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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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  《npj 计算材料学》是在线出版、完全开放获取的国际...
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Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning 由从头建模和机器学习法共同预测B2金属间化合物缺陷行为 

Bharat Medasani, Anthony Gamst, Hong Ding, Wei Chen, Kristin A Persson, Mark Asta, Andrew Canning & Maciej Haranczyk
npj Computational Materials
 2, Article number: 1 (2016)
doi:10.1038/s41524-016-0001-z
Published online:09 December 2016
Abstract| Full Text | PDF OPEN

摘要:利用带立方B2晶体结构(等原子化合物AB)系统作为实例,本研究提出了一种结合机器学习和高通量计算来预测二元金属间化合物(A–B)点缺陷行为。就我们所知,这是首次应用机器学习模型来研究点缺陷属性的工作。采用高通量第一性原理密度泛函理论计算了100 B2型金属间化合物的本征点缺陷能量。该系统化合物可分为两组:(i本征缺陷为错位,AB均富含的缺陷,及(ii)以空穴为主的缺陷,是AB中,或者AB有的缺陷。数据采用决策树机制的机器学习技术进行分析,之后还作了全部和缩减的多加性回归树(MART)模型分析。这三个分析方案中,缩减MART (r-MART)模型使用六个描述符(形成能量、电子密度的Wigner–Seitz小室边界最小量和差额量、原子半径差、最大原子数、最大电负性),计算结果显示了最高的匹配度(98 %)和预测精度(75 %)。该模型可以用来预测其他B2化合物的缺陷行为,我们发现,系统中45 %的化合物是以空穴缺陷为特征的,是AB,或两者都有的缺陷。预测材料中占主导地位的缺陷类型,对金属间化合物的热力学和动力学性能的建模非常重要。本研究结果证实,使用现代工具,结合高通量计算和数据分析,可以实现材料缺陷类型的预测。 

Abstract: We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic (A–B) compounds, using as an example systems with the cubic B2 crystal structure (with equiatomic AB stoichiometry). To the best of our knowledge, this work is the first application of machine learning-models for point defect properties. High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds. The systems are classified into two groups: (i) those for which the intrinsic defects are antisites for both A and B rich compositions, and (ii) those for which vacancies are the dominant defect for either or both composition ranges. The data was analyzed by machine learning-techniques using decision tree, and full and reduced multiple additive regression tree (MART) models. Among these three schemes, a reduced MART (r-MART) model using six descriptors (formation energy, minimum and difference of electron densities at the Wigner–Seitz cell boundary, atomic radius difference, maximal atomic number and maximal electronegativity) presents the highest fit (98 %) and predictive (75 %) accuracy. This model is used to predict the defect behavior of other B2 compounds, and it is found that 45 % of the compounds considered feature vacancies as dominant defects for either A or B rich compositions (or both). The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds, and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics. 

  Editorial Summary

  Machine learning a defect’s effect (机器学习可以有效预测缺陷类型)

  美国研究人员开发了一种快速预测材料中占主导地位的平衡原子级缺陷的方法。原子间的组合规则和对称安排,决定了由其构成的结晶材料的诸多属性。因此,缺少一个或出现一个杂质原子可以明显地改变这些属性。被称为密度泛函理论计算的量子物理方法,已被证明是一个预测这些点缺陷影响的强有力的方法。然而,材料的性能都是由点缺陷引起的,这个强大方法的应用若没有强大的计算能力,将难以高通量地筛选成千上万种材料的各种性能。来自劳伦斯伯克利国家实验室的Bharat Medasani及其同事,应用机器学习与密度泛函理论的上百个计算,可使这个筛选过程更快。他们通过检测一族二元金属间化合物合金的性能,证实了这种方法的高效性。

  A method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a missing or an impurity atom can noticeably change these properties. A quantum physics method known as density functional theory calculations has proven to be a powerful method for predicting the influence of these so-called point defects. However, the brute-force application of these methods requires significant computing power, thus hindering its application in high throughput screening of thousands of materials for properties influenced by point defects. Bharat Medasani from the Lawrence Berkeley National Laboratory and co-workers combine machine learning with a few hundred density functional theory calculations to make this process much faster. They demonstrate the power of their approach by examining the properties of a family of binary intermetallic alloys.  

Charge carrier transition in an ambipolar single-molecule junction: Its mechanical-modulation and reversibility (双极性单分子结的载流子转变:机械调制与可逆性) 

摘要:自下而上地精确调控材料的功能,对于开发分子级电子器件来说非常重要,然而目前在单分子规模上操控载流子仍然面临挑战。问题的根源是,载流子的性质往往因其分子系统复杂而难以研究。通过从头计算模拟,本研究发现,带有金-环丙烷-1,2-二巯基化物-金结构的双极性单分子结,可用机械力-调制而开关。分子中的环丙烷环可由机械力的作用而重复、可逆地关闭或打开,使双极性载流子从p-型变化为n-型。电子结构分析法清楚地揭示了C–S成键和载流子性质之间的力依赖性关系。在此基础上,本研究设计了一个二元互连,在机械力调制下,可表现出电阻、整流和负微分电阻功能,即表现出加载/卸载或拉/推的效应。这个有趣的现象在分子层面上既为分子载流子性能提供了解释,又为载流子调控赋予了可行性,也给单分子器件中的单分子结提供了总体认识和实用调控方法。 

Abstract: Precise control from the bottom-up for realizing tunable functionality is of utmost importance to facilitate the development of molecular electronic devices. Until now, however, manipulating charge carriers over single-molecule scale remains intractable. The origin of the problem is that the nature of charge carriers is often hindered by the complexity of the investigated molecular systems. Here, via ab initio simulations, we show a force-modulated and switched ambipolar single-molecule junction with Au/cyclopropane-1,2-dithiol/Au structure. The cyclopropane ring in the molecule can be opened and closed reversibly and repeatedly by the mechanical force. This structural transition from its closed state to open state enables the ambipolarity in charge carriers—from p-type to n-type. Analysis of electronic structure reveals unambiguously the force-dependent correlation between C–S bond order and the nature of charge carriers. Based on this, we design a binary interconnected junction exhibiting resistance, rectification and negative differential resistance functionalities under mechanical modulation, i.e., loading/unloading or pull/push. This interesting phenomenon provides both illuminating insight and feasible controllability into charge carriers in molecules, and a very general idea and useful approach for single-molecule junctions in practical single-molecule devices. 

  Editorial Summary

  Single-molecule switch for modular electronics (模块化电子器件的单分子开关)

  在连有金原子的分子结上施加机械力,可导致其电性能的可逆转换,将促进单分子器件的开发。在分子水平上构建电子器件有可能会开发出超小的电子电路。在单分子尺度精确地控制它们的功能就是一个挑战。中国中山大学的Zheng Yue及其同事通过计算机模拟证明,连接两个金原子的环丙烷-1,2-二巯基化物分子环在机械力反复作用下,可以持续地、可逆地打开和关闭,通过分子内的电子供体和受体转变,使电性能得以开关。以这样的分子作为模块化积木,他们设计了一种多功能接头,受机械力作用后,表现出电阻(升高电压出现电流增大)、整流(交流电转换为直流电)和负微分电阻(升高电压导致电流降低)。

  Applying force to a junction linking gold atoms leads to reversible switching of its electric properties, facilitating development of single-molecule devices. Building electronics from the molecular level up could lead to drastically smaller electronic circuits. Precisely controlling their functionality at the single-molecule scale is challenging. Yue Zheng and colleagues from China’s Sun Yat-sen University demonstrated by computer simulation that a cyclopropane-1,2-dithiol ring linking two gold atoms can be opened and closed reversibly and repeatedly using mechanical force, switching its electric properties from one that internally donates electrons to one that accepts them. Using the molecule as modular building block, they design a multifunctional junction that, with the application of mechanical force, exhibited resistance (increasing voltage leads to increased current), rectification (converts alternating current to direct) and negative differential resistance (increasing voltage leads to decreased current).

Hydrogen bonding: a mechanism for tuning electronic and optical properties of hybrid organic–inorganic frameworks (氢键连接:有机-无机杂化框架的电子和光学性质的调控机制)

Fedwa El-Mellouhi, El Tayeb Bentria, Asma Marzouk, Sergey N Rashkeev, Sabre Kais & Fahhad H Alharbi
npj Computational Materials
 2, Article number: 16035 (2016)
doi:10.1038/npjcompumats.2016.35
Published online:04 November 2016
Abstract| Full Text | PDF OPEN
摘要:
无机-有机杂化框架材料领域是材料科学中发展最快的领域之一,因其丰富的结构和化学多样性可通过多种技术赋予材料一些奇特的性能。这其中最重要的就是通过改变它们的化学、制造技术和制备条件,来调控这类复杂材料的结构、光学、热学、机械和电子方面的性能。本研究通过引入结构单元以与环境物质形成氢键而实现调控,因而在这一领域内取得了显著进展。考虑到杂化框架材料的各种有序结构中含质子化的锍阳离子H3S+和带负电的卤素阴离子(I?、Br?、Cl?和F?),我们发现氢键链接增加了材料的结构稳定性,从而有可能用于调控带隙附近的电子态。本研究认为,这样通过氢键连接调控有着普遍意义,在质子化阳离子无机-有机杂化框架材料中可观察到,可在光电和光伏应用方面发挥作用。

Abstract: The field of hybrid inorganic–organic framework materials is one of the fastest growing fields in materials science because their enormous structural and chemical diversity presents great opportunities for creating many technologically relevant properties. One of the most important issues is controlling and tuning the structural, optical, thermal, mechanical and electronic properties of these complex materials by varying their chemistry, fabrication techniques and preparation conditions. Here we demonstrate that significant progress in this area may be achieved by introducing structural elements that form hydrogen bonds with the environment. Considering hybrid framework materials with different structural ordering containing protonated sulfonium cation H3S+ and electronegative halogen anions (I?, Br?, Cl? and F?), we found that hydrogen bonding increases the structural stability of the material and may be used for tuning electronic states near the bandgap. We suggest that such a behaviour has a universal character and should be observed in hybrid inorganic–organic framework materials containing protonated cations. This effect may serve as a viable route for optoelectronic and photovoltaic applications.

Possible ground states and parallel magnetic-field-driven phase transitions of collinear antiferromagnets (可能的基态和平行磁场驱动的共线反铁磁物质的相变)

Hai-Feng Li
npj Computational Materials
 2, Article number: 16032 (2016)
doi:10.1038/npjcompumats.2016.32
Published online:14 October 2016
Abstract| Full Text | PDF OPEN

摘要:理解所有可能的基态性质,尤其是磁场驱动相变的反铁磁体基态性质,是揭示一些如超导、多铁性或磁致电阻等有趣现象背后本质的重要一环。本研究所作的相容平均场计算,赋予反铁磁(AFM)交换作用(J)、易轴各向异性(γ)、单轴单离子各向异性(D)以及一个平行于AFM易轴磁场的Zeeman耦合几个变量,始终保持了AFM态、自旋-翻牌(SFO)和自旋-反转转变三者统一。计算结果发现,数学上允许的一些奇特自旋态和奇特脉动取决于相对耦合强度(J, γ, D)。本研究建立了三维(J, γ, D)和二维(γ, D)相图,可清楚地显示平衡相条件,同时本文还讨论了各种磁性态的起源及其在不同耦合中的转换。除传统的一级SFO过渡类型外,本研究明确了二级SFO过渡类型的存在。这些研究为共线反铁磁体磁态(带有两个互相贯通的亚点阵结构)提供了一个完整的理论模型,也为估算磁交换参数(J, γ, D)提供了一个实用的替代方法,有可能为揭示块材、薄膜和相关电子体系的纳米结构的非平凡磁相关性能,提供理论指导。

Abstract: Understanding the nature of all possible ground states and especially magnetic-field-driven phase transitions of antiferromagnets represents a major step towards unravelling the real nature of interesting phenomena such as superconductivity, multiferroicity or magnetoresistance in condensed-matter science. Here a consistent mean-field calculation endowed with antiferromagnetic (AFM) exchange interaction (J), easy axis anisotropy (γ), uniaxial single-ion anisotropy (D) and Zeeman coupling to a magnetic field parallel to the AFM easy axis consistently unifies the AFM state, spin-flop (SFO) and spin-flip transitions. We reveal some mathematically allowed exotic spin states and fluctuations depending on the relative coupling strength of (J, γ and D). We build the three-dimensional (J, γ and D) and two-dimensional (γ and D) phase diagrams clearly displaying the equilibrium phase conditions and discuss the origins of various magnetic states as well as their transitions in different couplings. Besides the traditional first-order type one, we unambiguously confirm an existence of a second-order type SFO transition. This study provides an integrated theoretical model for the magnetic states of collinear antiferromagnets with two interpenetrating sublattices and offers a practical approach as an alternative to the estimation of magnetic exchange parameters (J, γ andD), and the results may shed light on nontrivial magnetism-related properties of bulks, thin films and nanostructures of correlated electron systems.

Editorial Summary Quantum matter: Mapping magnetic materials (量子物质:为磁性材料绘图)

来自中国的研究人员证实了可更好地理解磁性材料奇异性能的数学方法。澳门大学的李海峰(Hai-Feng Li)开发了一种计算方法,它能预测所谓关联物质从一某种状态转变成另一种状态的途径。之所以称为关联物质,是因为它们内部的电子都彼此相互作用,并能造成物质的非凡性能,如超导、多铁性和大磁电阻效应。对这些材料施加磁场可以使其从诸多状态(或相)中的一种转变成另一种。Li的这一理论框架将合作性和竞争性电子相互作用结合起来,用以预测物质的相转变。基于这一理论,可为所有平衡相画出一张地形图,加深对各种磁性态起源的了解。

A mathematical method for better understanding the exotic properties of magnetic materials is demonstrated by researchers in China. Hai-Feng Li from the University of Macao has developed calculations that predict the way so-called correlated matter can change from one state to another. Correlated materials are so called because the electrons within them all interact with each other to give the substance extraordinary properties. These include superconductivity, multiferroicity and large magneto-resistance effects. Applying a magnetic field to such materials can make it switch from one of these states, or phases, to another. Li’s theoretical framework combines both cooperative and competitive electron interactions to predict these phase changes. With this, a map of all equilibrium phases can be derived, and this provides insight into the origins of the various magnetic states.

Autonomy in materials research: a case study in carbon nanotube growth (自主材料研究:以碳纳米管生长为例)

Pavel Nikolaev, Daylond Hooper, Frederick Webber, Rahul Rao, Kevin Decker, Michael Krein,Jason Poleski, Rick Barto & Benji Maruyama
npj Computational Materials
 2, Article number: 16031 (2016)
doi:10.1038/npjcompumats.2016.31
Published online:21 October 2016
Abstract| Full Text | PDF OPEN
摘要:
材料进步是我们技术进步的重要因素,然而,材料的发现和开发过程却步履蹒跚。我们目前的研究过程是以人为中心的,由人类的研究人员来设计、执行、分析和解释实验结果,然后再确定下一步该做什么。本研究在国际上首次建立了一个自主研究系统(ARES),即一种能够自主研发的机器人,可做闭环迭代式材料实验。ARES能利用自主机器人、人工智能、数据科学、高通量和原位技术方面的诸多进展,并能够设计、执行和分析其自己所做的实验,比目前的研究方法速度快几个数量级。本研究将ARES投入单壁碳纳米管的合成研究,结果显示,ARES成功地学会、并以预定速度生长单壁碳纳米管。ARES未来将广泛参与以人为中心的科研、自主研发机器人、人-机合作等领域的工作。我们认为,诸如ARES之类的自主研发机器人,将构成人类理解能力的颠覆性进步,并以前所未有的速度研发复杂材料。

Abstract:Advances in materials are an important contributor to our technological progress, and yet the process of materials discovery and development itself is slow. Our current research process is human-centred, where human researchers design, conduct, analyse and interpret experiments, and then decide what to do next. We have built an Autonomous Research System (ARES)—an autonomous research robot capable of first-of-its-kind closed-loop iterative materials experimentation. ARES exploits advances in autonomous robotics, artificial intelligence, data sciences, and high-throughput and in situ techniques, and is able to design, execute and analyse its own experiments orders of magnitude faster than current research methods. We applied ARES to study the synthesis of single-walled carbon nanotubes, and show that it successfully learned to grow them at targeted growth rates. ARES has broad implications for the future roles of humans and autonomous research robots, and for human-machine partnering. We believe autonomous research robots like ARES constitute a disruptive advance in our ability to understand and develop complex materials at an unprecedented rate.

Editorial Summary

Autonomy: Robot Researchers Build Better Nanotubes (自主性:机器人研究者造出更好的纳米管)

一个能够使用反馈算法学习如何优化碳纳米管产物的机器人,有可能成为材料研发人员的得力合作伙伴。美国空军研究实验室和洛克希德马丁公司先进技术实验室的一组科学家建立了一个自主的研究系统(ARES),即一种可做迭代学习循环的人工智能机器人,它能自主设计、执行和分析其自己所做的实验,速度比目前的研究方法快几个数量级。与人类科研人员组成团队,它学会了按照一整套条件来调控碳纳米管以预定速度生长。该研究创建了一个快速、高通量的方法,将广泛适用于其他材料的研究,也将使研究人员能做更具挑战性的实验研究工作。

A robot that learns how to optimize carbon nanotube production using feedback algorithms may be a valuable partner in materials discovery. A team of scientists at the Air Force Research Laboratory and Lockheed Martin Advanced Technology Laboratories have built an Autonomous Research System (ARES), a robot guided by artificial intelligence in an iterative learning loop, that is capable of designing, executing and analyzing its own experiments orders of magnitude faster than current research methods. Teaming with human researchers, ARES learned to control the growth of carbon nanotubes by converging on a set of conditions that yielded an objective growth rate. This approach creates a rapid, high-throughput approach that is broadly applicable to other materials research problems and enables researchers to pursue more challenging experimental campaigns.

Softening of phonon spectra in metallic glasses(金属玻璃中声子谱的软化)

摘要:采用密度泛函理论和实施小位移法计算,可研究MgZnCa系列非晶合金的振动谱,采用从头算分子动力学模拟方法可得到这些合金的原子结构。基于德拜模型计算结果来看,振动的热力学性能是温度的函数,尤其是低温比热可近似为温度的3次函数。本研究计算了Mg的振动对比热的贡献,研究了Mg声子谱的软化,其中最大允许振动频率被降低,而高度集体扩散过程却被促进。结果在合金所报道的临界铸造厚度与Mg声子的软化之间获得了统计相关性。采用类似方法对两个明显不同的ZrTiCuAl非晶合金及他们大、小不同的关键铸件厚度分别进行了计算,发现ZrTiCuAl的计算结果与MgZnCa的一致。

Abstract: The vibrational spectra of a series of MgZnCa amorphous alloys were computed using density functional theory and implementing the small displacement method. The atomic structures of the alloys were obtained by ab initio molecular dynamics simulations. The vibrational thermodynamic properties were calculated as a function of temperature and, in particular, the specific heat at low temperature was approximated by temperature cubed based on the Debye model. We computed the contribution of Mg vibrations to the specific heat and investigated the softening of Mg phonon spectra, where the maximum allowed vibrational frequency is lowered and highly collective diffusion processes are promoted. The statistical correlation between the reported critical casting thickness of the alloys and softening of Mg phonons was obtained. Similar calculations were performed for two distinctively different amorphous ZrTiCuAl alloys with large and small reported critical casting thickness, respectively. The findings were consistent with those of the MgZnCa alloys.

Editorial Summary Lower vibrational frequencies of atoms in metallic glasses(金属玻璃中原子的较低振动频率)

金属玻璃是一类具有高硬度、高回弹性、耐腐蚀性和耐久性的结构材料。金属玻璃中原子按一定频谱振动,对确定热性能(如热容量)起重要作用,还可影响合金的稳定性。基于量子分子动力学模拟对金属玻璃的振动谱进行计算很有挑战性,原因是这样的计算既复杂,又密集。澳大利亚的新南威尔士大学Michael Ferry及其同事对此作了模拟,并对几种不同的金属玻璃作了振动谱计算。他们发现,在某些合金中,原子会作较低频率的振动。他们找到了向低频振动谱的转变对热容量、熵和自由能的影响规律,并讨论了这些方面对金属玻璃的动力学性能和稳定性的影响。

Metallic glasses are a class of structural materials exhibiting high hardness, high resilience, corrosion resistance and durability. The spectrum of frequencies at which atoms vibrate in metallic glasses play an important role in determining thermal properties such as heat capacity and can affect alloys stability. The calculation of vibrational spectrum for metallic glasses based on quantum molecular dynamic simulations is challenging due to the complexity and resource intensive nature of such calculations. Michael Ferry and colleagues at the University of New South Wales in Australia performed these simulations and computed vibrational spectra for a number of different metallic glasses. They found that in certain alloys atoms tend to vibrate at lower frequencies. They obtained the effect of such shifts to lower frequencies on heat capacity, entropy and free energy and discussed their implications on dynamical properties and observed stability of metallic glasses.

Faceted interfaces: a key feature to quantitative understanding of transformation morphology (面界面:对变换形态定量理解的一个关键特征)

摘要:面界面是固态相变时出现的许多微观结构形态中一个典型的重要特征。对面形态的解释、预测和模拟目前仍面临挑战,对两相间非理性取向关系(ORs)和非理性界面取向(IOs)都作为首选的系统来说尤为如此。就结构奇点来说,本研究提出了一个集成框架,可能对所有候选面界面都适用。从匹配的图案、位错结构和/或台阶结构中可以确定结构奇点。所得奇异界面存在离散的IOs,可以低指数g(理性取向)和/Δg(可以是理性也可以是非理性取向)加以描述。现有各种模型依据其针对OR IO所确定的结果进行分组,然后各模型间的关联可在集成框架中得以清楚描述。在主要的奇异界面中尽可能地消除缺陷类型,往往会对OR施加核心约束。非理性IO通常是在某个方向上位错消除的结果,如O-line界面。本文还就采用二维和三维模型对O-line界面定量测定的分析方法作了评述,而且还详细举例说明了非理性界面的计算方法。对fcc/bcc合金中界面能量所作的原子计算发现,所得出的平衡剖面结果与对该合金的观察结果有很好的一致性,从而验证了结构奇点和局部能量极小值之间的关系。 

Abstract: Faceted interfaces are a typical key feature of the morphology of many microstructures generated from solid-state phase transformations. Interpretation, prediction and simulation of this faceted morphology remain a challenge, especially for systems where irrational orientation relationships (ORs) between two phases and irrational interface orientations (IOs) are preferred. In terms of structural singularities, this work suggests an integrated framework, which possibly encompasses all candidates of faceted interfaces. The structural singularities are identified from a matching pattern, a dislocation structure and/or a ledge structure. The resultant singular interfaces have discrete IOs, described with low-index g’s (rational orientations) and/or Δg’s (either rational or irrational orientations). Various existing models are grouped according to their determined results regarding the OR and IO, and the links between the models are clarified in the integrated framework. Elimination of defect types as far as possible in a dominant singular interface often exerts a central restriction on the OR. An irrational IO is usually due to the elimination of dislocations in one direction, i.e., an O-line interface. Analytical methods using both three-dimensional and two-dimensional models for quantitative determinations of O-line interfaces are reviewed, and a detailed example showing the calculation for an irrational interface is given. The association between structural singularities and local energy minima is verified by atomistic calculations of interfacial energies in fcc/bcc alloys where it is found that the calculated equilibrium cross-sections are in a good agreement with observations from selected alloys. 

Shift current bulk photovoltaic effect in polar materials—hybrid and oxide perovskites and beyond (极性材料的位移电流光伏效应:杂化钙钛矿、氧化物钙钛矿及其他 

Liang Z Tan, Fan Zheng, Steve M Young, Fenggong Wang, Shi Liu & Andrew M Rappe
npj Computational Materials 2, Article number: 16026 (2016)
doi:10.1038/npjcompumats.2016.26
Published online:26 August 2016
Abstract| Full Text | PDF OPEN

摘要:光伏效应(BPVE)是指,在缺乏反对称性的单相均质材料中,产生一个稳定的光电流和高于带隙的光电压。BPVE机理与典型的p–n结基异质材料的机理完全不同。最近,受铁电体中发现高带隙光电压的启发,低带隙铁电材料的发明和迅速提高功率转换效率的金属卤化物钙钛矿,引起了国际上对采用铁电材料转换太阳能的新兴趣。然而,对BPVE的本质和其对材料组成和结构的相关性了解不够深入,将阻碍材料调控及其性能的实现。本文综述了探索BPVE机制的历史、发展和最新进展,重点关注了位移电流的机理,即所有缺乏反对称性结构材料所固有的BPVE。本文除了解释移位电流理论之外,还讨论了未来BPVE应用时,材料设计的机遇和挑战。 

Abstract: The bulk photovoltaic effect (BPVE) refers to the generation of a steady photocurrent and above-bandgap photovoltage in a single-phase homogeneous material lacking inversion symmetry. The mechanism of BPVE is decidedly different from the typical p–n junction-based photovoltaic mechanism in heterogeneous materials. Recently, there has been renewed interest in ferroelectric materials for solar energy conversion, inspired by the discovery of above-bandgap photovoltages in ferroelectrics, the invention of low bandgap ferroelectric materials and the rapidly improving power conversion efficiency of metal halide perovskites. However, as long as the nature of the BPVE and its dependence on composition and structure remain poorly understood, materials engineering and the realisation of its true potential will be hampered. In this review article, we survey the history, development and recent progress in understanding the mechanisms of BPVE, with a focus on the shift current mechanism, an intrinsic BPVE that is universal to all materials lacking inversion symmetry. In addition to explaining the theory of shift current, materials design opportunities and challenges will be discussed for future applications of the BPVE. 

    

A general-purpose machine learning framework for predicting properties of inorganic materials (预测无机材料性能的通用机器学习框架) 

Logan Ward, Ankit Agrawal, Alok Choudhary & Christopher Wolverton
npj Computational Materials 2, Article number: 16028 (2016)
doi:10.1038/npjcompumats.2016.28
Published online:26 August 2016
Abstract| Full Text | PDF OPEN

摘要:材料研究的一个非常活跃的领域,是设计自动发现新材料的方法,即机器自行学习而能从现有材料数据中自动产生预测模型。虽然之前的实例已证明了一些应用模型非常成功,但机器的自行学习可使更多的现有应用模型功能更为强大。为了更快地开发基于机器学习的模型投入应用,本研究创建了一个框架,适用于宽泛的材料数据领域。我们的方法是将化学属性作不同的列表,发现这样很适合于描述宽泛领域内各种不同的属性,我们的另一种创新方法是将数据集按相似材料组分区,以此提高预测精度。本文展示了我们这种新方法如何预测了结晶材料和非晶材料的多种属性,比如带隙能量和玻璃化能力。 

Abstract: A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability. 

Editorial Summary

Machine learning: Searching for novel materials (机器学习寻找新材料)

美国的研究人员开发了一个多功能的机器学习框架,以帮助寻找新材料。由西北大学的Christopher WolvertonLogan Ward领导的研究人员,使用了机器学习技术训练电脑从已知材料数据中产生计算模型,以预测具有特殊性能的新材料。该技术的实用性通过两个实例得到证实,一个是从光伏应用的角度寻找新的结晶化合物;另一个是从玻璃形成三元合金相的角度寻找金属玻璃合金。新模型可通过优化机器学习算法和分区输入数据而创建,从而最大限度地提高某些参数的准确性。结合现有的对研究人员可利用的庞大材料数据,该技术有望自动并加速对新功能材料的寻找与发现。 

Researchers in the United States have developed a versatile machine learning framework to aid the search for novel materials. Led by Christopher Wolverton and Logan Ward from Northwestern University, the researchers used machine learning techniques trained against known material data to generate models that predict the specific properties of new materials. The utility of the technique was demonstrated through searches for novel crystalline compounds for photovoltaic applications, and for metallic glass alloys based on the probability of glass formation for ternary alloys. New models can be created by optimizing the machine learning algorithm and partitioning input data to maximize the prediction accuracy for specific parameters. The technique has the potential to automate and accelerate the search for new functional materials using the large libraries of material data now available to researchers. 

Ab initio prediction of fast non-equilibrium transport of nascent polarons in SrI2: a key to high-performance scintillation (SrI2中新生极化子快速非平衡传输的从头预测:高性能闪烁的关键 

Fei Zhou, Babak Sadigh, Paul Erhart & Daniel Åberg
npj Computational Materials 2, Article number: 16022 (2016)
doi:10.1038/npjcompumats.2016.22
Abstract| Full Text | PDF OPEN

摘要:铕掺杂碘化锶 (SrI2:Eu) 的光产额极高,能赋予γ-射线探测器极高的高能分辨率,远远超过大多数卤素化合物。对这一类材料而言,其内部常常形成自扑捉空穴极化子,但通常认为极化子的形成限制了载流子移动,而且与闪烁光低产率和低分辨率相关。采用最近开发的第一原理方法,我们首创对SrI2平衡极化子和自陷事件后即刻形成的新生极化子进行了研究。本研究对SrI2意外高能分辨率提出了理论解释,确定了九种稳定的空穴极化子组态,包括二聚化碘对,结合能高达0.5 eV。这些空穴极化子组态通过复杂的势能景貌连结,这个势能景貌有66个独特的最邻近迁移路径。从头预测的分子动力学模拟表明,极化子的很大一部分产生后就进入了室温下几乎可自由迁移的组态中。这样,在γ-辐照过程中产生的载流子可以迅速扩散,减少了非线性重组的机会,成为非均衡性和低分辨率的主因。本研究认为,SrI2 极化子平坦而复杂的能量景貌是理解其卓越闪烁性能的关键。这种认识不仅为将来开发高性能闪烁体提供了重要的指导,也为研发高迁移率极化子的其他材料,如电池和固态离子导体,提供了重要的参考。 

Abstract: The excellent light yield proportionality of europium-doped strontium iodide (SrI2:Eu) has resulted in state-of-the-art γ-ray detectors with remarkably high-energy resolution, far exceeding that of most halide compounds. In this class of materials, the formation of self-trapped hole polarons is very common. However, polaron formation is usually expected to limit carrier mobilities and has been associated with poor scintillator light-yield proportionality and resolution. Here using a recently developed first-principles method, we perform an unprecedented study of polaron transport in SrI2, both for equilibrium polarons, as well as nascent polarons immediately following a self-trapping event. We propose a rationale for the unexpected high-energy resolution of SrI2. We identify nine stable hole polaron configurations, which consist of dimerised iodine pairs with polaron-binding energies of up to 0.5 eV. They are connected by a complex potential energy landscape that comprises 66 unique nearest-neighbour migration paths.  Ab initio molecular dynamics simulations reveal that a large fraction of polarons is born into configurations that migrate practically barrier free at room temperature. Consequently, carriers created during γ-irradiation can quickly diffuse away reducing the chance for non-linear recombination, the primary culprit for non-proportionality and resolution reduction. We conclude that the flat, albeit complex, landscape for polaron migration in SrI2 is a key for understanding its outstanding performance. This insight provides important guidance not only for the future development of high-performance scintillators but also of other materials, for which large polaron mobilities are crucial such as batteries and solid-state ionic conductors. 

Editorial Summary

Scintillation: A fast move for gamma ray detectors (闪烁:伽玛射线探测器中的快速移动)

An international team of researchers has uncovered evidence that may explain the excellent scintillation properties of strontium iodide. Daniel Åberg and colleagues at Lawrence Livermore National Laboratory in the United States and Chalmers University in Sweden looked at the role of polarons — a “quasi-particle” consisting of a charge carrier and the surrounding displaced lattice — in the performance of gamma ray scintillators, which absorb radiation and convert it into visible light. In “good” scintillators like strontium iodide, which is used in gamma ray detectors, it is imperative that the polarons move quickly away from the region of their creation. The team computed the properties of polarons in strontium iodide and found that generated polarons moved unusually fast and had a variety of energy configurations. These observations are thought to explain the high energy resolution of strontium iodide.  

一个国际研究小组发现了可以解释碘化锶优良闪烁性能的证据。工作于美国Lawrence Livermore国家实验室和瑞典Chalmers理工大学的Daniel Åberg及其同事,研究了极化子(一种准粒子,由电荷载体和其周边伴生晶格畸变组成)在γ-射线闪烁体中的作用,极化子吸收辐射并将其转换成可见光。类似于碘化锶那样的、应用于γ-射线探测器中的各种闪烁体来说,需要极化子迅速远离他们所产生的区域。该团队计算了碘化锶中极化子的性质,发现产生的极化子移动异常迅速,并有各种能量组态。他们研究认为,这些结果解释了碘化锶高能分辨的机理。

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