论结果之间的差异较小。这是因为,使用PBESCAN等预测ΔHf时对化学组成高度敏感,但预测反应类型2ΔHd时,对化学组成却不敏感,变化很小。他们的研究表明,由于反应类型2的分解反应在确定固体稳定性方面起主要作用,因此用高通量DFT方法所作的稳定性预测,通常与多种材料的实验结果非常一致。

High-throughput DFT method can predict the thermodynamics stability of compounds by calculating decomposition energy ΔHd. A team led by Charles B. Musgrave and Aaron M. Holder from the University of Colorado, analyzed the phase diagram of about 56,000 compounds in the Materials Project, and divided decomposition reactions into three types: 1) generating elemental phases, 2) generating compounds, 3) generating both elemental phases and compounds, and quantified the prevalence of these reaction types. They found that ΔHf is a quantitative parameter for stability prediction in rare cases (only about 3% of the compounds in the database), but ΔHd is the most relevant one for stability prediction. They compared the generalized gradient approximation (GGA, PBE) and meta-GGA (SCAN) density functional predictions with the measured ΔHd and ΔHf benchmarks, and found differences in both qualitative and quantitative. For the 231 decomposition reactions of type 2, the agreement between SCAN, PBE and experimental ΔHd data (~35 meV / atom) is comparable to the expected noise in the measured data. The difference between the experimental and the theoretical results is that ΔHd is systematically lower than ΔHf, due to PBE and SCAN for ΔHf sensitive to chemical composition, but for ΔHd of type 2 reactions varies minimally. Since this type of decomposition reactions is predominant in determining solid stability, the high-throughput DFT approaches to stability predictions are generally in excellent agreement with experiment for a diverse set of materials.

Manifold learning of four-dimensional scanning transmission electron microscopy (四维扫描透射电子显微镜的多维学习)
Xin Li, Ondrej E. Dyck, Mark P. Oxley, Andrew R. Lupini, Leland McInnes, John Healy, Stephen Jesse & Sergei V. Kalinin 
npj Computational Materials 5:5 (2019)
doi:s41524-018-0139-y
Published online:07 January 2019
Abstract| Full Text | PDF OPEN

摘要:显示局部原子衍射图的四维扫描透射电子显微镜(4D-STEM)正在成为探测原子结构和原子电场复杂细节的有力技术。然而,大量数据的有效处理和解释仍然具有挑战性,特别是对于二维或轻质材料,因为记录在像素化阵列上的衍射信号很弱。本研究采用数据驱动的多维学习方法,对4D-STEM数据集进行直观的可视化和探索分析,从像素化探测器上记录的具有单一掺杂原子的单层石墨烯中提取原子分辨偏转模式的实空间相邻效应。这些提取的图案涉及单个原子位置和子晶格结构,通过多模式视图有效地区分单个掺杂剂的异常。我们相信使用多维学习分析将加速物理学新发现,这些新发现会将铁电、拓扑自旋和范德华异质结等材料与数据丰富的成像机制联系起来   

Abstract:Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold learning approaches for straightforward visualization and exploration analysis of 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin, and van der Waals heterostructures.

Editorial Summary

Artificial intelligence: getting more from microscopy images (人工智能:从显微图像中获取更多信息) 

自动算法可以提取隐藏在高分辨率显微镜图像中的材料原子细节信息。由美国橡树岭国家实验室的Sergei V. Kalinin(本刊副主编)领导的国际团队,使用计算机协议进行数据分析(他们将该方法称为多维学习, manifoold learning),通过电子束照射石墨烯的超薄层来收集大量图像中的周期性特征。然后,计算机自动将这些特征与原子相对位置有关的信息链接。该技术不仅可用于识别可能引起异常光电或磁性能的材料局部结构变化,而且还可用来探索生成这些图像的实验条件如何进一步改善,以从高分辨显微技术中获得最多信息

Automatic algorithms can extract information on the atomic details of materials hidden in high-resolution microscopy images. An international team led by Sergei V. Kalinin at the Oak Ridge National Laboratory, USA, use a computer protocol for data analysis, called manifold learning, to find recurrent features in a large set of images collected by illuminating an ultrathin layer of graphene with a beam of electrons. Then, the computer automatically linked such features to information related to the relative position of the atoms. This technique may be used not only to identify local changes in the material structure that may be responsible for unusual optoelectronic or magnetic properties, but also to understand how the experimental conditions used to generate these images can be further improved to obtain the most from high-resolution microscopy techniques.

Active learning for accelerated design of layered materials (主动学习加速层状材料的设计)
Lindsay BassmanPankaj RajakRajiv K. KaliaAiichiro NakanoFei ShaJifeng SunDavid J. SinghMuratahan AykolPatrick HuckKristin Persson & Priya Vashishta 
npj Computational Materials 4:74 (2018)
doi:s41524-018-0129-0
Published online:10 December 2018
Abstract| Full Text | PDF OPEN

摘要:由过渡金属二硫属化合物单层垂直堆叠而成的异质结在光电和热电器件领域拥有巨大的应用潜力。要发现用于特定领域的最优层状材料,需要先估算关键的材料特性,例如电子能带结构和热输运系数。然而,通过严格从头计算方法搜索整个材料结构空间来筛选材料特性,大大超过了目前计算资源的限制。此外,材料特性函数对其结构的依赖性通常很复杂,在没有收集大量数据的情况下,难以使用简单的统计程序开展预测。本研究提出了一个高斯过程回归模型,可基于异质结结构预测材料属性,同时提出了基于贝叶斯优化的主动学习模型,可基于最少的从头算工作量来有效地发现最佳异质结。我们选取电子带隙、导带/价带色散关系和热电性能作为代表性的材料特性开展预测和优化。采用Materials Project平台计算电子结构,BoltzTraP程序用于计算热电性能。与构建回归模型相比,采用贝叶斯优化预测最优材料结构可以显著降低计算成本。本研究开发的模型可用于预测任意的材料性质,并且开发的软件(基于Python材料基因组学(PyMatGen)数据库的数据准备程序以及python机器学习程序)都是开源的   

Abstract:Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source. 

Editorial Summary

Materials design: Bayesian optimization (材料设计:贝叶斯优化) 

使用贝叶斯优化(BO)可以高精度的预测材料性能。南加州大学的Priya Vashishta领导的团队,开发了一种高斯回归模型,能够预测过渡金属二硫属化合物单层堆叠构成的三层范德华异质结的带隙值和热电性质。进一步,采用BO模型可以基于最少的从头计算数据量识别最佳异质结。他们采用BO模型计算找到了与光电和热电应用相关的最大带隙异质结或非常接近1.1 eV带隙值的异质结。发现BO识别近乎最优材料组合的概率很高,并能显着降低使用回归模型发现理想结构的计算成本

High accuracy predictions of materials properties can be obtained using Bayesian optimization (BO). A team led by Priya Vashishta at University of Southern California developed a Gaussian regression model capable of predicting the band gap value and thermoelectric properties of three-layered van der Waals heterostructures of transition metal dichalcogenides. A BO model further allowed identification of optimal heterostructures using a minimal number of ab initio calculations. BO models were computed to find either heterostructures with maximum band gap or heterostructures with a band gap value closest to 1.1?eV, relevant for optoelectronic and thermoelectric applications. BO was found to identify nearly optimal materials configurations with high probability, whilst significantly reducing the computational cost of discovering ideal structures using regression models.

Empirical modeling of dopability in diamond-like semiconductors (类金刚石半导体掺杂性能的经验模型)
Samuel A. MillerMaxwell DyllaShashwat AnandKiarash GordizG. Jeffrey Snyder & Eric S. Toberer 
npj Computational Materials 4:71 (2018)
doi:s41524-018-0123-6
Published online:06 December 2018
Abstract| Full Text | PDF OPEN

摘要:载流子浓度的优化在新型半导体的开发应用中(应用于诸如热电、透明导体和光伏等)一直是个挑战。这个问题在高通量的材料性能预测中尤其严重,由于计算量巨大,载流子浓度通常只能被假定为自由参数,其掺杂极限无法预测。本研究探索了机器学习在高通量预测载流子浓度方面的应用。我们将模型限定在类金刚石半导体材料体系中,基于从一元到四元共计127种化合物载流子浓度的实验数据,开发了机器学习数据集。采用各种统计和机器学习方法对这些数据进行分析。进而准确预测了类金刚石半导体材料的掺杂性能,预测的载流子浓度,不论对于p型还是n型,与实验值偏差都在一个数量级以内。通过分析拟合的模型,我们揭示了载流子浓度变化的趋势,并且与之前的计算工作进行了比较。最后,我们将该模型掺杂性能预测与高通量的品质因子预测相结合,预测了新型的热电材料   

Abstract:Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both p- and n-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Finally, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials. 

Editorial Summary

THERMOELECTRICS: Looking back is looking forward (热电材料:回首过去即是展望未来) 

实验测量的载流子浓度是理解和预测高性能热电材料的模型基础。载流子浓度对于控制材料性能十分重要。尽管实验已经取得了极大的进展,但建立掺杂性能的预测准则来实现材料性能设计仍是一种挑战。来自美国西北大学、科罗拉多矿业学院和美国国家可再生能源实验室的研究团队,根据实验报道的127种化合物的掺杂限值数据,预测了部分类金刚石半导体的掺杂范围,并筛选出了了几种兼具较高热电品质因子和较好掺杂性能的材料。该模型不仅阐明了类金刚石材料体系掺杂性能的决定因素,还预测了部分潜在的高性能热电化合物,这些材料值得后续研究关注

Experimental carrier concentration can serve as the basis for a model to understand and predict high performance thermoelectrics. Carrier concentration is instrumental in controlling properties. Despite significant experimental progress, establishing guidelines towards the desired performance through doping remains challenging. Now, a team from Northwestern University, Colorado School of Mines, and National Renewable Energy Laboratory in USA have predicted the dopability ranges of several diamond-like semiconductors, based on data from experimentally reported doping limits for 127 compounds. Several materials that combine simultaneously promising thermoelectric quality factor and complementary dopability are singled out. Apart from shedding light on what drives dopability in this family, the model also suggests that a number of less-studied compounds deserve more attention.

Large scale hybrid Monte Carlo simulations for structure and property prediction (大规模杂化蒙特卡洛模拟预测材料的结构和性质)
Sergei ProkhorenkoKruz KalkeYousra Nahas & Laurent Bellaiche 
npj Computational Materials 4:80 (2018)
doi:s41524-018-0137-0
Published online:21 December 2018
Abstract| Full Text | PDF OPEN

摘要:蒙特卡洛方法是现代计算物理学中最早、应用最广泛的算法之一。在凝聚态物理中,这种技术最受欢迎的是Metropolis蒙卡方法。虽然Metropolis抽样方法具有很强的鲁棒性和可操作性,但它并不适用于能量和力的计算。为了寻找一种更有效的计算方法,本研究探索了杂化蒙特卡洛采样方法(一种广泛用于量子电动力学的算法)在长程相互作用系统的结构预测方案方面的能力。我们的研究结果表明,杂化蒙特卡洛算法是一种优秀的计算方案,其不仅显著优于Metropolis抽样方法,而且可以弥补材料科学应用中的分子动力学,同时允许对包含数百万个粒子的系统进行超大规模模拟计算   

Abstract:The Monte Carlo method is one of the first and most widely used algorithms in modern computational physics. In condensed matter physics, the particularly popular flavor of this technique is the Metropolis Monte Carlo scheme. While being incredibly robust and easy to implement, the Metropolis sampling is not well-suited for situations where energy and force evaluations are computationally demanding. In search for a more efficient technique, we here explore the performance of Hybrid Monte Carlo sampling, an algorithm widely used in quantum electrodynamics, as a structure prediction scheme for systems with long-range interactions. Our results show that the Hybrid Monte Carlo algorithm stands out as an excellent computational scheme that can not only significantly outperform the Metropolis sampling but also complement molecular dynamics in materials science applications, while allowing ultra-large-scale simulations of systems containing millions of particles. 

Editorial Summary

Monte Carlo simulations: scaling-up property prediction (蒙特卡洛模拟:材料性能的大规模预测) 

该研究采用杂化蒙特卡洛抽样算法对数百万粒子的大规模体系进行结构搜索和性能预测。来自美国阿肯色大学的Laurent Bellaiche团队,在有限温度下,对具有长程相互作用的固态系统(如铁电、弛豫铁电体和多铁材料)的有效哈密顿模型进行了杂化蒙特卡洛(HMC)采样。他们将结果与Metropolis蒙卡算法(MMC)和热分子动力学(MD)的结果进行了比较。他们发现,在选定的模型案例中,HMC方案明显优于MMC和MD。通过对面向GPU的并行化架构实现的HMC算法,可以对粒子数达到106的体系进行大规模的HMC仿真。该算法也可用于大规模密度泛函理论计算,从而开辟更广阔的应用前景

A hybrid Monte Carlo sampling algorithm is adopted to predict structures and properties in large-scale simulations with millions of particles. A team led by Laurent Bellaiche from the University of Arkansas perform hybrid Monte Carlo (HMC) sampling on effective Hamiltonian models of solid-state systems with long-range interactions, such as ferroelectric, relaxor and multiferroic materials at finite temperatures. They compare the results with those obtained by the Metropolis Monte Carlo (MMC) algorithm and thermalized molecular dynamics (MD). They find that the HMC scheme significantly outperforms MMC and MD for selected model cases. By implementing the HMC algorithm for GPU-oriented parallelization architectures, they can perform HMC simulations for a large scale material simulations with the particle number reaching 106. This algorithm may also be implemented for large-scale density functional theory calculations so that a more broad space of applications might open.

Unexpectedly large energy variations from dopant interactions in ferroelectric HfO2from high-throughput ab initio calculations (高通量从头算预测HfO2铁电体掺杂剂相互作用的意外特大能量变化)
Max FalkowskiChristopher KünnethRobin Materlik & Alfred Kersch 
npj Computational Materials 4:73 (2018)
doi:s41524-018-0133-4
Published online:10 December 2018
Abstract| Full Text | PDF OPEN

摘要:了解过程相关属性(如小规模不均匀性)的起源是材料优化的关键。本研究使用DFT计算分析了随机掺杂Si、La和VO对HfO2结构的影响,并将其与生产过程相联系。用粗粒度方法比较了在局部不均匀性的影响下,相关铁电Pbc21相的总能与竞争的晶体相总能进行了比较。掺杂剂之间的相互作用增加了掺杂剂随机定位的统计效应。在原子层或化学溶液沉积后的退火过程中,由于掺杂剂不会扩散,与陶瓷工艺回火相比,原子层或化学溶液沉积后的退火过程相对较短,但仍然存在较大的能量变化。由于能量差异是相稳定性的判据,这种大的变化表明存在纳米区和弥散相变的可能性,因为这些局部掺杂效应可能使系统在顺电-铁电相界上来回移动   

Abstract:Insight into the origin of process-related properties like small-scale inhomogeneities is key for material optimization. Here, we analyze DFT calculations of randomly doped HfO2 structures with Si, La, and VO and relate them to the kind of production process. Total energies of the relevant ferroelectric Pbc21 phase are compared with the competing crystallographic phases under the influence of the arising local inhomogeneities in a coarse-grained approach. The interaction among dopants adds to the statistical effect from the random positioning of the dopants. In anneals after atomic layer or chemical solution deposition processes, which are short compared to ceramic process tempering, the large energy variations remain because the dopants do not diffuse. Since the energy difference is the criterion for the phase stability, the large variation suggests the possibility of nanoregions and diffuse phase transitions because these local doping effects may move the system over the paraelectric-ferroelectric phase boundary. 

Editorial Summary

Ferroelectrics: Dopant interactions stabilize nanoscale phases (铁电体:掺杂剂之间的相互作用稳定纳米级相) 

该研究对掺杂的HfO2进行了大尺度的密度泛函理论计算,并发现掺杂剂-掺杂剂之间的相互作用可以稳定纳米相。来自德国慕尼黑应用科学大学的Max Falkowski、Alfred Kersch和他们的同事对HfO2使用La或/和Si进行掺杂,其超结构具有1纳米的尺寸,他们对这些结构进行了高通量DFT计算。他们发现掺杂剂之间的相互作用范围在1 nm范围内,这与铁电相相对于介电相的稳定性有关。由于掺杂剂的相互作用,计算出的各结构相之间的能量变化出乎意料地大。结果表明,在这种材料中形成了纳米金属氧化物和纳米分子效应,这对于理解新近的实验发现非常重要,例如居里温度变宽、相间边界和弥散相变等

Large-scale density functional theory calculations (DFT) are performed on doped HfO2 where the dopant-dopant interactions are found to stabilize nanoscale phases. Max Falkowski, Alfred Kersch and co-workers from the Munich University of Applied Sciences in Germany carried out high-throughput DFT calculations with 1-nm-sized supercells of La or/and Si-doped HfO2. They found that the range of dopant interactions is on the scale of 1-nm, which is relevant for the stability of the ferroelectric phase relative to the dielectric phase. The calculated energy variation among all relevant phases is unexpectedly large, caused by the dopant interaction. The results suggest formation of nanoregions and nanolaminate effects in this material, which is important to understand recent experimental findings, such as Curie temperature broadening, interphase boundaries, and diffuse phase transitions.

Tailoring properties of hybrid perovskites by domain-width engineering with charged walls (通过带电畴壁的畴宽工程来调整杂化钙钛矿的性质)
Lan Chen,Charles Paillard,Hong Jian Zhao,Jorge iniguez,Yurong Yang & Laurent Bellaiche 
npj Computational Materials 4:75 (2018)
doi:s41524-018-0134-3
Published online:12 December 2018
Abstract| Full Text | PDF OPEN

摘要:带电的铁电畴壁是一种具有非凡特性的电拓扑缺陷,令人着迷。为了寻找该类材料的全新现象,本研究以第一性原理计算,分析了以甲基铵碘化铅杂化钙钛矿构成的光伏材料,以研究畴宽对其中具有带电畴壁的畴性质的影响。研究发现这样的畴非常稳定,而且所研究的任何畴宽(即多达13个晶格常数)都具有相当低的畴壁能量。增加畴宽首先会使电子带隙线性地从1.4eV减小到大约0 eV(从而提供了有效的带隙工程),然后体系从绝缘体过渡到金属、并在畴宽最大时保持金属特性。所有这些结果可从以下方面理解:(i)沿畴壁法线的极化分量在数量上很小; (ii)内部电场与畴宽基本无关; (iii)畴壁之间的电荷转移可忽略不计。这些发现加深了人们对带电铁电畴壁的认识,并进一步扩大其应用潜力,特别是在光伏用卤化物钙钛矿领域   

Abstract:Charged ferroelectric domain walls are fascinating electrical topological defects that can exhibit unusual properties. Here, in the search for novel phenomena, we perform and analyze first-principles calculations to investigate the effect of domain width on properties of domains with charged walls in the photovoltaic material consisting of methylammonium lead iodide hybrid perovskite. We report that such domains are stable and have rather low domain wall energy for any investigated width (that is, up to 13 lattice constants). Increasing the domain width first linearly decreases the electronic band gap from 1.4eV to about zero (which therefore provides an efficient band-gap engineering), before the system undergoes an insulator-to-metal transition and then remains metallic (with both the tail-to-tail and head-to-head domain walls being conductive) for the largest widths. All these results can be understood in terms of: (i) components of polarization along the normal of the domain walls being small in magnitude; (ii) an internal electric field that is basically independent of the domain width; and (iii) rather negligible charge transfer between walls. These findings deepen the knowledge of charged ferroelectric domain walls and can further broaden their potential for applications, particularly in the context of halide perovskites for photovoltaics. 

Editorial Summary

Hybrid perovskites: The influence of ferroelectric domains (杂化钙钛矿:铁电畴的影响) 

改变杂化钙钛矿中铁电畴的宽度会影响带隙宽度,并出现沿畴壁的金属导电性。杂化钙钛矿具有较高的光伏效率,引起了人们的广泛兴趣。这些材料的铁电畴可以被带电畴壁分离,该现象预计可以影响材料的性能。阿肯色大学的Yurong Yang及其同事,采用第一性原理计算,全面研究了带电畴壁分离的铁电畴的畴宽,对材料性能的影响。他们的研究表明,即使畴很宽,畴壁也能保持稳定,畴宽增加导致带隙减小,为带隙工程提供了一个有力的工具。增加畴宽可使畴壁具有金属性

Varying the width of ferroelectric domains in hybrid perovskites influences the band-gap width, and can result in metallic conduction along the domain walls. Hybrid perovskites are very interesting because of their high photovoltaic efficiency. These materials exhibit ferroelectric domains that can be separated by charged domain walls, which are predicted to influence the material’s properties. Yurong Yang from the University of Arkansas and colleagues performed a comprehensive first-principle investigation of the impact that the width of ferroelectric domains separated by charged domain walls has on the properties of the material. They show that domain walls remain stable even when the domains are considerably wide, and that a width increase results in a decrease of the band gap, offering a handle for band-gap engineering. For larger domain widths the domain walls become metallic.

Precision and efficiency in solid-state pseudopotential calculations (固态赝势计算的精度和效率)
Gianluca Prandini, Antimo Marrazzo, Ivano E. Castelli, Nicolas Mounet & Nicola Marzari 
npj Computational Materials 4:72 (2018)
doi:s41524-018-0127-2
Published online:06 December 2018
Abstract| Full Text | PDF OPEN

摘要:尽管密度泛函理论取得了巨大的成功和广泛普及,但系统的求证和验证研究在量度和广度方面仍均十分有限。本研究基于几个独立的标准,提出了一个实验方案来测试可以共享的赝势库,这些标准包括:验证全电子状态方程和声子频率、带结构、内聚能和压力的平面波收敛测试。采用这些标准,本研究获得了有序赝势库(或标准固态赝势库,SSSP),瞄准高通量材料筛选(“SSSP效率”)和高精度材料建模(“SSSP精度”)。在元素固态方程的Δ-因子检验中, SSSP精度在可应用的开源赝势库中表现最好   

Abstract:Despite the enormous success and popularity of density-functional theory, systematic verification and validation studies are still limited in number and scope. Here, we propose a protocol to test publicly available pseudopotential libraries, based on several independent criteria including verification against all-electron equations of state and plane-wave convergence tests for phonon frequencies, band structure, cohesive energy and pressure. Adopting these criteria we obtain curated pseudopotential libraries (named SSSP or standard solid-state pseudopotential libraries), that we target for high-throughput materials screening (“SSSP efficiency”) and high-precision materials modelling (“SSSP precision”). This latter scores highest among open-source pseudopotential libraries available in the Δ-factor test of equations of states of elemental solids. 

Editorial Summary

Density functional theory: A protocol for testing pseudopotentials (密度泛函理论:用于赝势测试的方案) 

该研究使用新提出的测试方案系统地测试现有赝势,获得了有序赝势库(或标准固态赝势库,SSSP)。尽管密度泛函理论非常受欢迎,但到目前为止,很少有人关注并验证基础赝势和投影增强波近似。由于更平滑的赝势可以实现更快的计算,所以赝势性能问题也很重要。现在,来自瑞士洛桑联邦理工学院的Nicola Marzari及其同事,介绍了共享数据库的赝势测试方案,并为85种元素选择了最佳赝势。该测试方案包括验证步骤和性能评估步骤。在高通量材料搜索中于精确度和性能之间,找到正确的平衡尤为重要,但目前这样的搜索正是全球范围内付出巨大努力有待实现的目标

Curated pseudopotential libraries obtained by systematic testing of available pseudopotentials are obtained using a newly proposed testing protocol. Density functional theory is very popular, but little attention has been devoted so far to the verification of the underlying pseudopotentials and projector augmented-wave approximations. The issue of performance is also of importance, as smoother pseudopotentials would enable faster calculations. Now, Nicola Marzari and colleagues from the Ecole Polytechnique Fédérale de Lausanne in Switzerland introduce a testing protocol for pseudopotentials in publicly available libraries, and select the optimal pseudopotential for 85 elements. The protocol includes both a verification step and performance evaluation step. Finding the right balance between precision and performance is particularly important for high-throughput materials searches, which are currently the focus of big efforts worldwide.

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