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Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks(基于数据增强和深度神经网络对X射线衍射小型数据集的快速和可解释分类)
Felipe OviedoZekun RenShijing SunCharles SettensZhe LiuNoor Titan Putri HartonoSavitha RamasamyBrian L. De CostSiyu I. P. TianGiuseppe RomanoAaron Gilad Kusne & Tonio Buonassisi
npj Computational Materials 5:60 (2019)
doi:s41524-019-0196-x
Published online:17 May 2019
Abstract| Full Text | PDF OPEN

摘要:X射线衍射(XRD)数据采集与分析是新型薄膜材料研发周期中最耗时的步骤之一。本研究提出了一种基于机器学习的方法,用于从有限数量的薄膜XRD?#35745;?#20013;预测晶体学维度和空间群。基于无机晶体结构数据库(ICSD)和实验数据的模拟数据,我们将监督机器学习方法与模型无关的、物理信息输入的数据增强策略相结合,克服了新材料开发固有的稀缺数据问题。作为实测案例,本研究合成115种跨越3个维度和7个空间群的金属卤化物薄膜并对其进行了分类。在测试了各种算法之后,我们开发、实现了一个全卷积神经网络,其交叉验证的维数和空间群分类的准?#33539;?#20998;别达到93%和89%。依据整体平均汇集层计算,我们提出了平均分类激活图,以便允许人们对实验模型结果作充分的解释、对分类错误的根本原因作出阐明。最后,我们系统地评估了发生预测精度损失的最大XRD图案步长(数据采集速率)为2θ=0.16°,因而仅需5.5分?#30001;?#33267;更短时间就可以得到一张XRD?#35745;?#24182;对其分类   

Abstract:X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal-halides spanning three dimensionalities and seven space groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross-validated accuracies for dimensionality and space group classification of 93 and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16° 2θ, which enables an XRD pattern to be obtained and classified in 5.5min or less. 

Editorial Summary

Small X-ray diffraction datasets: Fast and interpretable classificationX射线衍射小型数据集:快速和可解释的分类

快速材料表征对于高通量新材料探索十分重要。X射线衍射(XRD)?#35745;?#30340;获取和分析是当前高通量材料实验筛选的瓶颈之一。针对上述问题,来?#26376;?#30465;理工学院和新加坡的研究团队发展了一种基于监督机器学习的框架用于快速获得和识别新型薄膜材?#31995;?/span>XRD?#35745;住?#20182;们首先根据ICSD数据库中164种薄膜卤化物和115种实验合成薄膜的XRD?#35745;?#24314;立了一个数据库。基于这个小型库发展了一个与模型无关的、物理信息输入的数据扩展方法用于构建训练数据集。进而采用该数据集训练了一个卷积神经网络用于XRD?#35745;?#20998;类,其维度和空间群分类准确率分别可达9389%。本研究提出的方法可以成功解决新材料探索固有的数据稀缺问题,能够快速地(在5.5分钟以内)得到一个新材?#31995;?/span>XRD?#35745;?#24182;对其进行分类

Rapid characterization are necessary ingredients for accelerated material discovery in high-throughput way. However, XRD characterization is currently a common bottleneck in such screening loops. A joint team from Massachusetts Institute of Technology and Singapore-MIT Alliance for Research and Technology proposed a supervised machine learning framework for rapid crystal structure identification of novel materials from thin-film XRD measurements. They first created a library including 164 XRD patterns of thin-film halide materials extracted from the ICSD and an additional 115 experimental experimental XRD patterns. With this small dataset, a model-agnostic, physics-informed data augmentation is proposed to generate a suitable and robust training dataset for thin-film materials. Then a convolutional neural network is trained as an accurate and interpretable classifier with cross-validated accuracies for dimensionality and space group classification of 93 and 89%, respectively. This approach successfully addresses the sparse/scarce data problem intrinsic to novel materials and enables rapid acquisition and analysis of XRD pattern, e.g. in 5.5 min or less.

Electric field tuning of the anomalous Hall effect at oxide interfaces(氧化物界面处反常霍尔效应的电场调谐)
Sayantika Bhowal & Sashi Satpathy
npj Computational Materials 5:61 (2019)
doi:s41524-019-0198-8
Published online:21 May 2019
Abstract| Full Text | PDF OPEN

摘要:反常霍尔效应是自旋极化电子的输运特性受自旋轨道耦合控制的现象,自旋轨道耦合耦合了电子的轨道自由度和自旋自由?#21462;?span>本文证明了强自旋轨道耦合磁界面的反常霍尔效应可以通过外加电场进行调?#22330;?#36890;过改变反演对称性破缺的强度,电场改变了Rashba相互作用,而Rashba相互作用反过?#20174;?#25913;变了Berry曲率的大小,而Berry曲率是决定反常霍尔电导率的关键物理量。结果表明,在方点阵模型的小电场作用下,反常霍尔电导率呈二次相关关系。对新近生长的铱酸盐界面,即(SrIrO3)1/(SrMnO3)1(001)结构进行了显式密度泛函计算发现,该结构无论有无电场均表现出很强的电场?#35272;?#24615;。该效应在自旋电子学应用中具有很大的潜力   

Abstract:Anomalous Hall effect is the phenomenon where the transport properties of the spin-polarized electrons are governed by the spin-orbit coupling that couples the orbital and spin degrees of freedom of the electron. Here we show that the anomalous Hall effect at a magnetic interface with strong spin-orbit coupling can be tuned with an external electric field. By altering the strength of the inversion symmetry breaking, the electric field changes the Rashba interaction, which in turn modifies the magnitude of the Berry curvature, the central quantity in determining the anomalous Hall conductivity. The effect is illustrated with a square lattice model, which yields a quadratic dependence of the anomalous Hall conductivity for small electric fields. Explicit density-functional calculations were performed for the recently grown iridate interface, viz., the (SrIrO3)1/(SrMnO3)1 (001) structure, both with and without an electric field, which show a strong electric field dependence. The effect may be potentially useful in spintronics applications. 

Editorial Summary

Anomalous Hall effect: Electric field tuning at oxide interfaces反常霍尔效应:氧化物界面处的电场调谐

利用外加电场对Rashba自旋轨道相互作用进行修正,可以调节3d-5d界面处的反常霍尔效应。来自美国密苏里大学的Sayantika Bhowal  Sashi Satpathy两位研究人员,使用了一套普适参数以及密度泛函理论,计算了特定界面(SIO)1/(SMO)1结构的反常霍尔电导率电场?#35272;?#24615;的主要贡献来源于靠近费米能量的能带交叉点。此外,AHC可以通过掺杂来调控电子态,从而实现调整反常霍尔电导率。他们为?#24471;?#35813;结果,使用了铁磁晶格模型,并使用了最近生长的亚锰酸盐-铱酸盐界面 [(SIO)1/(SMO)1(001)]的密度泛函计算。实际上,最近的一些实验已经发现了氧化物异?#24335;?#26500;中反常霍尔电导率随电场变化的证据。因此,从理论上和实验上进一步发展这?#20013;?#24212;,并着眼于潜在的自旋电子学应用将是极具价值的

The anomalous Hall effect at the 3d-5d interfaces can be tuned by modifying the Rashba spin-orbit interaction with the application of an external electric field. Prof. Sayantika Bhowal and Sashi Satpathy from University of Missouri, USAillustrated this method using general arguments as well as from density-functional calculations of the anomalous Hall conductivity for a specific interface structure (SIO)1/(SMO)1. The major contribution to the electric-field dependence comes from the band-crossing points close to the Fermi energy. In addition, the AHC can be tuned by manipulating the electron density with doping. They illustrated the results with a ferromagnetic square-lattice model as well as with density-functional calculations for the recently grown manganite-iridate interface, viz., (001) (SIO)1/(SMO)1. In fact, several recent experiments have found evidence for the electric field dependence of the anomalous Hall conductivity in the oxide heterostructures. Therefore, it would be valuable to develop this effect further, both theoretically and experimentally, with an eye towards potential spintronics applications.

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning (基于机器学习的硅藻基因型与表?#25237;?#24212;联系的硅藻基因工程深度数据分析)
Artem A. TrofimovAlison A. PawlickiNikolay BorodinovShovon MandalTeresa J. MathewsMark HildebrandMaxim A. ZiatdinovKatherine A. HausladenPaulina K. UrbanowiczChad A. SteedAnton V. IevlevAlex BelianinovJoshua K. MichenerRama Vasudevan & Olga S. Ovchinnikova
npj Computational Materials 5:67 (2019)
doi:s41524-019-0202-3
Published online:13 June 2019
Abstract| Full Text | PDF OPEN

摘要:用于材料合成的材料基因组工程,是在一定条件下制造出具有独特性质的材?#31995;?#19968;种?#24615;?#22823;前途的工程路径。硅藻的生物矿化,是单细胞藻类使用二氧化硅构建的细胞壁,这种细胞壁虽是微?#20934;?#30340;,但其诸多特征性结构是纳?#20934;?#30340;,是?#22411;?#29992;于光学、传?#23567;?#36807;滤和药物递送等领域的合成功能材料,是这些领域引人注目的候选材料。因此,针对这些应用,通过定向遗传修饰实现可控的硅藻结构修?#27169;?#21069;途广阔。本研究中,我们在硅藻(伪矮海链藻)中,采用基因敲除?#38469;?/span>创建经过基因修饰的藻株,使藻体形态发生变化,应用监督机器学习实现了基因型变化与表型变化对应联系我们开发了人工神经网络(NN区分野生基因敲除型硅藻,NN可依据硅藻壳的SEM照片所展示的由特定蛋白质(Thaps3_21880)引起的表型变化进行辨别区分,辨别区分准?#33539;?/span>达94。类激活?#25104;?/span>使物理变化可视化,允许NN区分硅藻藻株,随后筛查找到控制孔的特定基因。进一步创建了另一个NN以批?#30475;?#29702;图像数据,自动识别刚毛毛孔,提取毛孔相关参数。使用多变量数据可视化(CrossVis)工具可视化所提取的参数之间的类相互关系,并允许直接将孔径和分布的形态学表型变化,与基因型的变化对应联系起来   

Abstract:Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype. 

Editorial Summary

Genotype modification linking changes of diatom frustule phenotype: machine learning硅藻基因型改变与硅藻壳变化:机器学习

该研究比较了一种野生型和基因敲除型硅藻,以揭示修改的基因型和表达的表型之间的相互作用,因为基因操作可以使这些生物体被直接用作特别定制的纳米结构和微结构材料。来自美国橡树林国家实验的Olga S. Ovchinnikova领导的研究团队,通过敲除怀疑可能与硅藻壳形成有关的基因来修改硅藻基因型,并通过扫描电镜表征该基因引起的表型变化。他们使用图像处理和机器学习分类算法(人工NN)?#29943;?#36873;影响硅藻表型的基因,并将野生型硅藻与基因修饰型区分开来。就控制毛孔形态的蛋?#20934;?#27979;来说,他们的识别野生型和基因修饰型硅藻的NN,检测准?#33539;?#20026;94%。为解释基于NN的分类表观准确率,他们?#32654;?#28608;活图(CAM)来突出?#20801;?#32593;络使用的图像区域,发现硅藻壳的孔是将野生型硅藻与一种特定的敲低基因表达的藻株分开的稳定特征。随后,他们创建了另一个神经网络,专门针对毛孔并提取其参数。这种自动化特征提取过程使人们能够将遗传修饰与硅藻形态对应关联起来。这一方法?#33539;?#20102;由给定的遗传修饰产生的藻壳结构的变化,为生物矿化过程提供了生物学探测能力

Wild type and genetically modified diatoms is investigated to capture the interplay between the changing genotype and the expressed phenotype in diatom frustule, as gene manipulation could enable these organisms to be used as a direct source of specifically tailored nanostructured and microstructured materials.  A team led by Olga S. Ovchinnikova from the Oak Ridge National Laboratory, USA, modified the genotype by knocking down genes potentially involved with frustule formation and characterized the phenotype by scanning electron microscopy. They used image processing and machine learning classification algorithms (artificial NNs) to screen for genes that affect diatom phenotype and to distinguish diatoms with wild type and modified morphologies. With regard to inspecting a protein modification that controls pores in frustule, they demonstrated a NN that can identify wild and modified diatoms with 94% accuracy. To explain the apparent efficiency of NN-based classification, class activation maps (CAMs) were used to highlight the image regions used by the network, consolidating the defining features separating wild-type diatoms from one specific knockdown strain. They then created a separate neural net to focus specifically on pores and to extract their parameters. This automated feature extraction process could correlate the genetic modification with diatom morphology. This approach identifies the changes in frustule structure that result from a given genetic modification, offering biological insight into the biomineralization process.

Coordination corrected ab initio formation enthalpies (协同纠正的从头算形成焓)
Rico FriedrichDemet UsanmazCorey OsesAndrew SupkaMarco FornariMarco Buongiorno NardelliCormac Toher & Stefano Curtarolo
npj Computational Materials 5:59 (2019)
doi:s41524-019-0192-1
Published online:15 May 2019
Abstract| Full Text | PDF OPEN

摘要:如何正确计算形成焓是从头算材料设计的一个功能。使用标准密度泛函理论对几类材料系统(如氧化物)计算时,会得到一些错误的结果。本研究基于最近邻阳离子-阴离子键的数量,提出了“协调校正?#30465;?#30340;方法(CCE),不仅校正焓,还能校正多晶型的相对稳定性。CCE使用PerdewBurke-ErnzerhofPBE)、局部密度近似(LDA)和强?#38469;?#21644;?#23454;?#35268;范(SCAN)交换相关泛函,结合准谐波Debye模型?#21019;?#29702;零点振动和热效应。在二元和三元氧化物(卤化物)上进行的基准测试结果?#20801;荊?#25152;有函数的室温结果都非常准确,用SCAN计算获得的最小平均绝对误差为27(24)meV/atom。这个误差对形成焓的零点振动和热贡献很小,并且不同的误差信号在很大程度上相互抵消   

Abstract:The correct calculation of formation enthalpy is one of the enablers of ab-initio computational materials design. For several classes of systems (e.g. oxides) standard density functional theory produces incorrect values. Here we propose the “coordination corrected enthalpies” method (CCE), based on the number of nearest neighbor cation–anion bonds, and also capable of correcting relative stability of polymorphs. CCE uses calculations employing the Perdew, Burke and Ernzerhof (PBE), local density approximation (LDA) and strongly constrained and appropriately normed (SCAN) exchange correlation functionals, in conjunction with a quasiharmonic Debye model to treat zero-point vibrational and thermal effects. The benchmark, performed on binary and ternary oxides (halides), shows very accurate room temperature results for all functionals, with the smallest mean absolute error of 27(24)meV/atom obtained with SCAN. The zero-point vibrational and thermal contributions to the formation enthalpies are small and with different signs—largely canceling each other. 

Editorial Summary

Coordination corrected ab initio formation enthalpies预测化合物稳定性的计算误差:协同纠正

该研究基于最近邻阳离子-阴离子键的数量引入了一种完全主动的校正方案:“协调校正焓(CCE)”方案,可以解决预测化合物的热力学稳定性时的误差。来自美国杜克大学的Stefano Curtarolo领导的团队,使用三?#20013;?#27491;计算方法:Perdew-Burke-ErnzerhofPBE)、局部密度近似(LDA)和强?#38469;?#21644;?#23454;?#35268;范(SCAN)交换相关泛函的计算,结合准谐波Debye模型来校正717)三元氧化物(卤化物)的零点振动和热效应,分别给出了3849),2974)和2724meV / atomMAE极为准确的校正形成?#30465;?#20182;们用准谐波Debye模型处理零点温度和有限温度的振动时,发现振动在很大程度上被消除了,误差比以前的方法要小得多。CCE得到精确的形成焓,平均绝对误差小至20-30 meV /原子。该方法简单且?#23376;?#25193;展到其他体系如氮化物、磷化物或硫化物等材料。本方法可用于预测?#35272;?#20110;精确形成焓的各?#20013;?#36136;,例如电池电压、缺陷能量和高熵材?#31995;?#24418;成。由于CCE考虑了化学键的连接和拓扑结构,因此它还可以纠正给定组分的不同结构的相对稳定性

A physically motivated correction scheme — coordination corrected enthalpies (CCE), based on the number of bonds between each cation and surrounding anions, is proposedwhich can minimize the error in predicting thermodynamic stability of compounds. A team led by Stefano Curtarolo from the Duke University, USA, employed the Perdew, Burke and Ernzerhof (PBE), local density approximation (LDA) and strongly constrained and appropriately normed (SCAN) exchange correlation functionals, in conjunction with a quasiharmonic Debye model to treat zero-point vibrational and thermal effects of 71(7) ternary oxides (halides), and gives very accurate corrected formation enthalpies with mean absolute errors of 38(49), 29(74) and 27(24)meV/atom, respectively. Zero-point and finite temperature vibrational contributions are treated within a quasiharmonic Debye model and are found to largely cancel out, with errors significantly smaller than previous approaches. CCE yields accurate formation enthalpies with an average absolute error as small as 20–30meV/atom. The method is simple and easy to extend to other materials classes, e.g. nitrides, phosphides, or sulfides. It can be used to predict a wide variety of properties relying on accurate formation enthalpies, such as battery voltages, defect energies, and the formation of high-entropy materials. Because CCE considers bonding connectivity and topology, it can also correct the relative stability of different structures at a given composition.

Computational strategies for design and discovery of nanostructured thermoelectrics (设计和发现纳米结构热电材?#31995;?#35745;算策略)
Shiqiang HaoVinayak P. DravidMercouri G. Kanatzidis & Christopher Wolverton
npj Computational Materials 5:58 (2019)
doi:s41524-019-0197-9
Published online:14 May 2019
Abstract| Full Text | PDF OPEN

摘要:理论计算和预测在先进高性能热电材?#31995;?#21457;展中发挥越来越重要的贡献,并成功地引?#38469;?#39564;理解并实现破纪录的好结果。本文从理论计算的角度,综述了近年来高性能纳米结构体热电材?#31995;?#30740;究进展。提高热电性能的一个有效的新兴策略涉及多尺度调控的电子散射最小化、声子散射的最大化。我们提出了几个重要的策?#38498;?#20851;键的例子,突出了基于第一性原理的计算在揭示热电性能协同优化的复杂但?#23376;?#22788;理的关系方面的贡献。综合优化方法为改进材料提供了四重设计策略:1)通过多尺度分层架构显著降低晶格热导率;2)通过本征矩阵的电子能带简并工程大幅提高塞贝克系数;3)通过主相和第二相之间的带边形状调控载流子迁移率;4)通过最大化加强非谐振动和声子Gruneisen参数来设计具有本征低导热率的材料。这些组合效应可以在降低晶格热导率的同时提高功率因子。本综述对理论如何影响该领域的现状提供了更好的理解,并有助于指导未来高性能热电材?#31995;?#30740;究   

Abstract:The contribution of theoretical calculations and predictions in the development of advanced high-performance thermoelectrics has been increasingly significant and has successfully guided experiments to understand as well as achieve record-breaking results. In this review, recent developments in high-performance nanostructured bulk thermoelectric materials are discussed from the viewpoint of theoretical calculations. An effective emerging strategy for boosting thermoelectric performance involves minimizing electron scattering while maximizing heat-carrying phonon scattering on many length scales. We present several important strategies and key examples that highlight the contributions of first-principles-based calculations in revealing the intricate but tractable relationships for this synergistic optimization of thermoelectric performance. The integrated optimization approach results in a fourfold design strategy for improved materials: (1) a significant reduction of the lattice thermal conductivity through multiscale hierarchical architecturing, (2) a large enhancement of the Seebeck coefficient through intramatrix electronic band convergence engineering, (3) control of the carrier mobility through band alignment between the host and second phases, and(4) design of intrinsically low-thermal-conductivity materials by maximizing vibrational anharmonicity and acoustic-mode Gruneisen parameters. These combined effects serve to enhance the power factor while reducing the lattice thermal conductivity. This review provides an improved understanding of how theory is impacting the current state of this field and helps to guide the future search for high-performance thermoelectric materials. 

Editorial Summary

Nanostructured thermoelectrics: design and discovery纳米结构热电材料:设计和发现

该综述描述了四种典型的计算策略在提高纳米结构体相热电性能方面的应用。来自美国西北大学Christopher Wolverton领导的研究小组综合了最近的重要研究进展,揭示?#22235;?#31859;结构热电体相材料设计和发现的计算策略的规律。到目前为止,已经用高ZT > 2证明了几种体积热电材?#31995;?#20248;异热电性能。所有这些高ZT优值的材料?#21152;?#38597;地体现了PGEC的概念。特别是,利用最小电子散射和最大限度地利用纳米结构方法的全长尺度热载流子散射的结合,实现了许多材料ZT优值的提高。纳米结构方法集成了许多调用多尺度声子散射的新思想:包括原子尺度合金化、内生纳米结构和中尺度颗粒边界控制,并以协同的方式结合?#22235;?#24102;对齐和简并工程方法。这种综合方法也是一种将ZT提高到3的最合理方法。在?#38750;?#26356;高的ZT优值时,第一性原理计算对于提供理论解释、材料选择甚至ZT预测都是至关重要的

The use of four typical computational strategies to enhance the thermoelectric performance of nanostructured bulk materials is reviewed. A team led by Christopher Wolverton from the Northwestern University, USA, combined all the recent important research progress and revealed the trends in computational strategies for design and discovery of nanostructured thermoelectrics. Thus far, the extraordinary thermoelectric performance of several bulk thermoelectric materials has been demonstrated with a high ZT>2. All of these high-ZT materials elegantly reflect the PGEC concept. In particular, many of the enhanced figures of merit were achieved using a combination of minimizing electron scattering and maximizing all-length-scale heat-carrying phonon scattering using nanostructuring methods. The nanostructuring methods integrate many new concepts of invoking multiscale phonon scattering, including atomic-scale alloying, endotaxial nanostructuring, and mesoscale grain-boundary control, with band alignment and convergence engineering methods in a synergistic manner. This integrated methodology is also the most plausible approach to increase ZT to the next threshold of ZT=3. In the pursuit of higher ZT, first-principles calculations are critical to providing theory explanations, material selections, and even ZT predictions.

Bayesian inference of atomistic structure in functional materials (功能材料中原子结构的贝叶斯预测)
Milica TodorovicMichael U. GutmannJukka Corander & Patrick Rinke
npj Computational Materials 5:35 (2019)
doi:s41524-019-0175-2
Published online:18 March 2019
Abstract| Full Text | PDF OPEN

摘要:订制先进有机/无机异质器件使其符合预期?#38469;?#24212;用的功能特性,需要了解器件内部的微观结?#20849;?#33021;对其调控。原子尺度量子力学模拟方法可以针对具体材料结构给出精确预测的能量和性质,然而,通过计算的结构仍然比较困?#36873;?#26412;研究提出了一种基于“构筑模块”的贝叶斯优化结构搜索(BOSS)方法,用于解决扩展的有机/无机界面问题,并证明了其在分子表面吸附研究中的可行性。在BOSS中,贝叶斯模型通过主动学习采样的原子构象快速?#33539;?#26448;?#31995;?#21183;能面。这使我们能够在TiO2 ?#32806;?#30719;相的(101)面上?#33539;?/span>C60的几种最有利的分子吸附结构,并阐明控?#24179;?#26500;组装的关键分子-表面相互作用。预测的结构与实验图像非常一致,证明了BOSS的良好预测能力,并为分子聚集体和薄膜的大尺度表面吸附研究开辟了道路   

Abstract: Tailoring the functional properties of advanced organic/inorganic heterogeneous devices to their intended technological applications requires knowledge and control of the microscopic structure inside the device. Atomistic quantum mechanical simulation methods deliver accurate energies and properties for individual configurations, however, finding the most favourable configurations remains computationally prohibitive. We propose a ‘building block’-based Bayesian Optimisation Structure Search (BOSS) approach for addressing extended organic/inorganic interface problems and demonstrate its feasibility in a molecular surface adsorption study. In BOSS, a Bayesian model identifies material energy landscapes in an accelerated fashion from atomistic configurations sampled during active learning. This allowed us to identify several most favourable molecular adsorption configurations for C60 on the (101) surface of TiO2 anatase and clarify the key molecule-surface interactions governing structural assembly. Inferred structures were in good agreement with detailed experimental images of this surface adsorbate, demonstrating good predictive power of BOSS and opening the route towards large-scale surface adsorption studies of molecular aggregates and films. 

Editorial Summary

Atomistic structure in functional materials: Bayesian inference功能材料中的原子结构:贝叶斯预测

该研究针对有机无机材料界面结构预测提出基于“结构块”的贝叶斯结构搜索方案。由?#20381;及?#23572;托大学Milica Todorovic等领导的团队,将人工智能采样策略、自然“构建块”表示与精确的量子力学计算相结合,提出了一?#20013;?#39062;的结构搜索方案。他们以C60团簇在二氧化钛(101)表面的吸附结构研究为例证明了该方法的准确性。其预测的吸附结构与实验观测很好的吻合。不仅如此他们还通过上述方法得到分子与表面的作用机理,理解了稳定吸附结构的成因。该研究提出的方法可以进一步推广用于分子聚集体和薄膜等大尺度表面吸附结构的研究

Applicability of PS algorithm can now restore full spectral and full spatial resolution AFM-IR dataset. A team led by Olga S. Ovchinnikova from the Center for Nanophase Materials Science, Oak Ridge National Laboratory, USA, applied a coupled non-negative matrix factorization (CNMF) pan-sharpening (PS) algorithm for AFM-IR data to enable rapid reconstruction of high spatial resolution hyperspectral chemical imaging data. They discussed the influence of the parameter affecting the result such as downsampling rate, number of components used for decomposition as well as number of fixed wavenumber maps involved in dataset restoration. Finally, they showcased the application of PS CNMF algorithm for the correlative analysis of plant cell walls in identifying the relationship between local mechanical properties and chemical composition. Their method drastically decreases time required to acquire spectral images while simultaneously providing multicomponent analysis capability. Such approaches can be readily adopted for other spectral imaging techniques utilized in chemical imaging of complex materials.

Application of pan-sharpening algorithm for correlative multimodal imaging using AFM-IR (全色锐化算法在AFM-IR相关多模态?#19978;?#20013;的应用)
Nikolay BorodinovNatasha BilkeyMarcus FostonAnton V. IevlevAlex BelianinovStephen JesseRama K. VasudevanSergei V. Kalinin & Olga S. Ovchinnikova 
npj Computational Materials 5:49 (2019)
doi:s41524-019-0186-z
Published online:16 April 2019
Abstract| Full Text | PDF OPEN

摘要:原子力显微镜与红外光谱(AFM-IR)的耦合提供了独特的能力,可对各种材?#31995;?#23616;部化学和物理组成作纳米分辨的表征。然而,为了充分利用AFM-IR的测量能力,需要取得3D数据集(具有光谱维度的2D图),常规的AFM扫描要达到相同的空间分辨率会非常耗?#34180;?#26412;研究提出了一种基于多组分全色锐化算法?#21019;?#29702;光谱AFM-IR数据的新方法。该方法仅需要低空间分辨率光谱和有限数量的高空间分辨?#23454;?#27874;数化学图,即可产生高空间分辨率的高光谱图像,可极大地减少数据采集时间。基于此,我们能够得到高分辨率的成分分布图,在光谱范围内的任何波数处生成化学图,并可对样品的物理和化学性?#24335;?#34892;相关分析。本研究以植物细胞壁?#19978;?#20316;为模型系统来突显本方法的作用,并?#20801;?#26679;品的力学刚度与其化学成分之间的相互作用。我们相信我们的全色锐化方法可以更广泛地应用于不同类别的材料,从而更深入地研究纳米尺度的结构-性能关系   

Abstract: The coupling of atomic force microscopy with infrared spectroscopy (AFM-IR) offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution. However, in order to fully utilize the measurement capability of AFM-IR, a three-dimensional dataset (2D map with a spectroscopic dimension) needs to be acquired, which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan. In this paper, we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm. This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image, greatly reducing data acquisition time. As a result, we are able to generate high-resolution maps of component distribution, produce chemical maps at any wavenumber available in the spectral range, and perform correlative analysis of the physical and chemical properties of the samples. We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition. We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale. 

Editorial Summary

Multimodal imaging using AFM-IR: Pan-sharpening algorithmAFM-IR相关多模态?#19978;瘢?#20840;色锐化算法

本研究证明了全色锐化算法在恢复全光谱和全空间分辨率AFM-IR数据集中的适用性。来自美国橡树岭国家实验室纳米材料科学中心的Olga S. Ovchinnikova教授应用AFM-IR数据的耦合非负矩阵分解(CNMF)全色锐化(PS)算法,实现了高空间分辨率、高光谱化学?#19978;?#25968;据的快速重建。他们讨论了诸如下采样率(downsampling rate)、用于分解的组分数量、数据集恢复所涉及的固定波数图数量等因素对结果的影响。最后,该研究展示了全色锐化-非负矩阵分解算法在植物细胞壁相关分析中的应用,?#33539;?#20102;局部力学性质与化学组分之间的关系。这一方法极大地减少了获取光谱图像所需的时间,同时提供了多组分分析能力。使用这些方法即可借助其他光谱?#19978;竇际?#24456;容易地实现复杂材?#31995;?#21270;学?#19978;?/span>

Applicability of PS algorithm can now restore full spectral and full spatial resolution AFM-IR dataset. A team led by Olga S. Ovchinnikova from the Center for Nanophase Materials Science, Oak Ridge National Laboratory, USA, applied a coupled non-negative matrix factorization (CNMF) pan-sharpening (PS) algorithm for AFM-IR data to enable rapid reconstruction of high spatial resolution hyperspectral chemical imaging data. They discussed the influence of the parameter affecting the result such as downsampling rate, number of components used for decomposition as well as number of fixed wavenumber maps involved in dataset restoration. Finally, they showcased the application of PS CNMF algorithm for the correlative analysis of plant cell walls in identifying the relationship between local mechanical properties and chemical composition. Their method drastically decreases time required to acquire spectral images while simultaneously providing multicomponent analysis capability. Such approaches can be readily adopted for other spectral imaging techniques utilized in chemical imaging of complex materials.

Analyzing machine learning models to accelerate generation of fundamental materials insights (分析机器学习模型以加速对基础材?#31995;?#35748;识)
Mitsutaro UmeharaHelge S. SteinDan GuevarraPaul F. NewhouseDavid A. Boyd & John M. Gregoire 
npj Computational Materials 5:34 (2019)
doi:s41524-019-0172-5
Published online:8 March 2019
Abstract| Full Text | PDF OPEN

摘要:材料科学的机器学习设想通过自动识别关键数据之间的关系来扩充人类对于规律的解释,获得科学的认知,从而加速基础科学研究。科学家的主要作用是从数据中提取基础知识,我们证明,通过分析训练的神经网络模型本身,而非将其作为预测工具应用,可以加速这种提取。卷积神经网络在多维参数空间中复杂数据关系(如通过组合材料科学实验得到的复杂数据)的建模方面具有优势。测量一种给定材料空间中的性能指标,可提供有关(局部)最佳材?#31995;?#30452;接信息,但不会给出引起性能变化背后的机理。通过建立模型基于材料参数(如本文中组合物和拉曼信号)来预测材料性能(太阳能燃料光阳极的光电化学发电),进而对训练模型的梯度分析,我们揭示了人工观察或传统统计分析不易识别的关键数据关系。并通过对这些关键关系的阐释进一步了获取本质的理解,由此展示了通过机器学习结合人类科学家的分析来加速数据解释的一种框架。我们还演示了使用神经网络梯度分析来自动预测参数空间中的优化方向(如添加特定的合金元素),其可突破数据限?#35780;?#25552;高材?#31995;?#24615;能   

Abstract: Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing dat. 

Editorial Summary

Analyzing machine learning models to accelerate generation of fundamental materials insights分析机器学习模型加速材?#31995;?#22522;础认识

研究训练了一种卷积神经网络模型,以模拟高维材料参数空间中复杂数据关系。来自美国加州理工学院的John M. Gregoire领导的团队,使用他们训练的卷积神经网络预测了BiVO4基光阳极的光电化学性能。他们利用高通量实验获得的1379个光阳极样品的组成和拉曼光谱来训练神经网络模型。该模型的梯度能有效地可视化材料参数空间中特定区域的数据规律,以及整个数据集的数据规律。梯度自动分析为材料研究提供了指导,包括如何超越现有数据集的限制,以进一?#25945;?#39640;材料性能。这种解释机器学习模型的方法加速了人们对材料科学的认识,并揭示了科学发现的自动化途径

A convolutional neural networks model is trained to model complex data relationships in high-dimensional materials parameter spaces. A team led by John M. Gregoire from the California Institute of Technology predicted photoelectrochemical performance of BiVO4-based photoanodes using their trained convolutional neural networks. The composition and Raman spectrum of 1379 photoanode samples obtained from high-throughput measurements were used to train the model. Gradients from this model enabled effective visualization of data trends at specific locations in the materials parameter space as well as collectively for the entire dataset. Automated analysis of the gradients provides guidance for research, including how to move beyond the confines of the present dataset to further improve performance. This approach to interpreting machine learning models accelerates scientific understanding and illustrates avenues for continued automation of scientific discovery.

Unlocking the potential of weberite-type metal fluorides in electrochemical energy storage (释放氟铝镁钠石型金属氟化物在电化学储能中的潜力)
Holger EuchnerOliver Clemens & M. Anji Reddy 
npj Computational Materials 5:31 (2019)
doi:s41524-019-0166-3
Published online:6 March 2019
Abstract| Full Text | PDF OPEN

摘要:?#35780;?#23376;电池(NIBs)是?#22411;?#21462;代锂离子电池(LIB)的替代电池?#38469;?#20013;的先行者,然而?#35780;?#23376;电池的比能量明显低于锂离子电池,这主要是由于钠?#24230;?#22411;正极材料具有?#31995;?#30340;?#20174;?#30005;位和较高的分子量。NIB要想与LIB的高能量密度竞争,它就需要高电压的正极材料。本研究报告了对Weberite型?#24179;?#23646;氟化物(SMF)的理论研究,该氟化物是一?#20013;?#22411;的高电压和高能量密度的材料,迄今为止?#24418;?#20316;为NIB的正极材料而被研究。Weberite型结构对于含钠过渡金属氟化物非常有利,其中多种过渡金属组合(MM')均属于相应的Na2MM'F7结构。本工作通过计算研究了一系列具有Weberite型结构的已知和假设的化合物,以评估它们作为NIB正极材?#31995;?#28508;力。WeberiteSMF?#20801;?#20986;Na+扩散的二维路径,具有异常低的活化能垒。高能量密度与Na+的低扩散?#35780;?#32467;合,使得这种类型的化合物?#22411;?#25104;为NIB正极材?#31995;?#20505;选   

Abstract:Sodium-ion batteries (NIBs) are a front-runner among the alternative battery technologies suggested for substituting the state-of-the-art lithium-ion batteries (LIBs). The specific energy of Na-ion batteries is significantly lower than that of LIBs, which is mainly due to the lower operating potentials and higher molecular weight of sodium insertion cathode materials. To compete with the high energy density of LIBs, high voltage cathode materials are required for NIBs. Here we report a theoretical investigation on weberite-type sodium metal fluorides (SMFs), a new class of high voltage and high energy density materials which are so far unexplored as cathode materials for NIBs. The weberite structure type is highly favorable for sodium-containing transition metal fluorides, with a large variety of transition metal combinations (M, M’) adopting the corresponding Na2MM’F7 structure.. A series of known and hypothetical compounds with weberite-type structure were computationally investigated to evaluate their potential as cathode materials for NIBs. Weberite-type SMFs show two-dimensional pathways for Na+ diffusion with surprisingly low activation barriers. The high energy density combined with low diffusion barriers for Na+ makes this type of compounds promising candidates for cathode materials in NIBs. 

Editorial Summary

New hope of sodium-ion batteries: Weberite-type metal fluoridesNa离子电池的新希望:weberite型金属氟化物

该研究考查了一系列拟作为NIB正极材?#31995;?/span>weberite?#24179;?#23646;氟化物。来自德国乌尔?#27867;?#22982;霍兹研究所M. Anji Reddy领导的研究小组,筛查了一些真实和虚拟的化合物,以揭示weberite金属氟化物作为NIB正极材?#31995;?#28508;力。虽然他们将研究限定于考查仅一定数量的化合物,但这些材?#31995;?#33539;围及对它们的各?#20013;?#39280;的可能性将非常大。除了不同元素组合外,通过多种物种填充每个金属亚晶格也可能是有意义的,这些策略可促进更快的扩散路径的形成同时又保持高的能量密度,以实现化合物的进一?#25509;?#21270;。按照这一策略,他们建议将一些高能量密度的材料与一定量的Ti合金化,以产生快速扩散通道。他们的研究从理论角度证明了这些材料具有作为NIB正极的潜力,作者希望未来的研究会开启这些化合物的合成和实验测试

A series of weberite-type sodium metal fluorides as cathode materials for NIBs have investigated. A group led by M. Anji Reddy from the Helmholtz Institute Ulm, Germany, screened real and virtual compounds revealing the potential of weberite-type metal fluorides as cathode materials for NIBs. They limited their study to the investigation of only a certain number of compounds, but the playground for these materials in combination with their variety of possible modifications might be even larger. Apart from other element combination, they highlighted that it may also be of interest to populate each of the metal sublattices by more than one species, which could allow for further optimization of the compounds by facilitating faster diffusion pathways while maintaining high energy density. Following this strategy, they suggested to alloy some high-energy density materials with a certain amount of Ti to create fast diffusion channels. With the potential of these materials being demonstrated from the theoretical viewpoint, the authors aim to trigger the synthesis and experimental testing of these compounds in future studies.

Topological superconducting phase in high-Tc superconductor MgB2 with Dirac–nodal-line fermions (Tc超导体MgB2中的拓扑超导相具有Dirac节点线费米子)
Kyung-Hwan JinHuaqing HuangJia-Wei MeiZheng LiuLih-King Lim & Feng Liu 
npj Computational Materials 5:57 (2019)
doi:s41524-019-0191-2
Published online:3 March 2019
Abstract| Full Text | PDF OPEN

摘要:拓扑超导体是一种有趣且难以捉摸的量子相,具?#22411;?#25169;保护的无带隙表面/边缘态特征,存在于体材超导带隙中,包含了Majorana费米子。不幸的是,所有目前已知的拓扑超导体转变温度都非常低,限制了Majorana费米子的实验测量。本研究发现,在众所周知的传统高温超导体MgB2中存在拓?#35828;依?#20811;节线态。第一性原理计算表明,受空间反演和时间反演对称?#21592;?#25252;的Dirac节点线结构具有独特的一维色散特征,连接着电子和空位Dirac态。最重要的是,我们用传统的s波超导带隙实现了拓扑超导相,用MgB2薄膜的拓扑边?#30340;?#24335;证明了?#20013;员?#32536;状态。我们的这一发现可以在高温下实现对Majorana费米子的实验测量   

Abstract:Topological superconductors are an intriguing and elusive quantum phase, characterized by topologically protected gapless surface/edge states residing in a bulk superconducting gap, which hosts Majorana fermions. Unfortunately, all currently known topological superconductors have a very low transition temperature, limiting experimental measurements of Majorana fermions. Here we discover the existence of a topological Dirac–nodal-line state in a well-known conventional high-temperature superconductor, MgB2. First-principles calculations show that the Dirac–nodal-line structure exhibits a unique one-dimensional dispersive Dirac–nodal line, protected by both spatial-inversion and time-reversal symmetry, which connects the electron and hole Dirac states. Most importantly, we show that the topological superconducting phase can be realized with a conventional s-wave superconducting gap, evidenced by the topological edge mode of the MgB2 thin films showing chiral edge states. Our discovery may enable the experimental measurement of Majorana fermions at high temperature. 

Editorial Summary

Topological superconducting phase in high-Tc superconductor MgB2 with Dirac–nodal-line fermionsTc超导体MgB2中的拓扑超导相具有Dirac节点线费米子

本研究在高温超导体MgB2中揭示了一种有趣的反演和时间反演对称保护的Dirac节点线态。来自由美国犹他大学和中国量子物质协同创新中心的刘锋领导的团队,使用第一性原理计算和模型分析,揭示了这种Dirac节点线态。最重要的是,他们用传统的s波超导带隙实现了拓扑超导相,用MgB2薄膜的拓扑边?#30340;?#24335;证明了?#20013;员?#32536;状态。他们的发现为在前所?#20174;?#30340;高温下研究拓扑超导相提供了一个令人兴奋的机会,并可能为构建新型量子和自旋电子器件,提供?#26143;?#36884;的材料平台。有可能在高温下实现对Majorana费米子的实验测量,将激发未来更广泛的超导材料拓扑相(如蜂窝状层状晶格结构)研究

An intriguing inversion and time-reversal symmetry- protected Dirac nodal line state is revealed in a high-temperature superconductor MgB2. A team led by Feng Liu from the University of Utah, USA, and Collaborative Innovation Center of Quantum Matter, China, performed first-principles calculations to discover the existence of a topological Dirac–nodal-line state in a well-known conventional high-temperature superconductor, MgB2. Most importantly, they showed that the topological superconducting phase can be realized with a conventional s-wave superconducting gap, evidenced by the topological edge mode of the MgB2 thin films showing chiral edge states. Their finding provokes an exciting opportunity to study a topological superconducting phase in an unprecedented high temperature and may offer a promising material platform to building novel quantum and spintronics devices. The authors’ discovery may enable the experimental measurement of Majorana fermions at high temperature. And it will stimulate future studies of topological phases in a broader range of superconducting materials, such as a honeycomb lattice layered structure.

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