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Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks(基于数据加强和深度神经收集对X射线衍射小型数据集的快速和可注释分类)
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)数据采集与阐发是新型薄膜材料研发周期中最耗时的措施之一。本研究提出了一种基于机械进修的方式,用于从有限数量的薄膜XRD图谱中预测晶体学维度和空间群。基于无机晶体布局数据库(ICSD)和测验考试数据的模仿数据,咱们将监视机械进修方式与模子无关的、物理消息输入的数据加强策略相连系,降服了新材料斥地固有的稀缺数据问题。作为实测案例,本研究合成115种逾越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。在测试了各类算法之后,咱们斥地、完成了一个全卷积神经收集,其交叉验证的维数和空间群分类的精确度别离达到93%和89%。根据全体平均汇集层算计,咱们提出了平均分类激活图,以便答应人们对测验考试模子成果作充实的注释、对分类错误的根来历根底因作出阐明。最后,咱们系统地评估了发生预测精度丧失的最大XRD图案步长(数据采集速度)为2θ=0.16°,因而仅需5.5分钟以至更短时间就能够获得一张XRD图谱并对其分类   

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:快速和可注释的分类

快速材料表征对于高通量新材料摸索很是次要。X射线衍射(XRD)图谱的获取和阐发是当前高通量材料测验考试筛选的瓶颈之一。针对上述问题,来自麻省理工学院和新加坡的研究团队成长了一种基于监视机械进修的框架用于快速获得和识别新型薄膜材料的XRD图谱。他们起首按照ICSD数据库中164种薄膜卤化物和115种测验考试合成薄膜的XRD图谱建立了一个数据库。基于这个小型库成长了一个与模子无关的、物理消息输入的数据扩展方式用于建立锻炼数据集。进而采用该数据集锻炼了一个卷积神经收集用于XRD图谱分类,其维度和空间群分类精确率别离可达9389%。本研究提出的方式能够成功处理新材料摸索固有的数据稀缺问题,能够大概快速地(在5.5分钟以内)获得一个新材料的XRD图谱并对其进行分类

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征受自旋轨道耦合节制的现象,自旋轨道耦合耦合了电子的轨道自由度和自旋自由度。本文证了然强自旋轨道耦合磁界面的反常霍尔效应能够经由外加电场进行调谐。经由改变反演对称性破缺的强度,电场改变了Rashba相互传染感动,而Rashba相互传染感动反过来又改变了Berry曲率的大小,而Berry曲率是决定反常霍尔电导率的环节物理量。成果表白,在方点阵模子的小电场传染感动下,反常霍尔电导率呈二次相关干系。对新近成长的铱酸盐界面,即(SrIrO3)1/(SrMnO3)1(001)布局进行了显式密度泛函算计发觉,该布局无论有无电场均暗示出很强的电场依赖性。该效应在自旋电子学使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

把持外加电场对Rashba自旋轨道相互传染感动进行批改,能够调理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,算计了特定界面(SIO)1/(SMO)1布局的反常霍尔电导率电场依赖性的次要贡献文章地址于接近费米能量的能带交叉点。此外,AHC能够经由掺杂来调控电子态,从而完成调整反常霍尔电导率。他们为申明该成果,使用了铁磁晶格模子,并使用了比来成长的亚锰酸盐-铱酸盐界面 [(SIO)1/(SMO)1(001)]的密度泛函算计。现实上,比来的一些测验考试曾经发觉了氧化物异质布局中反常霍尔电导率随电场变化的证据。因而,从理论上和测验考试长进一步成长这类效应,并着眼于潜在的自旋电子学使用将是极具价值的

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 (基于机械进修的硅藻基因型与表型对应联系的硅藻基因工程深度数据阐发)
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,是在必然前提下制造出具有奇异征质的材料的一种有弘远前途的工程路径。硅藻的生物矿化,是单细胞藻类使用二氧化硅建立的细胞壁,这类细胞壁虽是微米级的,但其诸多特征性布局是纳米级的,是无望用于光学、传感、过滤和药物递送等范畴的合成功能材料,是这些范畴惹人注方针候选材料。因而,针对这些使用,经由定向遗传润色完成可控的硅藻布局编削,前途广漠。本研究中,咱们在硅藻(伪矮海链藻)中,采用基因敲除技术建立颠末基因润色的藻株,使藻体形态发生变化,使用监视机械进修完成了基因型变化与表型变化对应联系咱们斥地了人工神经收集(NN区分野生基因敲除型硅藻,NN可根据硅藻壳的SEM照片所展现的由特定蛋白质(Thaps3_21880)惹起的表型变化进行分辨区分,分辨区分精确度94。类激活映照使物理变化可视化,答应NN区分硅藻藻株,随后筛查找到节制孔的特定基因。进一步建立了另一个NN以批量处置图像数据,主动识别刚毛毛孔,提取毛孔相关参数。使用多变量数据可视化(CrossVis)对象可视化所提取的参数之间的类相互干系,并答应间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:机械进修

该研究比力了一种野生型和基因敲除型硅藻,以揭示编削的基因型和表达的表型之间的相互传染感动,由于基因操作能够使这些生物体被间接用作出格定制的纳米布局和微布局材料。来自美国橡树林国度测验考试的Olga S. Ovchinnikova带领的研究团队,经由敲除思疑可能与硅藻壳构成相关的基因来编削硅藻基因型,并经由扫描电镜表征该基因惹起的表型变化。他们使用图像处置和机械进修分类算法(人工NN)来筛选影响硅藻表型的基因,并将野生型硅藻与基因润色型区分隔来。就节制毛孔形态的蛋白检测来说,他们的识别野生型和基因润色型硅藻的NN,检测精确度为94%。为注释基于NN的分类表观精确率,他们用类激活图(CAM)来凸起显示收集使用的图像区域,发觉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隔的不变特征。随后,他们建立了另一个神经收集,特地针对毛孔并提取其参数。这类主动化特征提取过程使人们能够大概将遗传润色与硅藻形态对应联系干系起来。这一方式确定了由给定的遗传润色发生的藻壳布局的变化,为生物矿化过程供给了生物学探测才能

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 (协同改正的从头算构成焓)
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:若何精确算计构成焓是从头算材料设想的一个功能。使用尺度密度泛函理论对几类材料系统(如氧化物)算计时,会获得一些错误的成果。本研究基于比来邻阳离子-阴离子键的数量,提出了“协调校正焓”的方式(CCE),不只校正焓,又一次能校正多晶型的相对不变性。CCE使用PerdewBurke-ErnzerhofPBE)、局部密度近似(LDA)和强束缚和得当规范(SCAN)互换相关泛函,连系准谐波Debye模子来处置零点振动和热效应。在二元和三元氧化物(卤化物)长进行的基准测试成果显示,所有函数的室温成果都很是精确,用SCAN算计获得的最小平均绝对误差为27(24)meV/atom。这个误差对构成焓的零点振动和热贡献很小,并且不合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预测化合物不变性的算计误差:协同改正

该研究基于比来邻阳离子-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,能够处理预测化合物的热力学不变性时的误差。来自美国杜克大学的Stefano Curtarolo带领的团队,使用三种校正算计方式:Perdew-Burke-ErnzerhofPBE)、局部密度近似(LDA)和强束缚和得当规范(SCAN)互换相关泛函的算计,连系准谐波Debye模子来校正717)三元氧化物(卤化物)的零点振动和热效应,别离给出了3849),2974)和2724meV / atomMAE极为精确的校正构成焓。他们用准谐波Debye模子处置零点温度和有限温度的振动时,发觉振动在很大程度上被消弭了,误差比以前的方式要小得多。CCE获得切确的构成焓,平均绝对误差小至20-30 meV /原子。该方式简单且易于扩展到其他系统如氮化物、磷化物或硫化物等材料。本方式可用于预测依赖于切确构成焓的各类性质,例如电池电压、缺陷能量和高熵材料的构成。由于CCE考虑了化学键的连接和拓扑布局,因而它又一次能够改正给定组分的不合布局的相对不变性

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 (设想和发觉纳米布局热电材料的算计策略)
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:理论算计和预测在前辈高机能热电材料的成长中阐扬越来越次要的贡献,并成功地指导测验考试理解并完成破记载的好成果。本文从理论算计的角度,综述了近年来高机能纳米布局体热电材料的研究进展。提高热电机能的一个无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。咱们提出了几个次要的策略和环节的例子,凸起了基于第一性道理的算计在揭示热电机能协同优化的复杂但易于处置的干系方面的贡献。分析优化方式为改良材料供给了四重设想策略:1)经由多尺度分层架构显著降低晶格热导率;2)经由本征矩阵的电子能带简并工程大幅提高塞贝克系数;3)经由主相和第二相之间的带边外形调控载流子迁移率;4)经由最大化加强非谐振动和声子Gruneisen参数来设想具有本征低导热率的材料。这些组合效应能够在降低晶格热导率的同时提高功率因子。本综述对理论若何影响该范畴的现状供给了更好的理解,并有助于指导将来高机能热电材料的研究   

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纳米布局热电材料:设想和发觉

该综述描述了四种典型的算计策略在提高纳米布局体相热电机能方面的使用。来自美国西北大学Christopher Wolverton带领的研究小组分析了比来的次要研究进展,揭示了纳米布局热电体相材料设想和发觉的算计策略的规律。到今朝为止,曾经用高ZT > 2证了然几种体积热电材料的优异热电机能。所有这些高ZT优值的材料都文雅地暗示了PGEC的概念。出格是,把持最小电子散射和最大限度地把持纳米布局方式的全长尺度热载流子散射的连系,完成了很多材料ZT优值的提高。纳米布局方式集成为很多调用多尺度声子散射的新思惟:包罗原子尺度合金化、内生纳米布局和中尺度颗粒鸿沟节制,并以协同的编制连系了能带对齐和简并工程方式。这类分析方式也是一种将ZT提高到3的最合理方式。在追求更高的ZT优值时,第一性道理算计对于供给理论注释、材料选择以至ZT预测都是至关次要的

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 (功能材料华夏子布局的贝叶斯预测)
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:订制前辈无机/无机异质器件使其合适预期技术使用的功能特征,需方式会器件内部的微观布局并能对其调控。原子尺怀抱子力学模仿方式能够针对具体材料布局给出切确预测的能量和性质,然而,经由算计的布局仍然比力坚苦。本研究提出了一种基于“建筑模块”的贝叶斯优化布局搜刮(BOSS)方式,用于处理扩展的无机/无机界面问题,并证了然其在分子概况吸附研究中的可行性。在BOSS中,贝叶斯模子经由主动进修采样的原子构象快速确定材料的势能面。这使咱们能够大概在TiO2 锐钛矿相的(101)面上确定C60的几种最无益的分子吸附布局,并阐明节制布局拆卸的环节分子-概况相互传染感动。预测的布局与测验考试图像很是分歧,证了然BOSS的优良预测才能,并为分子堆积体和薄膜的大尺度概况吸附研究斥地了道路   

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功能材猜中的原子布局:贝叶斯预测

该研究针对无机无机材料界面布局预测提出基于“布局块”的贝叶斯布局搜刮方案。由芬兰阿尔托大学Milica Todorovic等带领的团队,将人工智能采样策略、天然“建立块”暗示与切确的量子力学算计相连系,提出了一种新鲜的布局搜刮方案。他们以C60团簇在二氧化钛(101)概况的吸附布局研究为例证了然该方式的精确性。其预测的吸附布局与测验考试观测很好的吻合。不只如斯他们又一次经由上述方式获得分子与概况的传染感动机理,理解了不变吸附布局的成因。该研究提出的方式能够进一步推广用于分子堆积体和薄膜等大尺度概况吸附布局的研究

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相关多模态成像中的使用)
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)的耦合供给了奇异的才能,可对各类材料的局部化学和物理构成作纳米分辩的表征。然而,为了充实把持AFM-IR的丈量才能,需要取得3D数据集(具有光谱维度的2D图),常规的AFM扫描要达到不异的空间分辩率会很是耗时。本研究提出了一种基于多组分全色锐化算法来处置光谱AFM-IR数据的新方式。该方式仅需要低空间分辩率光谱和有限数量的高空间分辩率单波数化学图,即可发生高空间分辩率的高光谱图像,可极大地削减数据采集时间。基于此,咱们能够大概获得高分辩率的成分分布图,在光谱范畴内的任何波数处生成化学图,并可对样品的物理和化学性质进行相关阐发。本研究以动物细胞壁成像作为模子系统来突显本方式的传染感动,并显示样品的力学刚度与其化学成分之间的相互传染感动。咱们相信咱们的全色锐化方式能够更遍及地使用于不合类此外材料,从而更深刻地研究纳米尺度的布局-机能干系   

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相关多模态成像:全色锐化算法

本研究证了然全色锐化算法在恢复全光谱和全空间分辩率AFM-IR数据会合的合用性。来自美国橡树岭国度测验考试室纳米材料科学核心的Olga S. Ovchinnikova教授使用AFM-IR数据的耦合非负矩阵分化(CNMF)全色锐化(PS)算法,完成了高空间分辩率、高光谱化学成像数据的快速重建。他们会商了诸如下采样率(downsampling rate)、用于分化的组分数量、数据集恢复所涉及的固定波数图数量等要素对成果的影响。最后,该研究展现了全色锐化-非负矩阵分化算法在动物细胞壁相关阐发中的使用,确定结局部力学性质与化学组分之间的干系。这一方式极大地削减了获取光谱图像所需的时间,同时供给了多组分阐发才能。使用这些方式即可借助其他光谱成像技术很容易地完成复杂材料的化学成像

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 (阐发机械进修模子以加快对底子材料的认识)
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:材料科学的机械进修设想经由主动识别环节数据之间的干系来扩充人类对于规律的注释,获得科学的认知,从而加快底子科学研究。科学家的次要传染感动是从数据中提取底子学问,咱们证明,经由度析锻炼的神经收集模子本身,而非将其作为预测对象使用,能够加快这类提取。卷积神经收集在多维参数空间中复杂数据干系(如经由组合材料科学测验考试获得的复杂数据)的建模方面具有劣势。丈量一种给定材料空间中的机能目标,可供给相关(局部)最佳材料的间接消息,但不会给出惹起机能变化背后的机理。经由建立模子基于材料参数(如本文中组合物和拉曼信号)来预测材料机能(太阳能燃料光阳极的光电化学发电),进而对锻炼模子的梯度阐发,咱们揭示了人工察看或传全盘计阐发不易识此外环节数据干系。并经由对这些环节干系的阐释进一步了获取本质的理解,由此展现了经由机械进修连系人类科学家的阐发来加快数据注释的一种框架。咱们又一次演示了使用神经收集梯度阐发来主动预测参数空间中的优化标的目的(如添加特定的合金元素),其可冲破数据限制来提高材料的机能   

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阐发机械进修模子加快材料的底子认识

研究锻炼了一种卷积神经收集模子,以模仿高维材料参数空间中复杂数据干系。来自美国加州理工学院的John M. Gregoire带领的团队,使用他们锻炼的卷积神经收集预测了BiVO4基光阳极的光电化学机能。他们把持高通量测验考试获得的1379个光阳极样品的构成和拉曼光谱来锻炼神经收集模子。该模子的梯度能无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。梯度主动阐发为材料研究供给了指导,包罗若何超出现无数据集的限制,以进一步提高材料机能。这类注释机械进修模子的方式加快了人们对材料科学的认识,并揭示了科学发觉的主动化路子

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:钠离子电池(NIBs)是无望代替锂离子电池(LIB)的替代电池技术中的先行者,然而钠离子电池的比能量较着低于锂离子电池,这主如果由于钠嵌入型正极材料具有较低的反映电位和较高的分子量。NIB要想与LIB的高能量密度互助,它就需要高电压的正极材料。本研究演讲了对Weberite型钠金属氟化物(SMF)的理论研究,该氟化物是一种新型的高电压和高能量密度的材料,迄今为止尚未作为NIB的正极材料而被研究。Weberite型布局对于含钠过渡金属氟化物很是无益,此中多种过渡金属组合(MM')均属于响应的Na2MM'F7布局。本工作经由算计研究了一系列具有Weberite型布局的已知和假设的化合物,以评估它们作为NIB正极材料的潜力。WeberiteSMF显示出Na+扩散的二维路径,具有非常低的活化能垒。高能量密度与Na+的低扩散势垒连系,使得这类类型的化合物无望成为NIB正极材料的候选   

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离子电池的新但愿:weberite型金属氟化物

该研究考查了一系列拟作为NIB正极材料的weberite钠金属氟化物。来自德国乌尔姆亥姆霍兹研究所M. Anji Reddy带领的研究小组,筛查了一些实在和虚拟的化合物,以揭示weberite金属氟化物作为NIB正极材料的潜力。虽然他们将研究限制于考查仅必然命量的化合物,但这些材料的范畴及对它们的各类润色的可能性将很是大。除了不合元素组合外,经由多种物种填充每个金属亚晶格也可能是成心义的,这些策略可推动更快的扩散路径的构成同时又保持高的能量密度,以完成化合物的进一步优化。按照这一策略,他们建议将一些高能量密度的材料与必然量的Ti合金化,以发生快速扩散通道。他们的研究从理论角度证了然这些材料具有作为NIB正极的潜力,作者但愿将来的研究会开启这些化合物的合成和测验考试测试

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:拓扑超导体是一种风趣且难以捉摸的量子相,具有拓扑庇护的无带隙概况/边缘态特征,具有于体材超导带隙中,包含了Majorana费米子。倒霉的是,所有今朝已知的拓扑超导体改变温度都很是低,限制了Majorana费米子的测验考试丈量。本研究发觉,在家喻户晓的保守高温超导体MgB2中具有拓扑狄拉克节线态。第一性道理算计表白,受空间反演和时间反演对称性庇护的Dirac节点线布局具有奇异的一维色散特征,连接着电子和空位Dirac态。最次要的是,咱们用保守的s波超导带隙完成了拓扑超导相,用MgB2薄膜的拓扑边缘模式证了然手性边缘情况。咱们的这一发觉能够在高温下完成对Majorana费米子的测验考试丈量   

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中揭示了一种风趣的反演和时间反演对称庇护的Dirac节点线态。来自由美国犹他大学和中国量子物质协同立异核心的刘锋带领的团队,使用第一性道理算计和模子阐发,揭示了这类Dirac节点线态。最次要的是,他们用保守的s波超导带隙完成了拓扑超导相,用MgB2薄膜的拓扑边缘模式证了然手性边缘情况。他们的发觉为在史无前例的高温下研究拓扑超导相供给了一个令人兴奋的机缘,并可能为建立新型量子和自旋电子器件,供给有前途的材料平台。有可能在高温下完成对Majorana费米子的测验考试丈量,将激发将来更遍及的超导材料拓扑相(如蜂窝状层状晶格布局)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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