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期刊介绍
  《npj 计算材料学》是在线出版、完全开放获取的国际学术期刊。发表结合计算模拟与设计的材料学一流的研究成果。本刊由中国科学院上海硅酸盐研究所与英国自然出版集团(Nature Publishing Group,NPG)以伙伴关系合作出版。
  主编为陈龙庆博士,美国宾州大学材料科学与工程系、工程科学与力学系、数学系的杰出教授。
  共同主编为陈立东研究员,中国科学院上海硅酸盐研究所研究员高性能陶瓷与超微结构国家重点实验室主任。
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Online search tool for graphical patterns in electronic band structures (电子能带结构图形图案的在线搜索工具)
Stanislav S. BorysovBart OlsthoornM. Berk GedikR. Matthias Geilhufe & Alexander V. Balatsky
npj Computational Materials 4:46 (2018)
doi:s41524-018-0104-9
Published online:20 August 2018
Abstract| Full Text | PDF OPEN

摘要:许多功能材料以其电子能带结构具有的特定模式为特征,例如,Dirac材料以能带的线性交叉为特征,拓扑绝缘体以“墨西哥帽”图案为特征,有效自由电子气以抛物线分散为特征。为了成功找到这些材料的电子能带特征图形图案,手动检查少量材料的电子能带结构还是比较容易的。然而现代电子能带结构数据库中的数据量不断增加,手动查找已不切实际。为了解决这个问题,本研究开发了一种图形图案的自动搜索工具,用来在有机材料数据库(Organic Materials Database)的电子能带结构中搜索。该工具能够通过在线高通量计算,于数千个能带结构集合中,找到用户指定的图形图案。使用这一工具,只需几秒钟即可为26,739个有机晶体在费米面附近的10个电子能带内找到任意图形图案。开发工具适用于任何其他电子能带的结构数据库,而且免费提供源代码   

Abstract:Many functional materials can be characterized by a specific pattern in their electronic band structure, for example, Dirac materials, characterized by a linear crossing of bands; topological insulators, characterized by a “Mexican hat” pattern or an effectively free electron gas, characterized by a parabolic dispersion.To find material realizations of these features, manual inspection of electronic band structures represents a relatively easy task for a small number of materials. However, the growing amount of data contained within modern electronic band structure databases makes this approach impracticable. To address this problem, we present an automatic graphical pattern search tool implemented for the electronic band structures contained within the Organic Materials Database. The tool is capable of finding user-specified graphical patterns in the collection of thousands of band structures from high-throughput calculations in the online regime. Using this tool, it only takes a few seconds to find an arbitrary graphical pattern within the ten electronic bands near the Fermi level for 26,739 organic crystals. The source code of the developed tool is freely available and can be adapted to any other electronic band structure database. 

Editorial Summary

New materials: online searching (新材料:在线搜索) 

该研究开发了一种依据电子能带结构中所选图形图案,从数据库中寻找候选材料的在线搜索工具。瑞典Nordita、KTH皇家理工学院和斯德哥尔摩大学的Alexander V. Balatsky教授的团队提供了一个在线搜索工具,用于查找有机材料数据库中包含的电子能带结构数据中的某些图形图案。该工具能在几秒钟内从数据库数千个带结构的集合内、费米面附近的十个电子能带中,找到任意给定的图形图案。电子能带结构的图形图案可作为Dirac材料、拓扑绝缘体、自由电子气等的特征。该工具可对无法手动检查的大量带结构进行自动在线分析

An online search tool is developed to look for candidate materials by exploring chosen graphical patterns in their electronic band structure from a database.A team led by Alexander V. Balatsky from Nordita, KTH Royal Institute of Technology and Stockholm University present an online search tool to find certain graphical patterns in the electronic band structure data contained within the Organic Materials Database.The tool is capable of finding an arbitrary graphical pattern within the ten electronic bands near the Fermi level in the collection of thousands of band structures in the database within a few seconds.The graphical patterns can be features of Dirac materials, topological insulators, free electron gas, etc. This tool allows for an automatic online analysis for a large collection of band structures where manual inspection is impractical.

Multiobjective genetic training and uncertainty quantification of reactive force fields(多目标遗传训练和反应力场的不确定度量化)
Ankit MishraSungwook HongPankaj RajakChunyang ShengKen-ichi NomuraRajiv K. KaliaAiichiro Nakano & Priya Vashishta
npj Computational Materials 4:42 (2018)
doi:s41524-018-0098-3
Published online:02 August 2018
Abstract| Full Text | PDF OPEN

摘要:ReaxFF反应力场方法极大地扩展了反应分子动力学模拟在各种材料性质和过程中的适用性。其参数一般都经过了大量的训练,以适用于一组预定义的量子力学数据,但在应用于复杂化学反应时,仍然无法准确地描述好我们感兴趣的量。本研究提出了一种基于多目标遗传算法的动态方法,用于训练ReaxFF的参数和量化我们感兴趣的物理量的不确定度。ReaxFF参数的训练是通过直接拟合反应分子动力学轨迹与量子分子动力学轨迹的动力学过程来进行的,在这个过程中,多个感兴趣量的Pareto最优前沿为不确定度的量化提供了一系列ReaxFF模型。我们的原位多目标遗传算法工作流程利用进程间通信消除了文件I/O瓶颈,实现了可扩展性。高温硫化是化学气相沉积合成MoS2层的必要反应步骤,我们将原位多目标遗传算法工作流程应用于H2S前驱体对MoO3的高温硫化。这项研究为远离平衡的化学过程提出了一种新的反应分子动力学模拟方法,该方法在提供误差范围的同时定量再现了量子分子动力学的模拟   

Abstract:The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes.ReaxFF parameters are commonly trained to fit a predefined set of quantum-mechanical data, but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions.Here, we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest.ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories ReaxFF on the fly,where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification.Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications.The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO3 by H2S precursor, which is an essential reaction step for chemical vapor deposition synthesis of MoS2layers.Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes, which quantitatively reproduces quantum molecular dynamics simulations while providing error bars. 

Editorial Summary

Molecular dynamics: multi-objective genetic algorithms for training and uncertainty quantification(分子动力学:用于训练和量化不确定度的多目标遗传算法) 

多目标遗传算法允许训练力场参数和量化不确定度,同时对分子动力学代码的修改是最小的。来自美国南加利福尼亚大学的中野爱一郎教授领导的团队,使用了一种基于反应分子动力学(RMD)模拟的算法来训练反应力场参数和对模拟量进行了不确定性的量化。他们以化学气相沉积MoS2单层的合成为例,对量子分子动力学(QMD)模拟进行了力场参数的训练。从一个128原子MoO3-H2S系统出发,通过估算在QMD模拟过程中H-S、Mo-O和Mo-S键的数量作为时间的函数来研究反应动力学。通过与RMD模拟结果的比较,他们发现RMD可以在误差范围内定量地再现QMD

Multi-objective genetic algorithms allow training and uncertainty quantification of force-field parameters with minimal modifications of molecular dynamics codes. A team led by Aiichiro Nakano at University of Southern California used an algorithm based on reactive molecular dynamics (RMD) simulations for the training of reactive force-field parameters and uncertainty quantification of simulated quantities of interest.Chemical vapor deposition synthesis of MoS2 monolayer was set as a specific example, and force-field parameters were trained against quantum molecular dynamics (QMD) simulations.Starting from a 128-atom MoO3–H2S system, the reaction dynamics were investigated by estimating the numbers of H–S, Mo–O, and Mo–S bonds as a function of time during the QMD simulations.By comparing these quantities of interest with those obtained in the RMD simulations, it was found that RMD can quantitatively reproduce QMD within an error bar.

Mapping the elastic properties of two-dimensional MoS2 via bimodal atomic force microscopy and finite element simulation (通过双模态原子力显微镜和有限元模拟描绘二维MoS2 的弹性性质)
Yuhao LiChuanbin YuYingye GanPeng JiangJunxi YuYun OuDai-Feng ZouCheng HuangJiahong WangTingting JiaQian LuoXue-Feng YuHuijuan ZhaoCun-Fa Gao & Jiangyu Li
npj Computational Materials 4:49 (2018)
doi:s41524-018-0105-8
Published online:29 August 2018
Abstract| Full Text | PDF OPEN

摘要:二维(2D)材料的弹性是材料的基本力学特性,在其应用及应变工程方面起关键作用。然而,精确测量2D材料的弹性模量却仍是一个挑战,而且传统的悬空测量法存在不少不足之处。本研究展示了一种新方法,可以高空间分辨地描绘在衬底上沉积的单层和双层MoS2的面内杨氏模量。我们采用双模态原子力显微镜精确地描绘了扫描探针针尖和样品之间的有效弹簧常数,并发展了有限元方法来定量地考查基底刚度对二维材料变形的影响。使用这些方法,可将单层MoS2的面内杨氏模量与衬底的分开,并测定为265±13 GPa,与先前的报道基本一致,但不确定性显著降低。研究还发现单层和双层MoS2的弹性不能区分,并通过第一原理计算证实了这点。该方法为直接测量衬底上的2D材料面内杨氏模量提供了一种方便、稳健和准确的方法   

Abstract:Elasticity is a fundamental mechanical property of two-dimensional (2D) materials, and is critical for their application as well as for strain engineering. However, accurate measurement of the elastic modulus of 2D materials remains a challenge, and the conventional suspension method suffers from a number of drawbacks.In this work, we demonstrate a method to map the in-plane Young’s modulus of mono- and bi-layer MoS2 on a substrate with high spatial resolution.Bimodal atomic force microscopy is used to accurately map the effective spring constant between the microscope tip and sample, and a finite element method is developed to quantitatively account for the effect of substrate stiffness on deformation.Using these methods, the in-plane Young’s modulus of monolayer MoS2 can be decoupled from the substrate and determined as 265?±?13?GPa, broadly consistent with previous reports though with substantially smaller uncertainty.It is also found that the elasticity of mono- and bi-layer MoS2 cannot be differentiated, which is confirmed by the first principles calculations.This method provides a convenient, robust and accurate means to map the in-plane Young’s modulus of 2D materials on a substrate. 

Editorial Summary

Elastic properties: quantitative mapping of MoS2 Young’s modulus (弹性性质:MoS2杨氏模量的定量描绘) 

该研究可在支撑衬底上直接对2D材料的面内杨氏模量进行稳健而准确地测定。来自中科院深圳先进技术研究院和华盛顿大学的李江宇教授团队开发了一种新方法,用于高空间分辨地描绘衬底上的2D材料的面内杨氏模量。考虑到单层和多层MoS2的情况,他们将双模态原子力显微镜(AFM)与有限元方法结合使用,以测量AFM探针针尖和样品之间的有效弹簧常数,同时兼顾区分衬底的作用。测定出单层MoS2的面内杨氏模量为265±13 GPa,不确定性小于5%,而双层MoS2的面内杨氏模量与单层的面内杨氏模量却无法区分开来。这一结果也为第一性原理所验证

A robust and accurate determination of the in-plane Young’s modulus of 2D materials can be accomplished directly on the supporting substrate. A team at the Shenzhen Institutes of Advanced Technology and University of Washingtondeveloped a method to map the in-plane Young’s modulus of 2D materials supported on a substrate with high spatial resolution.Considering the case of mono- and by-layered MoS2, bimodal atomic force microscopy (AFM) was used in combination with the finite element method to map the effective spring constant between the AFM tip and the sample, whist also accounting for the effect of the substrate.The in-plane Young’s modulus of monolayer MoS2 was found to be 265?±?13?GPa, with less than 5% uncertainty, whereas that of bilayer MoS2 is indistinguishable from the monolayer counterpart.

Interplay between epidermal stem cell dynamics and dermal deformation (表皮干细胞动力学与皮肤变形之间的相互作用)
Yasuaki KobayashiYusuke YasugahiraHiroyuki KitahataMika WatanabeKen Natsuga & Masaharu Nagayama
npj Computational Materials 4:45 (2018)
doi:s41524-018-0101-z
Published online:20 August 2018
Abstract| Full Text | PDF OPEN

摘要:生命系统的形态变化由组织生长驱动。虽然屈曲不稳定性被认为是启动这种生长系统形成空间格局的关键因素,但屈曲不稳定性之外,人们对持续形态变化的基本原理却知之甚少。在哺乳动物皮肤中,真皮具有许多朝向表皮的突起,在突起的顶端通常分布着表皮干细胞。尽管屈曲理论联系有增殖细胞的真皮层和基底层可以很好地解释最初的不稳定性,但并不能决定那些突起的朝向,也不能决定表皮干细胞的空间分布。本研究在真皮和基底膜组成的可变形基底上,引入了基于粒子的、自我复制的细胞模型,并研究了真皮变形与真皮上表皮干细胞分布之间的关系。我们的模型再现了真皮突起从真皮长向表皮的形成过程,以及表皮干细胞在真皮突起顶端的优先分布,这是基础的屈曲机制所无法解释的。我们认为,特定类型的细胞与基底膜之间的粘附强度,是影响这些干细胞分布的关键因素   

Abstract:Tissue growth is a driving force of morphological changes in living systems. Whereas the buckling instability is known to play a crucial role for initiating spatial pattern formations in such growing systems, little is known about the rationale for succeeding morphological changes beyond this instability.In mammalian skin, the dermis has many protrusions toward the epidermis, and the epidermal stem cells are typically found on the tips of these protrusions.Although the initial instability may well be explained by the buckling involving the dermis and the basal layer, which contains proliferative cells, it does not dictate the direction of these protrusions, nor the spatial patterning of epidermal stem cells.Here we introduce a particle-based model of self-replicating cells on a deformable substrate composed of the dermis and the basement membrane, and investigate the relationship between dermal deformation and epidermal stem cell pattering on it.We show that our model reproduces the formation of dermal protrusions directing from the dermis to the epidermis, and preferential epidermal stem cell distributions on the tips of the dermal protrusions, which the basic buckling mechanism fails to explain.We argue that cell-type-dependent adhesion strengths of the cells to the basement membrane are crucial factors influencing these patterns. 

Editorial Summary

Epidermal renewal: How cells find their place (表皮更新:细胞如何找到它们的位置) 

我们的表皮每小时损失180万个皮肤细胞。这种损失要通过在表皮-真皮界面(即基底膜)处产生的新细胞来填补,这个基底膜有很多特殊的微笑皮肤突起。来自日本北海道大学的MasaharuNagayama团队,采用一种基于粒子的模型模拟了表皮更新。使用这种方法,作者发现持续的细胞分裂导致基底膜拥挤。依据能量消耗,细胞要么从基底膜上脱离,要么使基底膜变形继续留在其中,这样便形成真皮突起。该模型还解释了为什么干细胞会优先存在于这些突起的顶端。他们的模型不依赖于真皮之外的信号,可以作为其他系统分布格局形成的通用研究方法

Our epidermis loses 1.8 million skin cells per hour. This loss is balanced by the generation of new cells at the epidermal-dermal interface, the basement membrane, which features characteristic dermal protrusions.A team led by MasaharuNagayama at Hokkaido University in Japan emulates epidermal renewal using a particle-based model.Using this approach, the authors uncover that constant cell division leads to crowding at the basement membrane.Depending on the energy cost, cells will either detach from the membrane or can be accommodated by deforming the membrane, thereby forming dermal protrusions.The model also explains observations that stem cells are preferentially found at the tips of these protrusions.The model does not rely on external signals from the dermis and could serve as a generic mechanism to other pattern-forming systems.

Entropy contributions to phase stability in binary random solid solutions (熵对二元随机固溶体相稳定性的贡献)
Anus ManzoorShubham PandeyDebajit ChakrabortySimon R. Phillpot & Dilpuneet S. Aidhy 
npj Computational Materials 4:47 (2018)
doi:s41524-018-0102-y
Published online:22 August 2018
Abstract| Full Text | PDF OPEN

摘要:高熵合金含有多种元素而且比例很大,容易导致相分离。这些合金通常具有浅混合焓,导致了相似级别的熵贡献。因此,合金的相稳定性平等地取决于混合焓和混合熵,了解二者各自对热力学性质的贡献就成为关键。在设计高熵合金的总体框架中,本研究使用密度泛函理论计算,阐明了振动熵、电子熵和位形熵对二元合金相稳定性的贡献。研究表明,与振动熵和位形熵相比,电子熵的贡献非常小,并且在合金的相稳定性中不起重要作用。位形熵和振动熵既能破坏固溶体的稳定又可共助固溶体的稳定。因此,即使那些具有负混合焓的系统也可能表现出相不稳定性,呈现混溶带隙;相反,由于熵贡献,那些具有正混合焓的系统却可以是相稳定的。我们认为,与有序金属间化合物不同,综合考虑各种熵贡献,对于具有浅混合焓的稳定、单相高熵合金的计算预测理论框架的发展来说,非常重要   

Abstract:High entropy alloys contain multiple elements in large proportions that make them prone to phase separation.These alloys generally have shallow enthalpy of mixing which makes the entropy contributions of similar magnitude. As a result, the phase stability of these alloys is equally dependent on enthalpy and entropy of mixing and understanding the individual contribution of thermodynamic properties is critical. In the overall vision of designing high entropy alloys, in this work, using density functional theory calculations, we elucidate the contributions of various entropies, i.e., vibrational, electronic and configurational towards the phase stability of binary alloys. We show that the contribution of electronic entropy is very small compared to the vibrational and configurational entropies, and does not play a significant role in the phase stability of alloys. The configurational and vibrational entropies can either destabilize or can collectively contribute to stabilize the solid solutions. As a result, even those systems that have negative mixing enthalpy can show phase instability, revealed as a miscibility gap; conversely, systems with positive mixing enthalpy can be phase stable due to entropic contributions. We suggest that including entropic contributions are critical in the development of theoretical framework for the computational prediction of stable, single-phase high entropy alloys that have shallow mixing enthalpies, unlike ordered intermetallics. 

Editorial Summary

Phase stability: entropy enabled predictions (相稳定性:启用熵来预测) 

位形熵和振动熵虽然一直以来被忽视,但现在本研究指出,二者可能确定了合金的相稳定性。来自美国怀俄明大学的DilpuneetAidhy教授等,采用密度泛函理论计算,检验了不同类型的熵在7种具有浅混合焓的二元金属合金中的贡献。他们发现依靠混合焓来预测固溶体稳定性是不够的。虽然电子熵对任何合金的相稳定性没有显著贡献,但是位形熵稳定了固溶体,同时振动熵在不同合金系统中起促进或破坏固溶体稳定的作用。振动熵是衡量原子键柔软度的指标,研究它在不同体系中的变化方式,可能有助于我们更好地预测单相多组分合金的稳定性。

Configurational and vibrational entropies, while conventionally neglected, can determine an alloy’s phase stability. A team led by DilpuneetAidhy at the University of Wyoming in U.S.A. used density functional theory calculations to examine the contribution of different types of entropy in seven binary metallic alloys with shallow enthalpies of mixing. They found that relying on the enthalpy of mixing to predict solid solution stability was insufficient. While electronic entropy did not meaningfully contribute to phase stability in any of the alloys, configurational entropy stabilized solid solutions, while vibrational entropy either stabilized or destabilized solid solutions depending on the alloy system. Vibrational entropy is a measure of the softness of atomic bonds, and research into how it varies in different systems may help us better predict single-phase multicomponent alloy stability.

Ductile deformation mechanism in semiconductor α-Ag2S(α-Ag2S半导体的延展变形机制)
Guodong LiQi AnSergey I. MorozovBo DuanWilliam A. Goddard IIIQingjie ZhangPengcheng Zhai & G. Jeffrey Snyder 
npj Computational Materials 4:44 (2018)
doi:s41524-018-0100-0
Published online:13 August 2018
Abstract| Full Text | PDF OPEN

摘要:无机半导体α-Ag2S在室温下表现出类似金属的延展性,但产生这种高延展性的机理尚未得到充分研究。基于密度泛函理论模拟α-Ag2S的本征力学性质研究表明,其潜在的延展性机制归因于以下三个因素:(i)低理想剪切强度和压力作用下的多个滑移路径,(ii)α-Ag2S八边形框架易于滑动而不需要破坏Ag-S键,(iii)金属Ag-Ag键的形成抑制Ag-S框架滑移并将其有效地耦合。α-Ag2S中的易滑路径(或键合的原子易重排而不破坏化学键)为半导体材料的塑性变形机制提供了新视角,这将有利于设计和开发柔性半导体材料和电子器件   

Abstract:Inorganic semiconductor α-Ag2S exhibits a metal-like ductile behavior at room temperature, but the origin of this high ductility has not been fully explored yet. Based on density function theory simulations on the intrinsic mechanical properties of α-Ag2S, its underlying ductile mechanism is attributed to the following three factors: (i) the low ideal shear strength and multiple slip pathways under pressure, (ii) easy movement of Ag–S octagon framework without breaking Ag?S bonds, and (iii) a metallic Ag?Ag bond forms which suppresses the Ag–S frameworks from slipping and holds them together.The easy slip pathways (or easy rearrangement of atoms without breaking bonds) in α-Ag2S provide insight into the understanding of the plastic deformation mechanism of ductile semiconductor materials, which is beneficial for devising and developing flexible semiconductor materials and electronic devices. 

Editorial Summary

Semiconductors: easy slip explains silver sulfide ductility(硫化银半导体:易滑性解释了延展性) 

虽然半导体通常是脆性的,但立方硫化银(α-Ag2S)中的原子键却是柔性的,在室温下即具有延展性。来自武汉理工大学的李国栋博士及其美国和俄罗斯的合作者,采用密度泛函理论模拟,研究了压力下α-Ag2S中Ag-S之间的键稳定性。他们发现,沿特定方向的剪切变形,会扭曲α-Ag2S中由Ag-S键形成的八边形框架,同时它还会产生新的Ag-Ag键以进一步耦合Ag-S框架,使α-Ag2S在变形过程中保持结构稳定。他们还发现Ag2S沿着两个不同晶面具有低理想剪切强度,这促进了原子滑移,同时保持了原子框架的完整性。半导体延展性的原子尺度机制研究,将有助于我们更好地理解和设计柔性电子产品。

While semiconductors are usually brittle, the atomic bonds in cubic silver sulfide (α-Ag2S) are flexible, making it ductile at room temperature.Guodong Li from Wuhan University of Technology and colleagues in the USA and Russia used density function theory simulations to examine the bonds between silver and sulphur in α-Ag2S under pressure.They found that shear deformation along specific directions distorted the octagons formed by the Ag-S bonds, while it also created new Ag-Ag bonds to couple the Ag-S octagons, enabling α-Ag2S to retain its structure during deformation.They also found low ideal shear strength along two crystallographic planes, which promoted easy atomic slip while maintaining the integrity of the atomic framework.Research into the atomic origins of ductility in semiconductors may help us better understand and design flexible electronics.

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning (用机器学习开发锆马氏体相变的原子间势)
Hongxiang Zong (宗洪祥)Ghanshyam PilaniaXiangdong Ding(丁向东教授)Graeme J. Ackland & Turab Lookman
npj Computational Materials 4:48 (2018)
doi:s41524-018-0103-x
Published online:24 August 2018
Abstract| Full Text | PDF OPEN

摘要:原子模拟为理解结构相变的物理机制提供了有效手段。但因经典的原子间作用势很难同时描述多型键合,因此对于某些具有同素异构转变的相变金属而言,揭示其结构相变的物理过程仍然面临挑战。基于机器学习(ML)技术,本研究开发了一种机器学习与相变理论相结合的方法(ML-AIMD),该方法使得描述相变的原子间作用势能够从头算分子动力学模拟(AIMD)中高保真地学习得到。我们以锆作为模型系统,证明了该方法的可行性和有效性。具体而言,我们基于ML-AIMD方法开发的原子间作用势所预测的声子、弹性常数和堆垛层错能等关键材料属性与实验或者第一性原理的计算结果相一致,并能够正确地描述金属锆中不同相结构之间的转变机制和预测压力-温度相图   

Abstract:Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations. However, this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of bonding.Based on machine-learning (ML) techniques, we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations (AIMD).Using zirconium as a model system, for which an adequate semiempirical potential describing the phase transformation process is lacking, we demonstrate the feasibility and effectiveness of our approach.Specifically, the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons, elastic constants, as well as stacking fault energies.Molecular dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure–temperature phase diagram of zirconium. 

Editorial Summary

Interatomic potentials: predicting phase transformations in zirconium (原子间作用势:预测锆的相变) 

机器学习揭示了纯锆的原子间作用势,并预测锆的相变行为。来自中国西安交通大学丁向东教授(长江学者)课题组的宗洪祥博士和美国洛斯阿拉莫斯国家实验室的TurabLookman教授共同领导的团队,采用高斯型机器学习方法开发相变金属的原子间作用势,并基于此预测了纯锆的多型结构相变行为。他们通过局部原子环境的变化来表达每种原子的能量贡献,如键长、形状和体积,并基于机器学习模型成功地描述了纯锆的物理特性。进一步,通过大规模分子动力学模拟成功的预测出了纯锆在温度和压力共同作用下的相稳定性:锆的相图,该相图与之前的实验和模拟结果一致。使用机器学习开发描述相变系统的原子间势,可以帮助我们更好地模拟复杂系统。

Machine learning leads to a new interatomic potential for zirconium that can predict phase transformations. A team led by HongxianZong at Xi’an Jiaotong University, China, and TurabLookman at Los Alamos National Laboratory, U.S.A,used a Gaussian-type machine learning approach to produce an interatomic potential that predicted phase transformations in zirconium.They expressed each atomic energy contribution via changes in the local atomic environment, such as bond length, shape, and volume.The resulting machine-learning potential successfully described pure zirconium’s physical properties.When used in molecular dynamics simulations, it predicted a zirconium phase diagram as a function of both temperature and pressure that agreed well with previous experiments and simulations.Developing learnt interatomic potentials in phase-transforming systems could help us better simulate complex systems.

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials(用机器学习-数据驱动法识别多晶材料的小疲劳裂纹驱动力)
Andrea RovinelliMichael D. SangidHenry Proudhon & Wolfgang Ludwig
npj Computational Materials 4:35 (2018)
doi:s41524-018-0094-7
Published online:16 july 2018
Abstract| Full Text | PDF OPEN

摘要:小裂纹的扩展是导致结构部件进入疲劳期的主要因素。尽管人们对此有很大的兴趣,但就裂缝扩展的方向和速度而言,小裂缝的生长标准尚未确定。本研究提出了一种识别微结构小疲劳裂纹驱动力的新方法。采用贝叶斯网络和机器学习技术可识别微机械和微结构变量,以及它们对疲劳裂纹的扩展方向和扩展速率的影响关系。多模态数据集结合了多晶聚合体内原位扩展的小裂纹的高分辨率4D实验数据和晶体塑性模拟数据,用来提供训练数据。相关变量构成解析表达式的基础,因此代表方向和速率方程的小裂纹驱动力。我们对该表达式捕获所观察实验行为的能力作了量化,并与直接来自贝叶斯网络的结果和文献中常见的疲劳指标作了比较。结果表明,采用所提出的分析模型可靠地预测了小裂纹扩展的方向,比其他疲劳指标更为有利   

Abstract:The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented.Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation.A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data.The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation.The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature.Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics. 

Editorial Summary

Crack propagation: machine learning identifies micromechanical variables(裂缝扩展:机器学习识别微机械变量) 

机器学习技术可以识别钛合金中小裂纹扩展方向背后的复杂变量。由美国普渡大学Michael Sangid领导的团队,采用机器学习建立了两个独立的贝叶斯网络,分析了钛合金原位疲劳循环过程中获得的衍射数据和X线断层影像数据。第一主应力轴在特定方向上的取向和最大分辨剪切应力,与裂纹扩展最为相关,将其纳入关系分析中,用以描述裂纹扩展方向的概率。该分析表达式再现了实验结果,比以前文献的预测更为可靠。这种半监督机器学习方法可以帮助我们识别其他复杂工程问题中的驱动力。

A machine learning technique can identify the complex variables behind the propagation direction of small cracks in a titanium alloy.A team led by Michael Sangid at Purdue University in the U.S.A built two separate Bayesian networks using machine learning to analyse diffraction and tomography data acquired during in situ fatigue cycling of a titanium alloy.The orientation of the first principal stress axis in a specific direction and the maximum resolved shear stress were the most strongly correlated with crack propagation, and were incorporated into an analytical relationship to describe the probability of the crack propagation direction.This analytical expression reproduced experimental results and was more reliable than previous literature predictions. This sort of semi-supervised machine learning methodology may help us identify driving forces in other complex engineering problems.

Phase-field model of pitting corrosion kinetics in metallic materials(金属材料点腐蚀动力学的相场模型)
Talha Qasim AnsariZhihua XiaoShenyang HuYulan LiJing-Li Luo & San-Qiang Shi
npj Computational Materials 4:38 (2018)
doi:s41524-018-0089-4
Published online:24 july 2018
Abstract| Full Text | PDF OPEN

摘要:点腐蚀是最具破坏性的腐蚀形式之一,可导致结构的灾难性破坏。本研究提出了一种热力学一致性相场模型,用以定量预测金属材料中的点腐蚀动力学。引入一个序参数来表示金属-电解质系统内金属的物理状态。系统的自由能由离子浓度和序参数来描述。电解质中的离子传输和在电解质/金属界面处的电化学反应都被明确地包含在分析当中。离子浓度分布和序参数随时间的演变,是由系统的总自由能的减少而驱动,通过数值求解控制方程而获得。为了用实验校准本模型,我们建立了腐蚀过电位和动力学金属界面参数的直接关系。本模型被用于研究几个腐蚀问题,如两个点腐蚀坑间距的影响、残余应力的作用、陶瓷颗粒增强钢的腐蚀行为及其晶体取向对腐蚀速率的作用   

Abstract:Pitting corrosion is one of the most destructive forms of corrosion that can lead to catastrophic failure of structures. This study presents a thermodynamically consistent phase field model for the quantitative prediction of the pitting corrosion kinetics in metallic materials.An order parameter is introduced to represent the local physical state of the metal within a metal-electrolyte system.The free energy of the system is described in terms of its metal ion concentration and the order parameter.Both the ion transport in the electrolyte and the electrochemical reactions at the electrolyte/metal interface are explicitly taken into consideration.The temporal evolution of ion concentration profile and the order parameter field is driven by the reduction in the total free energy of the system and is obtained by numerically solving the governing equations.A calibration study is performed to couple the kinetic interface parameter with the corrosion current density to obtain a direct relationship between overpotential and the kinetic interface parameter.The phase field model is validated against the experimental results, and several examples are presented for applications of the phase-field model to understand the corrosion behavior of closely located pits, stressed material, ceramic particles-reinforced steel, and their crystallographic orientation dependence. 

Editorial Summary

Corrosion: phase field method reproduces complex situations(腐蚀:相场法再现复杂情况) 

本研究通过建立包含阳极和阴极反应的相场模型,可以成功地模拟金属材料的腐蚀。来自香港理工大学的石三强教授领导的团队,用相场法模拟金属腐蚀形貌的演变,研究了不锈钢浸入温和盐水后的点腐蚀坑的生长。作者开发了几种方程式,考虑了多种离子的迁移和浓度因素,引入了一个序参数来描述腐蚀界面的变化。由此构建的模型模拟了从介观到宏观尺度的腐蚀过程,成功地演示了两个腐蚀坑可以聚结形成更宽的腐蚀坑,残余应力的作用,以及腐蚀沿不同晶体取向的发展速率。这个模型可以被用于研究扩散控制,或者界面反应控制的腐蚀过程。成功地模拟复杂的腐蚀过程,可以帮助我们更好地理解腐蚀并减少其影响。

Stainless steel corrosion can be successfully simulated using a phase field model that takes into account anodic and cathodic reactions. A team led by San Qiang Shi at the Hong Kong Polytechnic University adapted a phase field method used for simulating metallic microstructure evolution to study corrosive pit growth in stainless steel immersed in mildly salted water.The authors developed equations taking into account the transport and concentration of ionic species and introduced a orderparameter to describe the changing corrosion interface.The resulting model simulated the corrosion process from the meso- to the macroscale, and successfully showed that two pits can coalesce to form a wider pit, and that residual stress affected corrosion rate, while corrosion occurred preferentially along specific crystallographic planes.Successfully simulating complex corrosion process may help usbetter understand its mechanisms and lessen its impact.

Nanometer-scale gradient atomic packing structure surrounding soft spots in metallic glasses (金属玻璃中软区周围的纳米级梯度原子堆垛结构)
Binbin WangLiangshun LuoEnyu GuoYanqing SuMingyue WangRobert O. RitchieFuyu DongLiang WangJingjie Guo & Hengzhi Fu
npj Computational Materials 4:41 (2018)
doi:s41524-018-0097-4
Published online:30 july 2018
Abstract| Full Text | PDF OPEN

摘要:对于金属玻璃(非晶合金)来说,无定形结构中原子堆垛的隐藏序,及其如何提供塑性项的起源,长期以来一直是理解这一材料塑性变形机理的关键所在。为了解决这一问题,我们采用分子动力学计算模拟的研究方法,构建了数种非晶合金的三维模型,并且基于每一原子周围配位多面体的几何阻挫差异,将非晶结构的原子共分为六个不同的种类。对于不同体系的非晶合金,在纳米尺度范围内,都存在着“梯度原子堆垛结构”,也就是说非晶态的局域结构表现为一种从原子松散堆垛到致密堆垛的梯度演变,原子的性能同样表现为一种梯度的变化。据此,非晶合金中存在三个可识别的区域:类固态区、过渡区以及类液态区,每一个区域都具有各自专属类型的原子。此外,我们还证明了类液态原子与剪切转变的关联性最强,过渡态原子次之,而类固态原子对剪切转变贡献最低。与“GUMs”模型不同的是,我们的模型考虑了中程序的作用,进而给出了明确的”软区“结构,即类液态原子和它们近邻原子的组合,这将更有利于定量地比较不同非晶合金中”软区“的数量,为非晶合金独特的变形行为提供合理的解释   

Abstract:The hidden order of atomic packing in amorphous structures and how this may provide the origin of plastic events have long been a goal in the understanding of plastic deformation in metallic glasses. To pursue this issue, we employ here molecular dynamic simulations to create three-dimensional models for a few metallic glasses where, based on the geometrical frustration of the coordination polyhedra, we classify the atoms in the amorphous structure into six distinct species, where “gradient atomic packing structure” exists. The local structure in the amorphous state can display a gradual transition from loose stacking to dense stacking of atoms, followed by a gradient evolution of atomic performance. As such, the amorphous alloy specifically comprises three discernible regions: solid-like, transition, and liquid-like regions, each one possessing different types of atoms. We also demonstrate that the liquid-like atoms correlate most strongly with fertile sites for shear transformation, the transition atoms take second place, whereas the solid-like atoms contribute the least because of their lowest correlation level with the liquid-like atoms. Unlike the “geometrically unfavored motifs” model which fails to consider the role of medium-range order, our model gives a definite structure for the so-called “soft spots”, that is, a combination of liquid-like atoms and their neighbors, in favor of quantifying and comparing their number between different metallic glasses, which can provide a rational explanation for the unique mechanical behavior of metallic glasses. 

Editorial Summary

Metallic glasses: gradient atomic packing and plasticity (金属玻璃:梯度原子堆垛与塑性) 

梯度原子堆垛结构中的类液态原子可能决定了金属玻璃的塑性。来自中国哈尔滨工业大学的苏彦庆教授和美国加州大学伯克利分校的Robert Ritchie教授领导的团队,依据原子的堆垛方式,采用分子动力学模拟对不同体系金属玻璃中的原子进行分类研究,从而理解了非晶合金中的中程有序。他们发现局域原子结构,凭借类液态区、过渡区和类固体区,逐渐从松散的原子堆垛转变为致密的原子堆垛,类液态原子和它们近邻原子一起构成“软区”,并与局部不可逆原子重排的萌发密切相关。这种梯度原子堆垛结构模型可以帮助我们更好地理解和设计非晶合金的塑性行为。

Liquid-like atoms in a gradient atomic packing structure might determine plasticity in metallic glasses. A team led by Yanqing Su at the Harbin Institute of Technology in China and Robert Ritchie at the University of California, Berkeley, in the USA, used molecular dynamics simulations to classify atoms in different metallic glasses according to their stacking, thereby accounting for the amorphous medium-range order in metallic glasses. They found that local atomic structures gradually transitioned from a loose to a dense stacking of atoms via liquid-like, transition, and solid-like regions, and that liquid-like atoms and their neighbors were equivalent to ‘soft spots’ and associated with initiation of local irreversible atomic arrangements. Modeling this gradient atomic packing structure may help us better understand and design plasticity in glassy alloys.

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