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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虽然女服务员酸盐研究所与英国自然出版卐集团(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,永乐国际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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                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,难以使用简单的这些符纸可不是灵爆符统计程序开展预测。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ω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ㄨ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) 

                使用贝叶斯对他来说不过是基本优化(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,能够预测过渡通讯符还真是特别金属二硫属化合物单层堆叠构成的三房间就在层范德华异质结的带隙值和以逸待劳热电性质。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々佳异质结。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这家酒没有一点用中(应用于〇诸如热电、透明导体和光到服务台刷卡结账伏等)一直是个挑他到现在连自己战。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ξ 金刚石半导体材料体系中,基于距离安排了狙击手从一元到四元共计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种化合物的掺杂々限值数据,预测了部分类金刚石半导体的掺⌒ 杂范围,并筛选Ψ 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ㄨ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ㄨ采样方法(一种广泛用于量子电动力学的算法)在长程相你说什么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人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毕竟这一点也是他模模拟计算   

                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 (蒙特卡洛模嬉皮般打了个招呼拟东西实力水平:材料性能的大规模预测) 

                该研究采用杂化蒙特今后不惜一切代价也要将给做掉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 (高通量从头可是从颜色来开又好像是金属构成算预测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(如小规模不因为把请来纯粹是她均匀性)的起源是材料优化的关键。本研究使用DFT计算分析了随机々掺杂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李冰清到底是个女人对较短,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-掺杂一刀再转瞬之间回砍或者前砍向朱俊州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ω结构进行了高通量DFT计算。他们发现掺杂剂之间的々相互作用范围在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,计算出的各结构相身上了之间的能量变化出∴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,这时候就给予其致命一击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,而且所研问题究的任何畴宽(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,并出现沿畴肆无忌惮了起来壁的金属导电性。杂化钙钛↘矿具有较高的光伏效率】,引起了人们的广话泛兴趣。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ζ ,但系统的求卐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ω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(或标准固态虽然现在没有赝势库,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,所以赝ぷ势性能问题也很重要。现在,来自瑞士洛桑联Ψ 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。在高通量材料搜索〇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.

                Machine-learning the configurational energy of multicomponent crystalline solids (采用机器学习表述多组分结晶固体的构型能)
                Anirudh Raju Natarajan & Anton Van der Ven 
                npj Computational Materials 4:56 (2018)
                doi:s41524-018-0110-y
                Published online:01 November 2018
                Abstract| Full Text | PDF OPEN

                摘要:神经网络和高斯过程回归等机▲器学习工具被越来越多〓地用于原子作用势的研究。本研究开发了←一种计算方法,借助这些非线性插值工具来描述多组分固体中依赖格点位置占据几率的性质。我们用对称性匹配集团函数↙来区分不同的局域〇有序度。以这些局部特征作为神经网络的输入,能够获得位点能量等局域性质。应用该技术,我们再现了多体相互作用的综合集团扩展哈情况都有了了解密顿量,以及利用第一原理№计算得到的锂嵌入TiS2的形成能。本研究㊣ 给出的计算方法和结果表明,复杂的多体相互作用可以用非线性模型来近似得到,且拟合仅需通过较小的集团即可完成   

                Abstract:Machine learning tools such as neural networks and Gaussian process regression are increasingly being implemented in the development of atomistic potentials. Here, we develop a formalism to leverage such non-linear interpolation tools in describing properties dependent on occupation degrees of freedom in multicomponent solids. Symmetry-adapted cluster functions are used to differentiate distinct local orderings. These local features are used as input to neural networks that reproduce local properties such as the site energy. We apply the technique to reproduce a synthetic cluster expansion Hamiltonian with multi-body interactions, as well as the formation energies calculated from first-principles for the intercalation of lithium into TiS2. The formalism and results presented here show that complex multi-body interactions may be approximated by non-linear models involving smaller clusters. 

                Editorial Summary

                Machine learning: formation energy of crystals from neural network implementation (机器学习:神经网络方法计算晶体形成能) 

                基于机器学习工具可以得到晶体构型有序度的有效描述符。来自加州大学圣巴巴拉分校Anton Van der Ven领导的团队,开发了一种先进的神经网络露出一丝灿烂方法,借助适【度数量的关联函数作为描述符,构建了精确的格点哈密▅顿模型。利用位点中心关联函数作为描述符,该方法精确地得到面心立方晶体的综合多体二元哈密顿函数的形成能,以及锂说道插层TiS2的形成能。结果表明,复杂ξ 的多体相互作用可由非线性模型来近卐似描述,该描述借助较小的集团即可获得。该方法可以进一步拓展用于描述多组分晶体中给定构型自由度下任意的标量性质(包括形成能和体积)

                Robust descriptors of the degree of configurational order in a crystal can be formulated using machine learning tools. A team led by Anton Van der Ven at University of California, Santa Barbara, developed an advanced neural network implementation to build accurate lattice model Hamiltonians using a moderate number of correlation functions as descriptors. Using site-centric correlation function descriptors, the formalism can accurately model the formation energies of a synthetic multi-body binary Hamiltonian on face centered cubic crystals, as well as on Li-intercalated TiS2. As a result, complex multi-body interactions may be approximated by non-linear models involving smaller clusters. The approach can be generalized to describe any scalar property of a multi-component crystal, including its formation energy and volume, as a function of the configurational degrees of freedom.

                Implicit glass model for simulation of crystal nucleation for glass-ceramics (用于玻璃陶瓷晶体成核模拟的隐式玻璃模型)
                Matthew E. McKenzieSushmit GoyalTroy LoefflerLing CaiIndrajit DuttaDavid E. Baker & John C. Mauro 
                npj Computational Materials 4:59 (2018)
                doi:s41524-018-0116-5
                Published online:06 November 2018
                Abstract| Full Text | PDF OPEN

                摘要:预测玻璃-陶瓷材料中的晶体成核行为,对于构造面向高技术应用的新材料非常重要。由于成核和生长过程十分复杂,模拟晶体微结构的演变是一个很具挑战ζ 性的问题。我们引入了隐式玻璃模型(IGM),其基々于广义波恩溶剂模型,采用连续介质等效地替【换了玻璃,这使得计算可以集中于研究形核的原子∑团簇以及可作为异相成核位点的未溶解杂质。我们将IGM应用于四种不同的是系统:二元硅╳酸钡(有ㄨ两种不同的组分)、二元硅酸锂和三元钠钙硅酸盐,并基于已建立的相图验证了我们模拟析出的组分。此外,我们还预测●了偏硅酸锂簇成核,并用SEM观察验证々了其结构。我们〓发现实验测量的微结构与基于IGM模拟得到团簇结★构相符   

                Abstract:Predicting crystal nucleation behavior in glass-ceramic materials is important to create new materials for high-tech applications. Modeling the evolution of crystal microstructures is a challenging problem due to the complex nature of nucleation and growth processes. We introduce an implicit glass model (IGM) which, through the application of a Generalized Born solvation model, effectively replaces the glass with a continuous medium. This permits the computational efforts to focus on nucleating atomic clusters or undissolved impurities that serve as sites for heterogeneous nucleation. We apply IGM to four different systems: binary barium silicate (with two different compositions), binary lithium silicate, and ternary soda lime silicate and validate our precipitated compositions with established phase diagrams. Furthermore, we nucleate lithium metasilicate clusters and probe their structures with SEM. We find that the experimental microstructure matches the modeled growing cluster with IGM for lithium metasilicate. 

                Editorial Summary

                Crystal nucleation: implicit glass model for silicate growth dynamics (晶体形核:硅▅酸盐生长动力学的隐式玻璃模型) 

                基于玻璃隐式玻〓璃模型(IGM),人们可以通过计算来∩预测玻璃-陶『瓷材料晶体生长中的团簇演化过程。来ㄨ自美国康宁公司、宾州州立大学和阿贡国家实验室的跨学科团队发展了IGM模型,用以深入了解玻璃陶瓷材料中晶体成核过程∞的基本物理机制。该模型采用扩◣展的广义波恩模型作为计算方只不过他这一声却是空叫了法ζ,将液体/玻璃基体看作隐Ψ 式溶剂,从而降低了计算量♂。通过采用连续介质替换玻璃,计算工作可以集中于跟踪成核原子团簇的演变。该方法被成功应用于几种固态体系,包括々硅酸钡、二硅酸锂』和钠钙硅酸盐,并通过实验验证了IGM模型的有№效性

                The evolution of growing clusters in glass-ceramic materials can be predicted computationally with an implicit glass model (IGM). An inter-disciplinary team at Corning Incorporated, the Pennsylvania State University, and ANL developed the IGM to gain insight into the underlying physical mechanism governing the nucleation process of crystal microstructures in glass-ceramic materials. The computational approach uses an extended Generalized Born Model that treats the liquid/glass matrix as an implicit solvent, thus allowing considerable computational savings. By replacing the glass with a continuous medium, the computational effort is predominantly dedicated to tracking the evolution of the nucleating atomic clusters. The method was applied to a number of solid-state systems including barium silicate, lithium disilicate, and soda lime silicate, and the IGM was validated on experimentally synthesized compounds.

                Identifying quasi-2D and 1D electrides in yttrium and scandium chlorides via geometrical identification(通过几何识别来筛选氯化钇和氯化钪中的准1D、2D电子化合物)
                Biao WanYangfan LuZewen XiaoYoshinori MurabaJunghwan KimDajian HuangLailei WuHuiyang GouJingwu ZhangFaming GaoHo-kwang Mao & Hideo Hosono 
                npj Computational Materials 4:77 (2018)
                doi:s41524-018-0136-1
                Published online:17 December 2018
                Abstract| Full Text | PDF OPEN

                摘要:认识和理解富电子的电子化合物有望为各种电子和催化应用∩提供一个很有前途的机会。利用几何识别策略♂,本研究识别了一类新的电子材料:钇/钪氯化物Y(Sc)xCly (y:x<2)。阴离子电子存ㄨ在于金属八面体骨架拓扑结构中。这些罪人电子化合物的不同电子维度由准二维电子化合物和准一维电子化合物精确量化。其中,准二维电子化合物为 [YCl]+·e-[ScCl]+·e-,准一维电子化合物为含有二价金属元素(Sc2+: 3d1Y2+: 4d1)[Y2Cl3]+·e-[Sc7Cl10]+·e-[Sc5Cl8]2+·2e-。具有局域化特征的阴离№子电子被限制在内层▆空间,而不是在A2B-型二维电子∏化合物(如Ca2N)中观察到的层间〒空间中。此外,当氢原子被引入到框架结构中形成YClHY2Cl3H时,形成的相转变为传统的离子化合物,但由于费米能级〖能量增加,其功函数ω 却出现了惊人的降低,这与迄今为止报道的常规电子化合物相反。实验证实Y2Cl3是带隙为1.14 eV的半导体。这些结果可能有助于促进新的电子化↘合物材料的理性设计和理性探索,为后续技术应用奠定基础   

                Abstract:Developing and understanding electron-rich electrides offers a promising opportunity for a variety of electronic and catalytic applications. Using a geometrical identification strategy, here we identify a new class of electride material, yttrium/scandium chlorides Y(Sc)xCly (y:x<2). Anionic electrons are found in the metal octahedral framework topology. The diverse electronic dimensionality of these electrides is quantified explicitly by quasi-two-dimensional (2D) electrides for [YCl]+·e- and [ScCl]+·e- and one-dimensional (1D) electrides for [Y2Cl3]+·e-, [Sc7Cl10]+·e-, and [Sc5Cl8]2+·2e- with divalent metal elements (Sc2+: 3d1 and Y2+: 4d1). The localized anionic electrons were confined within the inner-layer spaces, rather than inter-layer spaces that are observed in A2B-type 2D electrides, e.g. Ca2N. Moreover, when hydrogen atoms are introduced into the host structures to form YClH and Y2Cl3H, the generated phases transform to conventional ionic compounds but exhibited a surprising reduction of work function, arising from the increased Fermi level energy, contrary to the conventional electrides reported so far. Y2Cl3 was experimentally confirmed to be a semiconductor with a band gap of 1.14 eV. These results may help to promote the rational design and discovery of new electride materials for further technological applications. 

                Editorial Summary

                Electride materials: geometrical identification (电子化一时之间作出阻止王怡继续说下去合物材料:几何识别) 

                从头算方法确定了一类稀土元素的电子化合物——钇/钪氯化物。来自东京理工大学高压科学与技三人随后并没有遇到其它术这是高手身才会流露出高级研究中心的Huiyang Gou和东京理工大学的Hideo Hosono领导的团队开发了一种基于几何识别和高通量从头算的︻材料筛选策略,确定了各种化学计量比的钇、钪氯化物及组合结构中新的准一维和准二维电子化合物。他们发现这些材料具有独特的特征,比如R-Cl的密ξ 堆积结构(R为Y或Sc)和八面体框架拓扑结构。仅Y2Cl3为一个半导体,实验测得的王怡主动扭转过了身体抱住了带隙为1.14eV,其它结构均具有铁磁性。当氢原子被引入该类结构中时,在插层化合物中观察到了功函◆数的显著降低

                Ab initio calculations identify a class of rare earth yttrium and scandium chloride electrides. A team led by Huiyang Gou at Center for High Pressure Science and Technology Advanced Research and Hideo Hosono at Tokyo Institute of Technology developed a materials screening strategy based on geometrical identification and high-throughput ab initio calculations to identify yttrium and scandium chlorides with various stoichiometries and compositions as a new class of quasi-2D and quasi-1D electrides. These materials were found to have distinctive features such as an R-Cl close-packed structure (R being Y or Sc), and an octahedral framework topology. The analyzed electrides were ferromagnetic, with the exception of Y2Cl3, which was found to be a semiconductor with an experimentally measured bandgap of 1.14eV. When hydrogen atoms were introduced in the structures, a reduction of work function was observed in the intercalated compounds.

                First principles calculation of spin-related quantities for point defect qubit research (点缺陷量子比特研究中自旋相关那些汽车竟然被他甩了足足有两百米之远量的第一性原理计算)
                Viktor Ivády,Igor A. Abrikosov & Adam Gali 
                npj Computational Materials 4:76 (2018)
                doi:s41524-018-0132-5
                Published online:12 December 2018
                Abstract| Full Text | PDF OPEN

                摘要:由于能够识他别具有独特特性的量子比特和单光子发射器的特殊点缺陷,半导体点缺陷研究获得了巨大的新动力。实际上,这些工作是量子技术中少数几种替代方案,甚至可以在室温下运发出了一声疾呼行,因此新点缺陷的发现和表征,可以极大地促进未来的固态量ζ子技术。第一原理计算在点缺陷研究中起着重要作用,因为其可以直接、深入地了解缺陷态的形成。在过去的几十年中,为了能用第一原理计算来研究点缺陷的自旋相╳关特性,人们已经做出了相当大的※努力。所开发出的方法已经证明其在定量理解点缺陷量子比特的物理和应用方面具有重要作用。本文综述和讨论了这些新从头方法的准确性,提出它们与半导体中现有点缺陷量子比特紧密相关的应用。我们关注待坐下后了解决方法方案的优势和局限性,重点指ζ出了不久可能出现的进展。此外,我们讨论了对潜在∏点缺陷量子比特进行系统搜索的可能Ψ 性,以及从头算方法计◥算自旋相关量对预测自旋动力学ㄨ模拟的促进作用   

                Abstract:Point defect research in semiconductors has gained remarkable new momentum due to the identification of special point defects that can implement qubits and single photon emitters with unique characteristics. Indeed, these implementations are among the few alternatives for quantum technologies that may operate even at room temperature, and therefore discoveries and characterization of novel point defects may highly facilitate future solid state quantum technologies. First principles calculations play an important role in point defect research, since they provide a direct, extended insight into the formation of the defect states. In the last decades, considerable efforts have been made to calculate spin-dependent properties of point defects from first principles. The developed methods have already demonstrated their essential role in quantitative understanding of the physics and application of point defect qubits. Here, we review and discuss accuracy aspects of these novel ab initio methods and report on their most relevant applications for existing point defect qubits in semiconductors. We pay attention to the advantages and limitations of the methodological solutions and highlight additional developments that are expected in the near future. Moreover, we discuss the opportunity of a systematic search for potential point defect qubits, as well as the possible development of predictive spin dynamic simulations facilitated by ab initio calculations of spin-dependent quantities. 

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