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Strain-induced resonances in the dynamical quadratic magnetoelectric response of multiferroics (多铁动力学二次磁电响应中的应变感应共振)
S. Omid Sayedaghaee, Charles Paillard1, Sergey Prosandeev, Bin Xu and Laurent Bellaiche
npj Computational Materials 6:60(2020)
doi:s41524-020-0311-z
Published online:21 May2020

Abstract| Full Text | PDF OPEN

摘要:近年来,对磁电(ME)效应——这一多铁材料中铁电有序与磁有序间交叉耦合的研究兴趣经历了重大的复兴。近期大量的工作不仅研究了利用磁场(或电场)对极化(或磁化)的交叉控制来设计传感器,驱动器,换能器和存储设备,更旨在清楚地理解ME响应的来源以及与之相关的新颖效应。在这里,我们推导出解析模型用于理解多铁体系中ME效应的惊人和新颖的动力学,并通过原子模拟进一步确认该现象的存在。具体而言,揭示了应变可以导致电声磁振子的存在,其为一种声学和光学声子与磁振子混合的新型准粒子。该粒子可导致共振,从而显著增强了磁电响应。而且,在本工作之前,尚未有工作讨论变频磁场导致动态二次ME响应,此为二次谐波过程。这些过程表明处理此类系统时应考虑非线性的是十分重要的 

Abstract:For the last few years, the research interest in magnetoelectric (ME) effect, which is the cross-coupling between ferroelectric and magnetic ordering in multiferroic materials, has experienced a significant revival. The extensive recent studies are not only conducted towards the design of sensors, actuators, transducers, and memory devices by taking advantage of the cross-control of polarization (or magnetization) by magnetic (or electric) fields, but also aim to create a clearer picture in understanding the sources of ME responses and the novel effects associated with them. Here we derive analytical models allowing to understand the striking and novel dynamics of ME effects in multiferroics and further confirm it with atomistic simulations. Specifically, the role of strain is revealed to lead to the existence of electroacoustic magnons, a new quasiparticle that mixes acoustic and optical phonons with magnons, which results in resonances and thus a dramatic enhancement of magnetoelectric responses. Moreover, a unique aspect of the dynamical quadratic ME response under a magnetic field with varying frequencies, which is the second harmonic generation (SHG), has not been discussed prior to the present work. These SHGs put emphasis on the fact that nonlinearities should be considered while dealing with such systems.

Editorial Summary

Strain matters: resonance enhanced dynamic quadratic magnetoelectric effect应变生新花:共振增强动态二次磁电效应

本研究发现了应变可以诱导多铁体系中动态二次磁电耦合响应的共振增强。由美国阿肯色大学Bellaiche教授领导的国际团队基于唯象模型和分子动力学模拟研究了应变对于多铁体系中的动态二次磁电耦合响应的影响。他们首先构建了多铁体系应变、磁和电多场耦合的唯象朗道模型,并基于该模型的推导得到了二次磁电系数随外场频率的变化关系。他们发现,当体系允许应变存在时,体系中会出现一种新的元激发准粒子,即电声磁振子,其为声学/光学声子与磁振子耦合态。该准粒子导致二次磁电系数在某些频率会极大的增强,发生所谓共振现象。为证实上述结果,他们基于典型多铁体系BiFeO3的等效哈密顿量开展了分子动力学模拟。通过施加不同频率的交变外场,他们发现在应变允许发生时的确观察到磁电系数的共振增强,而当应变不能发生时,共振增强消失,从而证实了唯象模型的结果。该研究提出一种全新的强磁电响应的物理机制,且可以单相材料中实现,不仅丰富磁电耦合的物理图像,而且有望用于实现新型磁电耦合器件 

Stain induced resonant enhancement of dynamic quadratic magnetoelectric (ME) response in multiferroics was discovered. An international team led by Professor L. Bellaiche from the University of Arkansas studied the role of strain on the dynamic quadratic ME coupling in multiferroic systems based on phenomenological model and molecular dynamics simulations. They first proposed a phenomenological Landau model to describe the coupling of strain, magnetic and electric variants in multiferroic system. By derivation of this model, the relationship between the dynamic quadratic ME coefficient and the frequency of external field was obtained. They found that when strain exists, a new kind of elementary excitation, namely electroacoustic magnon, will appear in the system, which is the coupling state of acoustic/optical phonon and magnon. This excitation results in a dramatic enhancement of the quadratic ME coefficient at certain frequencies, resulting in the so-called resonance phenomenon. To confirm the above results, they carried out molecular dynamics simulations based on the effective Hamiltonian of BiFeO3. By applying alternating external fields of different frequencies, they found that the resonance enhancement of ME coefficient can be observed when  strain was allowed, while it disappears when the strain is clamped, which confirms the results of the phenomenological model. This study proposes a new mechanism of ME response, which can be realized in single-phase materials. It not only enriches the physics of ME coupling, but is also expected to be used to realize new ME devices.

Simulating Raman spectra by combining first-principles and empirical potential approaches with application to defective MoS2(第一性原理结合经验势方法模拟拉曼光谱并应用于缺陷MoS2的研究)
Zhennan KouArsalan HashemiMartti J. PuskaArkady V. Krasheninnikov & Hannu-Pekka Komsa
npj Computational Materials 6:59(2020)
doi:s41524-020-0320-y
Published online:15 May 2020

Abstract| Full Text | PDF OPEN

摘要:二维过渡金属双硫属化合物在光电、催化或传感器件中的成功应用,很大程度上依赖于材料的质量,即厚度均匀性、晶界的存在以及点缺陷的类型和浓度。拉曼光谱是探测这些因素的一个强大而无损的工具,但光谱的解释,特别是不同贡献的区分并不简单。与模拟光谱进行比较是有益的,但对于有缺陷的材料系统,由于所涉及的尺寸太大,第一性原理模拟通常在计算上过于昂贵。在此,本研究提出了一种第一性原理和经验势结合的方法来模拟缺陷材料的拉曼光谱,并将其应用于具有MoS空位随机分布的单层MoS2中。我们研究了在何种程度上可以区分空穴类型,并提供随缺陷浓度变化时拉曼光谱演化的起源分析。我们将模拟光谱应用于声子局域模型(之前实验中使用的模型)来评估缺陷浓度。结果显示,该模型的最简单形式不足以完全捕获峰形,但是当声子局域类型与完整的声子谱联系起来的时候,该模型获得的结果与实验数据有很好的一致性 

Abstract:Successful application of two-dimensional transition metal dichalcogenides in optoelectronic, catalytic, or sensing devices heavily relies on the materials’ quality, that is, the thickness uniformity, presence of grain boundaries, and the types and concentrations of point defects. Raman spectroscopy is a powerful and nondestructive tool to probe these factors but the interpretation of the spectra, especially the separation of different contributions, is not straightforward. Comparison to simulated spectra is beneficial, but for defective systems first-principles simulations are often computationally too expensive due to the large sizes of the systems involved. Here, we present a combined first-principles and empirical potential method for simulating Raman spectra of defective materials and apply it to monolayer MoS2 with random distributions of Mo and S vacancies. We study to what extent the types of vacancies can be distinguished and provide insight into the origin of different evolutions of Raman spectra upon increasing defect concentration. We apply our simulated spectra to the phonon confinement model used in previous experiments to assess defect concentrations, and show that the simplest form of the model is insufficient to fully capture peak shapes, but a good match is obtained when the type of phonon confinement and the full phonon dispersion relation are accounted for.

Editorial Summary

Simulating Raman spectra in defective MoS2: by first-principles and empirical potential approaches模拟缺陷MoS2的拉曼光谱:第一性原理结合经验势方法

该研究展示了一种基于经验势和第一性原理计算的方法,用于模拟缺陷材料的拉曼光谱,其中经验势用于评估缺陷系统的振动模式,然后与第一性原理计算得到的拉曼张量进行结合。来自芬兰阿尔托大学应用物理系的Hannu-Pekka Komsa领导的团队,构建了该组合方法,并研究了在何种程度上可以区分空位类型,最后探讨了随缺陷浓度增加时拉曼光谱不同演化的机理。这种方法不仅能可靠地模拟拉曼光谱,还可深入了解缺陷系统中振动模式的物理内涵,以及如何用拉曼光谱对它们进行探测。作者利用该方法研究了单层MoS2中的空位缺陷,捕获了缺陷对突出峰位移和不对称展宽的影响,其结果与实验数据定性一致。此外,他们使用声子局域模型来拟合其模拟的拉曼光谱,以评估该模型在缺陷材料中的适用性。结果发现,当同时考虑完整的声子色散关系和局域类型时,该模型非常有效。通过本研究发现,只要有适当的经验势,就可以有效地评估缺陷系统的拉曼光谱 

A method for simulating Raman spectra of defective materials based on a combination of empirical potentials and first-principles calculations is demonstrated, in which the empirical potentials are used to evaluate the vibrational modes of the defective system, which are then combined with Raman tensors evaluated from the first-principles calculations. A team led by Hannu-Pekka Komsa from the Department of Applied Physics, Aalto University, Finland, had constructed this combined method and studied to what extent the types of vacancies can be distinguished, and finally provided insight into the origin of different evolutions of Raman spectra upon increasing defect concentration. This approach allows them to not only reliably simulate Raman spectra, but also gain insights into the physics of vibrational modes in defective systems and how they can be probed with Raman spectroscopy. The authors used this method to study vacancies in monolayer MoS2 and captured the effect of defects on the shifts and on the asymmetric broadening of the prominent peaks, with the results being in a qualitative agreement with experimental data. They then used the phonon confinement model to fit their simulated Raman spectra to assess the applicability of the model in the context of defective materials. They found it to work well when the full dispersion relation and the type of confinement are accounted for. The approach presented here allows for efficient evaluation of the Raman spectra of defective systems provided that an appropriate empirical potential is available.

Predicting synthesizable multi-functional edge reconstructions in two-dimensional transition metal dichalcogenides(预测二维过渡金属双卤化物中可合成的多功能边缘重构)
Guoxiang HuVictor FungXiahan SangRaymond R. Unocic & P. Ganesh
npj Computational Materials 6:120(2020)
doi:s41524-020-0327-4
Published online:13 August 2020

Abstract| Full Text | PDF OPEN

摘要:二维(2D)过渡金属双硫属化合物(TMDC)由于其独特的多样性和可调性,尤其是其边缘特性,已经引起了人们极大的兴趣。除了常规六边形2D材料常见的扶手椅边和锯齿形边缘外,通过对合成条件的精细调控,可以实现更复杂的边缘重构。然而目前缺乏对整个可合成的边缘重构家族的研究。本研究开发了一种集成计算方法,整合了构型生成、力的弛豫以及电子结构计算等流程,以系统、有效地发现的重构边缘和筛选其功能特性。以MoS2为模型系统,对数百条重构边缘进行筛选,发现超过160条重构边缘比传统边缘更稳定。更令人兴奋的是,我们发现了9个新的可合成的重构边缘,具有热力学稳定性,此外还成功地再现了3个最近合成的边缘结构。我们还发现预测的重构边缘具有多功能特性(与常规边缘相比,还具有接近最佳的析氢活性),是析氢反应(HER)的理想选择,并且具有半金属性,且磁矩变化很大,使其特别适合纳米自旋电子应用。我们的工作揭示了在2D TMDC中存在大量可合成的重构边缘,并打开了2D材料“本征”边缘工程多功能性的材料设计新范式 

Abstract:Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties, especially in their edges. In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials, more complex edge reconstructions can be realized through careful control over the synthesis conditions. However, the whole family of synthesizable, reconstructed edges remains poorly studied. Here, we develop a computational approach integrating ensemble-generation, force-relaxation, and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties. Using MoS2 as a model system, we screened hundreds of edge-reconstruction to discover over 160 reconstructed edges to be more stable than the conventional ones. More excitingly, we discovered nine new synthesizable reconstructred edges with record thermodynamic stability, in addition to successfully reproducing three recently synthesized edges. We also find our predicted reconstructed edges to have multi-functional properties—they show near optimal hydrogen evolution activity over the conventional edges, ideal for catalyzing hydrogen-evolution reaction (HER) and also exhibit half-metallicity with a broad variation in magnetic moments, making them uniquely suitable for nanospintronic applications. Our work reveals the existence of a wide family of synthesizable, reconstructed edges in 2D TMDCs and opens a new materials-by-design paradigm of ‘intrinsic’ edge engineering multifunctionality in 2D materials.

Editorial Summary

2D transition metal dichalcogenides: Predicting synthesizable multi-functional edge reconstructions二维过渡金属双卤化物:预测可合成的多功能边缘重构

由于2D过渡金属双硫属化合物(TMDC中整个可合成的重构边缘家族仍然未知,该研究开发出了一种集成计算方法,可以快速有效地发现2D TMDC体系中更多可合成的功能性边缘,为计算筛选和发现其他功能重构的TMDC边缘提供了独特的机会。来自美国橡树岭国家实验室纳米材料科学中心的Guoxiang HuP. Ganesh共同领导的研究团队,从构型文件生成开始,使用力场方法,筛选了材料的稳定边缘。并使用基于DFT的电子结构计算,进一步细化了所获得的稳定边缘,以生成相图并筛选其功能特性。以MoS2为例,筛选出了2H1TMoS2625个边缘构型,并预测了稳定的边缘以指导实验合成。随后,他们研究了这些边缘的功能特性,发现许多这些内在可调的边缘重构,对于析氢反应来说是接近最佳的。因此,他们的研究为预测??2D材料的可合成功能边缘提供了一个全面而经济的计算方案,并为实验研究人员提供了有用的指导。许多研究已经通过Edisonian方法中的外部掺杂来调控了TMDCs的催化、电子和磁性能,但这项研究的优点是发现了一系列“本征”(基于金属/硫属元素比)可调材料。该研究成功开启了一种新的材料设计范式,可搜索和发现其他具有特定多功能的、有潜在边缘多型性的2D“本征”边缘重构家族,并适用于纳米尺度的广泛应用 

Due to the whole family of synthesizable reconstructed edges in 2D TMDCs remains largely unknown, a computational approach to rapidly and efficiently discover more synthesizable functional edges in the family of 2D TMDCs is developed, which presents a unique opportunity to computationally screen and discover additional functional reconstructed TMDC edges. A team co-led by Guoxiang Hu and P. Ganesh from the Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, USA, starting with configuration ensemble generations, screened for stable edges using a computationally affordable force-field method. The obtained stable edges are then further refined with DFT based electronic-structure calculations to generate phase diagrams and screen for their functional properties. Using MoS2 as an example, the authors screened 625 edge configurations for 2H and 1T MoS2 phases, and predicted stable edges to guide the experimental synthesis. Subsequently they studied the functional properties of these edges and discovered many of these intrinsically tunable edge reconstructions to be near-optimal for hydrogen evolution reaction. Their study thus provides a comprehensive yet affordable computational scheme for predicting synthesizable functional edges of 2D materials and provides useful guidelines to experimental researchers. Many studies have investigated tuning the catalytic, electronic and magnetic properties of TMDCs by external doping in an Edisonian approach, but the merit of this study is to discover a family of ‘intrinsically’ (based on the metal/chalcogen ratio) tunable materials with widely varying functional properties. Success of this study opens a new materials-by-design paradigm to search and discover other families of 2D ‘intrinsic’ edge reconstructions with specific multi-functionalities, with potential edge polytypism, for a wide range of nanoscale applications.

Perfect short-range ordered alloy with line-compound-like properties in the ZnSnN2:ZnO system (ZnSnN2ZnO系统中具有线化合物性质的完美短程有序合金)
Jie PanJacob J. CordellGarritt J. TuckerAndriy ZakutayevAdele C. Tamboli & Stephan Lany
npj Computational Materials 6:120(2020)
doi:s41524-020-0331-8
Published online:13 August 2020

Abstract| Full Text | PDF OPEN

摘要:我们提出了一种新的固体材料相,它是一种无序的固溶体,但具有许多有序线化合物的特征。这些新的物理现象源于完美的短程序,从而保留了局域八隅律规则。我们采用第一性原理计算、模型哈密顿量的蒙特卡罗模拟和基于短程序扩展的固溶模型对双亚晶格混合半导体合金(ZnSnN21-xZnO2x开展模拟。我们证明,这种独特的固溶体必须在“幻术”组分中出现,其电子特征没有无序引起的电荷局域化,因此具有类似有序相的优良载流子传输。有趣的是,该相具有传统固溶体模型(如规则溶体和带隙弯曲模型等)没有的奇异性。在热力学上,该合金相的形成焓急剧降低(类似线化合物),但仍需要长程无序带来的熵才能在实验温度下稳定该化合物 

Abstract:We present a new solid-state material phase which is a disordered solid solution but offers many ordered line-compound features. The emergent physical phenomena are rooted in the perfect short-range order which conserves the local octet rule. We model the dual-sublattice-mixed semiconductor alloy (ZnSnN2)1?x(ZnO)2x(ZnSnN2)1?x(ZnO)2x using first-principles calculations, Monte-Carlo simulations with a model Hamiltonian, and an extension of the regular solution model by incorporating short-range order. We demonstrate that this unique solid solution, occurring at a “magic” composition, can provide an electronically pristine character without disorder-induced charge localization and, therefore, a superior carrier transport similar to ordered phases. Interestingly, this phase shows singularities that are absent in the conventional solid-solution models, such as the regular solution and band-gap bowing model. Thermodynamically, this alloy phase has a sharply reduced enthalpy at its composition (like a line compound), but it still requires the entropy from long-range disorder to be stabilized at experimentally accessible temperatures.

Editorial Summary

Singularity in solid solution: disordered structure but ordered properties固溶体的奇异点:具有有序化合物性能的无序固溶体

本文通过计算预测了一种具有固溶体结构特征但类似有序化合物物理性能的新奇固体相。来自美国可再生能源实验室(NREL)的团队基于综合利用多种计算模型,即固溶体模型、蒙特卡洛模拟和第一性原理,针对双亚晶格混合半导体合金(ZnSnN21-xZnO2x的相结构开展了研究。该体系可以看成是由ON四面体组合而成。他们首先提出了一种用于描述短程有序的参量,基于该参量构建了描述该化合物形成焓的经验表达式。进而基于该能量表达式开展蒙特卡洛模拟,由此获得所有固溶体组分(0<x<0.5)能量最低的结构,并开展密度泛函计算精确计算这些结构的能量。有趣的是,他们发现x=0.25成为所谓“幻数”组分。在该组分,混合焓随组分变化的曲线出现明显奇异点。分析表明,该组分对应的结构具有完美的短程有序,即所有四面体内部都满足八隅律,就是说八面体内阴阳离子化合价之和为零。然而八面体间的连接仍然是无序的。因此该体系处于短程有序长程无序的特殊状态。更有意义的是,对该体系电子结构分析表明,该组分结构的带隙明显偏离正常固溶体模型预测结果,且能带边缘的电子态具有很强的离域性,这是有序化合物的典型特征,其电输运性能应明显优于无序固溶体。通过进一步理论计算,研究人员证明了上述新奇化合物存在的可能性并预测可能存在的温度区间。该工作的意义在于,提出了在多元固溶体中可能存在特别的短程有序长程无序结构,其具有类型有序化合物的物理性质。这一发现为新型功能材料设计提供新思路和更广阔的搜索空间 

A novel solid phase with the characteristics of solid solution like structure but ordered compound like physical properties was predicted. A team from the National Renewable Energy Laboratory (NREL) utilized a set of calculation models, namely solid solution model, Monte Carlo simulation and density functional theory calculation to study the phase structure of (ZnSnN2)1-x(ZnO)2x, a dual-sublattice-mixed semiconductor alloy. This system can be regarded as a combination of O and N tetrahedrons. The authors first proposed an order parameter to describe the short-range order, based on which an empirical expression describing the formation enthalpy of the compound was constructed. Based on the energy expression, Monte Carlo simulations were carried out to obtain the structures for each composition (0 < x < 0.5). Then density functional calculations were carried out to determine the energy of these structures accurately. Interestingly, they found that x = 0.25 becomes a so-called "magic number" composition. In this component, a singularity appears in the curve of the enthalpy of mixing with the component. The analysis shows that the corresponding structure of the component has perfect short-range order, that is, all tetrahedrons satisfy the local octet rule, i.e. the sum of the valence of cation and anion within one tetrahedron is zero. While the connection between octahedrons is still disordered. Therefore, the system is in a special state with short-range order but long-range disorder. More importantly, the analysis of the electronic structure shows that the band gap of the structures with the “magic” composition deviate from the predicted results of the normal solid solution model, and the electronic states at the edge of the energy band exhibit strong delocalization, which is a typical characteristic of ordered compounds and could lead to superior electrical transport properties over that of disordered solid solutions. By further theoretical calculation, the researchers demonstrated the possibility of the existence of these novel compounds and predicted the possible temperature range. The significance of this work lies in the prediction that special short-range ordered but long-range disordered structures may exist in multicomponent solid solutions, which could exhibit physical properties of typical ordered compounds. This study provides a broader space for new material design and discovery.

Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells (机器学习助力高性能非富勒烯有机太阳能电池供体/受体材料的开发)
Yao Wu, Jie Guo, Rui Sun & Jie Min
npj Computational Materials 6:120(2020)
doi:s41524-020-00388-2
Published online:13 August 2020

Abstract| Full Text | PDF OPEN

摘要:通过人工智能、计算机科学和材料的合成与优化有机结合,可以大幅促进高性能有机光伏材料的开发。在这个过程中,机器学习模型与算法的选择发挥着至关重要的作用。本研究以565组供体/受体对的数据为训练集通过五种常见算法构建了机器学习模型,并评估了这些模型应用于指导材料设计和供体/受体配对物筛选的可靠性,结果显示基于随机森林(RF)和提升回归树(BRT)算法的模型表现优异。因此本研究进一步利用RF和BRT模型对3200万组供体/受体对进行性能预测和筛选,并从该数据库中选出六组供体/受体对进行合成与器件表征,从而获得它们的实验光电转化效率。实验验证结果显示,基于RF的机器学习模型更适合用于有机光伏材料的高通量筛选。这为材料的设计和供体/受体配对物的选择提供了新的思路,从而加速有机太阳能电池的发展 

Abstract:Integrating artificial intelligence (AI) and computer science together with current approaches in material synthesis and optimization will act as an effective approach for speeding up the discovery of high-performance photoactive materials in organic solar cells (OSCs). Yet, like model selection in statistics, the choice of appropriate machine learning (ML) algorithms plays a vital role in the process of new material discovery in databases. In this study, we constructed five common algorithms, and introduced 565 donor/acceptor (D/A) combinations as training data sets to evaluate the practicalities of these ML algorithms and their application potential when guiding material design and D/A pairs screening. Thus, the best predictive capabilities are provided by using the random forest (RF) and boosted regression trees (BRT) approaches beyond other ML algorithms in the data set. Furthermore, >32 million D/A pairs were screened and calculated by RF and BRT models, respectively. Among them, six photovoltaic D/A pairs are selected and synthesized to compare their predicted and experimental power conversion efficiencies. The outcome of ML and experiment verification demonstrates that the RF approach can be effectively applied to high-throughput virtual screening for opening new perspectives to design of materials and D/A pairs, thereby accelerating the development of OSCs.

Editorial Summary

Donor/acceptor pairs screening:in organic solar cells有机太阳能电池供体-受体材料的配对:需要“红娘”!

传统有机光伏材料研究方法包括对化学合成、供体/受体材料匹配和器件制备进行精细控制及优化,需要大量的资源投入和较长的研究周期,限制了有机光伏产业的发展与实际商业应用。近日,武汉大学闵杰研究员课题组以被文献报道过的565组基于非富勒烯小分子受体材料和聚合物给体材料的供体/受体对数据库,采用ASCII码字符串的表达方式将供体/受体材料的化学结构进行转化成二进制机器语言,并与其相关光伏参数一起作为训练集和验证集,分别采用线性回归(LR)、多类逻辑回归(MLR)、提升回归树(BRT)、人工神经网络(ANN)和随机森林(RF)算法构建机器学习模型(如图1所示),可对供体、受体材料以及活性层供体/受体对的适配性进行快速的评估和筛选。研究人员对五种典型的算法模型进行评估发现,基于RF和BRT模型的预测结果与测试集中真实值的皮尔森相关系数(r)均超过了0.7,说明该两种模型是进行这类机器学习的最佳表达方式。进一步,研究人员通过原有数据集并结合RF和BRT模型,分别筛选和计算出了3200万个给受体对。为了验证上述模型是否能够有效地指导设计新的有机光伏体系,研究人员从该数据库中选出六组易于合成且具有高效率的给受体对,并进行了材料合成、器件制备与表征。研究结果表明,相较于BRT,RF机器学习模型预测的结果和实验结果之间具有更高的一致性,从而验证了RF模型的高通量虚拟筛选与预测能力。这体现了机器学习方法在解决有机光伏材料问题方面强大的能力,将大大加快高性能有机光伏材料及其供体/受体对的探索过程 

The traditional research lifecycle of organic photovoltaic (OPV) materials is tedious and laborious process which contains materials design and synthesis, device characterization and optimization and performance evaluation, hampering the development of organic photovoltaic. Recently, Prof. Min’s group collected data of 565 donor/acceptor (D/A) pairs with nonfullerene small molecule acceptors and polymer donors as training and testing set to construct five machine learning (ML) methods. These methods which were based on five common algorithms, linear regression (LR), multinomial logistic regression (MLR), boosted regression trees (BRT), artificial neural network (ANN) and random forest (RF), can be used to fast screening and evaluation of new promising materials and donor or acceptor counterparts. According to the predicted results of testing set, the researchers found that ML models based on RF and BRT algorithms performed well with high Pearson’s coefficient of over 0.7. What’s more, the RF and BRT models were used to screen 3.2 million D/A pairs automatically generated by the original dataset, among which six D/A pairs were selected, synthesized and characterized to further evaluate the applicability of these ML methods in OPV. The experiment results correlated better to the predicted results of RF methods compared to that of BRT methods, which indicated superiority of RF method in high throughput virtual screening of OPV materials. This work demonstrates machine learning as a powerful tool to solve problems in OSCs, which will accelerate the discovery of high-performance D/A combinations to a large extent.

Fundamental electronic structure and multiatomic bonding in 13 biocompatible high-entropy alloys (13种生物相容性高熵合金的基本电子结构和多原子键合)
Wai-Yim ChingSaro SanJamieson BrechtlRidwan SakidjaMiqin Zhang & Peter K. Liaw
npj Computational Materials 6:45(2020)
doi:s41524-020-0321-x
Published online:06 May 2020

Abstract| Full Text | PDF OPEN

摘要:高熵合金(HEA)由于其诸多独特性能和潜在应用而备受关注。在此独特的复杂多组分合金类别中,原子间相互作用的性质尚未得到充分认识或开发。本研究报告了一种理论建模技术,可以对其电子结构和原子间键合进行深入分析,并根据量子力学指标,即总键序密度(TBOD)和部分键序密度(PBOD),的使用来预测HEA性能。将该理论建模技术应用于13种生物相容性多组分HEA的研究,得到了许多新颖而有价值的结果,包括使用价电子数不足、对大晶格畸变进行量化、利用实验数据验证机械性能、对孔隙率进行建模以降低杨氏模量等。这项研究概述了应用HEA的路线图作生物医学用材料的合理设计方法 

Abstract:High-entropy alloys (HEAs) have attracted great attention due to their many unique properties and potential applications. The nature of interatomic interactions in this unique class of complex multicomponent alloys is not fully developed or understood. We report a theoretical modeling technique to enable in-depth analysis of their electronic structures and interatomic bonding, and predict HEA properties based on the use of the quantum mechanical metrics, the total bond order density (TBOD) and the partial bond order density (PBOD). Application to 13 biocompatible multicomponent HEAs yields many new and insightful results, including the inadequacy of using the valence electron count, quantification of large lattice distortion, validation of mechanical properties with experiment data, modeling porosity to reduce Young’s modulus. This work outlines a road map for the rational design of HEAs for biomedical applications.

Editorial Summary

Fundamental electronic structure and multiatomic bonding:biocompatible high-entropy alloys生物相容性高熵合金:基本电子结构和多原子键合

该研究通过使用先进的大型超胞建模方法研究了13种受生物启发的HEA的电子结构、原子间键合和机械性能,得到了许多对开发和应用生物相容性高熵合金(HEA)至关重要的新认识。来自美国密苏里大学堪萨斯城分校物理与天文学系的陈慧妍领导的团队,报道了他们针对HEA的形成理论及其潜在应用方面所面临的挑战,所作的有关电子结构、原子间键合以及总键序密度(TBOD)和部分键序密度(PBOD)的研究结果。他们指出,使用TBODPBOD作为评估多组分合金基本性能的关键指标时,具有特别的优点:无论HEA的原子种类、组成或大小如何,都可以直接将它们进行相互比较。而且,该方法还可应用于其他材料系统,只需每对原子间的所有原子间键合,再通过单胞的体积作标准化即可。此特性与基于焓评估的方法中所使用的基态能有很大不同,后者在评估不同组成的多组分HEA性能时计算繁重且耗时 

The electronic structures, interatomic bonding, and mechanical properties of the 13 bioinspired HEAs are investigated through advanced modeling using large supercells yielding many new and insightful results critical to the development and application of biocompatible HEAs. A team led by Wai-Yim Ching from the Department of Physics and Astronomy, University of Missouri Kansas City, USA, presented the electronic structure, interatomic bonding, and the application of total bond order density (TBOD) and partial bond order density (PBOD) in addressing the challenges for fundamental understanding on the theory of formation of HEAs and its potential applications. They pointed out the special merits of using TBOD and PBOD as key metrics for assessing the fundamental properties of multicomponent alloys. They can be directly compared with each other irrespective of their atomic species, composition, or size. Moreover, they can be applied to other materials systems as long as all interatomic bonding between every pair of atoms are included and normalized by the volume of the cell. This characteristic is very different from other techniques based on ground state energies used in the enthalpy evaluation, which can be quite onerous and time consuming for multi-component HEAs with different compositions.

Fifth-degree elastic energy for predictive continuum stress-strain relations and elastic instabilities under large strain and complex loading in silicon (大变形任意载荷下可预测材料不同失稳条件的五阶连续介质模型)
Hao ChenNikolai A. Zarkevich, Valery I. Levitas, Duane D. Johnson & Xiancheng Zhang
npj Computational Materials 6:115(2020)
doi:s41524-020-00382-8
Published online:04 August 2020

Abstract| Full Text | PDF OPEN

摘要:材料在复杂载荷下会有大变形,并经常伴有弹性失稳的相变过程。这种过程在简单体系和复杂体系内都被观察到。这里,基于对大量DFT计算结果的拟合,五阶连续介质力学模型被发展来拟合任意载荷下材料失稳条件。该模型的柯西应力-拉格朗日应变曲线可以很好重现第一性原理计算结果。并且该模型准确的预测了任意载荷下材料的临界失稳应力,包括多轴正应力和剪切应力下的失稳应力。这个模型将为连续介质力学模拟材料在任意载荷下大变形失稳提供了理论基础 

Abstract:Materials under complex loading develop large strains and often phase transformation via an elastic instability, as observed in both simple and complex systems. Here, we represent a material (exemplified for Si I) under large Lagrangian strains within a continuum description by a -order elastic energy found by minimizing error relative to density functional theory (DFT) results. The Cauchy stress-Lagrangian strain curves for arbitrary complex loadings are in excellent correspondence with DFT results, including the elastic instability driving the Si III phase transformation (PT) and the shear instabilities. PT conditions for Si I II under action of cubic axial stresses are linear in Cauchy stresses in agreement with DFT predictions. Such continuum elastic energy permits study of elastic instabilities and orientational dependence leading to different PTs, slip, twinning, or fracture, providing a fundamental basis for continuum physics simulations of crystal behavior under extreme loading.

Editorial Summary

Fifth-degree elastic energy for predictive continuum stress-strain relations and elastic instabilities under large strain and complex loading in silicon准确预测材料在任意载荷下失效的大变形弹性理论

任意载荷下,材料失效的临界应力具有很大的不同,例如静水压下,材料失稳的压强可以到100GPa,而在多方向剪切应力下材料失稳可能只需100-200MPa,可以达到3个量级差。然而目前针对材料失稳的连续介质模型大多基于能量或者最大剪切应力,并不能完全覆盖材料任意并不完善任意载荷。该研究基于连续介质理论提出了基于拉格朗日应变的五阶大变形模型,该模型能够准确获得硅在任意载荷下的材料失效应力。来自中国华东理工大学的陈浩讲师和其博士导师美国爱荷华州立大学航空航天工程和机械工程系的Valery I. Levitas教授团队,以及爱荷华州立大学材料学院的Duane D. Johnson教授团队合作,采用第一性原理计算得到了单晶硅材料在任意载荷下的失稳应力,拟合了提出的大变形弹性理论,发现该弹性理论可以精确给出硅材料任意载荷下的失稳应力。该研究为在连续介质框架下研究精确模拟材料在任意载荷下的失稳条件提供了理论基础,由于不同载荷可以导致不同的失稳模式,比如剪切应力下发生塑性变形,而在正应力下发生相变。因此该模型为连续介质力学提供了模拟任意载荷下导致不同失效模式的可能性 

Materials under complex loading develop large strains and often phase transformation via an elastic instability, as observed in both simple and complex systems. Here, we represent a material (exemplified for Si I) under large Lagrangian strains within a continuum description by a 5th-order elastic energy found by minimizing error relative to density functional theory (DFT) results. The Cauchy stress-Lagrangian strain curves for arbitrary complex loadings are in excellent correspondence with DFT results, including the elastic instability driving the Si I to Si II phase transformation and the shear instabilities. Phase transformation conditions for Si I to Si II under action of cubic axial stresses are linear in Cauchy stresses in agreement with DFT predictions. Such continuum elastic energy permits study of elastic instabilities and orientational dependence leading to different phase transformations, slip, twinning, or fracture, providing a fundamental basis for continuum physics simulations of crystal behavior under extreme loading.

EPIC STAR: a reliable and efficient approach for phonon- and impurity-limited charge transport calculations (EPIC STAR:一种可靠且高效的方法,用于声子和杂质限制的电荷输运计算)
Tianqi DengGang WuMichael B. SullivanZicong Marvin WongKedar HippalgaonkarJian-Sheng Wang & Shuo-Wang Yang
npj Computational Materials 6:46(2020)
doi:s41524-020-0316-7
Published online:7 May 2020

Abstract| Full Text | PDF OPEN

摘要:本研究提出了一种计算效率高的第一性原理方法,以预测半导体本征电荷输运性质。利用短程电子-声子散射的广义Eliashberg函数和长程电子-声子和电子-杂质散射的解析表达式,实现了不需要经验参数即可快速可靠地预测载流子迁移率和电子热电性能。该方法被命名为“能量依赖性声子-和杂质-限制的载流子散射近似(EPIC STAR)”方法。通过对几种代表性半导体的实验测量和其他理论方法的比较,验证了该方法的有效性,得到了极性和非极性、各向同性和各向异性材料的定量一致性。该方法的效率和鲁棒性有助于实现自动化预测和无监督预测,从而可对半导体材料进行高通量筛选和新材料发现,以作导电、热电和其他电子学方面的应用 

Abstract:A computationally efficient first-principles approach to predict intrinsic semiconductor charge transport properties is proposed. By using a generalized Eliashberg function for short-range electron–phonon scattering and analytical expressions for long-range electron–phonon and electron–impurity scattering, fast and reliable prediction of carrier mobility and electronic thermoelectric properties is realized without empirical parameters. This method, which is christened “Energy-dependent Phonon- and Impurity-limited Carrier Scattering Time AppRoximation (EPIC STAR)” approach, is validated by comparing with experimental measurements and other theoretical approaches for several representative semiconductors, from which quantitative agreement for both polar and non-polar, isotropic and anisotropic materials is achieved. The efficiency and robustness of this approach facilitate automated and unsupervised predictions, allowing high-throughput screening and materials discovery of semiconductor materials for conducting, thermoelectric, and other electronic applications.

Editorial Summary

EPIC STAR: a reliable and efficient approach for phonon- and impurity-limited charge transport calculationsEPIC STAR:一种可靠且高效的方法,用于声子和杂质限制的电荷输运计算

  该研究提出了一种计算效率高的第一性原理方法,以预测半导体本征电荷输运性质。来自新加坡科学技术研究局高性能计算研究所的Gang WuShuo-Wang Yang共同领导的团队,通过引入广义Eliashberg函数并加入光学声子极化、杂质散射和自由载流子屏蔽等过程,经过密度泛函微扰理论的计算,使该方法在计算量很小的情况下,尤其对非极性和极性半导体都能实现高保真度。该研究论证了极性光学声子散射的重要性,这表明在研究极性半导体的电子性质时,在没有考虑极性光学声子散射时,需格外小心。通过与SiGaAsMg2SiNbFeSb的实验和理论结果进行的比较,作者验证了这一方法的可靠性,并揭示了NaInSe2是一种潜在的新型热电材料。随着近年来高通量DFPT计算发展,该方法提出的方法可广泛应用于高迁移率半导体和高性能热电光伏材料的高通量筛选。 

A swift and automation-friendly approach for intrinsic and impurity-limited charge transport property prediction from first-principles is proposed. A team co-led by Gang Wu and Shuo-Wang Yang from the Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, by introducing generalized Eliashberg function and adding polar optical phonon contribution, impurity scattering, and free carrier screening, enabled the new approach to achieve high fidelity especially for both non-polar and polar semiconductors with very small computational cost after calculations by density functional perturbation theory (DFPT). They demonstrated the importance of polar optical phonon scattering, which suggests that care should be taken when the electronic properties of polar semiconductors are studied without polar optical phonon scattering. The authors verified this approach by comparing with previous experimental and theoretical results for Si, GaAs, Mg2Si, and NbFeSb, and also revealed NaInSe2 as a potential new thermoelectric material. As high-throughput DFPT computations have been demonstrated recently, this methodology can be widely applied for reliable high-throughput screening of high mobility semiconductors and high-performance thermoelectric and photovoltaic materials.

Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide (通过包括通过泰勒展开产生的原子力并应用于水和过渡金属氧化物中,有效地训练ANN势)
April M. CooperJohannes KastnerAlexander Urban & Nongnuch Artrith
npj Computational Materials 6:54(2020)
doi:s41524-020-0323-8
Published online:13 May 2020

Abstract| Full Text | PDF OPEN

摘要:基于人工神经网络(ANN)的经验势函数可以实现针对复杂材料的高精度(接近第一原理)大规模的原子模拟。对于分子动力学模拟,准确的能量和原子间作用力是先决条件,然而这需要同时基于电子结构计算得到的能量和力进行ANN训练。本工作,我们基于总能量的泰勒展开,提出了一种同时基于能量和力信息来训练ANN势的有效替代方法。通过将力信息转换为近似能量,可以避免传统力训练方法中计算量随原子数量二次方增长的关系,从而可以利用包含复杂原子结构的参考数据集进行训练。以不同系统为例,如水分子团簇、液态水和锂过渡金属氧化物,我们证明了所提出的力训练方法相对于仅依靠能量训练的方案具有显著提升。在训练中包含力信息可减少构建ANN势所需的参考数据集的大小,增加势函数的可移植性,并整体提升力预测的精度。对于水团簇,与所有力分量的显式训练相比,泰勒展开方法可降低约50?%的误差,而计算成本却要低得多。因此,这样的力训练方法,简化了用于模拟复杂材料能量和力的ANN势的构造过程,正如本研究在水和过渡金属氧化物中证明的情形那样 

Abstract:Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling of complex materials with near first-principles accuracy. For molecular dynamics simulations, accurate energies and interatomic forces are a prerequisite, but training ANN potentials simultaneously on energies and forces from electronic structure calculations is computationally demanding. Here, we introduce an efficient alternative method for the training of ANN potentials on energy and force information, based on an extrapolation of the total energy via a Taylor expansion. By translating the force information to approximate energies, the quadratic scaling with the number of atoms exhibited by conventional force-training methods can be avoided, which enables the training on reference datasets containing complex atomic structures. We demonstrate for different materials systems, clusters of water molecules, bulk liquid water, and a lithium transition-metal oxide that the proposed force-training approach provides substantial improvements over schemes that train on energies only. Including force information for training reduces the size of the reference datasets required for ANN potential construction, increases the transferability of the potential, and generally improves the force prediction accuracy. For a set of water clusters, the Taylor-expansion approach achieves around 50% of the force error improvement compared to the explicit training on all force components, at a much smaller computational cost. The alternative force-training approach thus simplifies the construction of general ANN potentials for the prediction of accurate energies and interatomic forces for diverse types of materials, as demonstrated here for water and a transition-metal oxide.

Editorial Summary

Transferring force into energy: accelerating construction of the machine learning potential变力为能量:加速机器学习势函数构建

  该研究提出一种基于原子间作用力信息来高效训练高精度神经网络经验势函数的方法。来自美国、德国和英国的联合研究团队,提出将力信息转换为近似能量,由此构建机器学习经验势的新方法。基于第一性原理集合机器学习,训练经验势对于大规模材料模拟来说十分重要。准确的经验势需要同时拟合体系能量和原子间作用力。而作用力的作为能量一阶导数,拟合比较复杂,其计算量与原子数目成二次方关系。 

  作者将作用力转化为能量巧妙的绕开了上述问题,由此可以采用较大体系的数据集开展训练。计算结果表明,与直接采用作用力训练势函数的方法相比,该方法不仅将势函数的精度提升了50%,同时计算效率明显提升。为进一步验证该方法的有效性,作者选取了三个具体的模型体系,即水分子团簇、液态水和复杂金属氧化物开展势函数训练。他们发现,该方法可以显著降低训练所需的数据集大小;具有很好的可移植性,可以准确预测数据集之外的新体系;可以同时提升作用力预测的准确性。简而言之,该方法简化了神经网络经验势的构造过程,有望推广应用于任意类型的材料中。

An efficient training method for high-precision artificial neural network (ANN) empirical potential has been proposed based on the information of interatomic forces. 

The joint research team from the United States, Germany and the United Kingdom proposed this new method to by Taylor extrapolation of the total energy with inter-atomic forces. Empirical interatomic potential trained by machine learning based on first principles is important for large-scale material simulation. Accurate empirical potential requires simultaneous fitting of the total energy and interatomic forces. The force, as the first derivative of energy, is more complex to fit, as the computational cost is quadratic with the number of atoms. By translating the force information into energy, the authors bypass this problem. They found that compared with the conventional training method directly by force, the accuracy of potential can be improved by 50% by the newly proposed method with the calculation efficiency significantly improved. To further validify this method, three specific model systems, namely water molecular clusters, liquid water and complex metal oxides, are selected to train the potential. The results showed that this method can significantly reduce the size of the data set needed for training; it has good transferability as could accurately predict the new structure outside the data set; it can also improve the accuracy of force prediction. In short, this method simplifies the construction process of ANN potential, and is expected to be applied to any kind of materials.

Design of two-dimensional carbon-nitride structures by tuning the nitrogen concentration (基于氮浓度的调控实现二维氮化碳结构的设计)
Saiyu Bu, Nan Yao, Michelle A. Hunter, Debra J. Searles & Qinghong Yuan
npj Computational Materials 6:128(2020)
doi:s41524-020-00393-5
Published online:21 August 2020

Abstract| Full Text | PDF OPEN

摘要:氮掺杂石墨烯(NG)的性质与本征石墨烯的显著差异使其在物理、化学、生物和材料科学等领域中具有更广泛的应用。近些年NG的研究引起了越来越多的关注。然而,目前大多数实验制备的NG通常氮浓度较低且多种类型的氮混合掺杂,限制了不同类型NG优异的物理和化学性质的应用。在本研究工作中,我们运用第一性原理计算与局域粒子群优化算法相结合的方法,探索了不同C/N比率下二维氮化碳(C1-xNx)可能的稳定结构。基于理论计算得到的不同结构的C1-xNx的形成能,得出了低氮掺杂浓度下C1-xNx结构中同时含有石墨氮和吡啶氮的结论,并且发现低氮掺杂浓度的C1-xNx结构的形成能要比高N掺杂浓度的结构的形成能低得多,这意味着合成低氮掺杂浓度的NGs在能量上更为有利。这一系列结果解释了实验中观察到的NG中石墨氮和吡啶氮的共存以及实验中氮掺杂浓度过低的现象。计算还表明,如果氮掺杂浓度大于0.25,则C1-xNx结构中吡啶氮占优势。进一步,我们提出了通过控制C和N源的前驱体以及生长温度来克服低N掺杂浓度和N混合掺杂的限制,实现NGs的可控制备的实验设想 

Abstract:Nitrogen-doped graphene (NG) has attracted increasing attention because its properties are significantly different to pristine graphene, making it useful for various applications in physics, chemistry, biology, and materials science. However, the NGs that can currently be fabricated using most experimental methods always have low N concentrations and a mixture of N dopants, which limits the desirable physical and chemical properties. In this work, first principles calculations combined with the local particle-swarm optimization algorithm method were applied to explore possible stable structures of 2D carbon nitrides (C1-xNx) with various C/N ratios. It is predicted that C1-xNx structures with low N-doping concentration contain both graphitic and pyridinic N based on their calculated formation energies, which explains the experimentally observed coexistence of graphitic and pyridinic N in NG. However, pyridinic N is predominant in C1-xNx when the N concentration is above 0.25. In addition, C1-xNx structures with low N-doping concentration were found to have considerably lower formation energies than those with a high N concentration, which means synthesized NGs with low N-doping concentration are favorable. Moreover, we found the restrictions of mixed doping and low N concentration can be circumvented by using different C and N feedstocks, and by growing NG at lower temperatures.

Editorial Summary

Structure of nitrogen-doped graphene: How to tune it?氮掺杂石墨烯的结构调控:路在何方?

对石墨烯进行氮掺杂是打开石墨烯带隙,提高其自由载流子密度,拓展石墨烯应用的重要方法之一。然而目前实验上合成的氮掺杂石墨烯(NG)中氮原子的掺杂比率普遍较低,掺杂的氮原子通常以吡啶氮、吡咯氮、石墨氮等多种形式共存,并且掺杂氮原子在石墨烯的面内排列无序,这些特点极大地限制了NG的实际应用。本研究基于理论计算,揭示了NG中氮掺杂浓度低和各种类型氮混合掺杂的本质原因,并提出可通过控制前驱体种类、反应温度和压强对NG的氮掺杂浓度和类型进行调控的设想。
来自华东师范大学精密光谱科学与技术国家重点实验室的博士生补赛玉(昆士兰大学交流学生)及其导师袁清红研究员与澳大利亚昆士兰大学澳大利亚生物工程及纳米科技研究所的Debra J. Searles教授等人,采用第一性原理计算与粒子群优化算法相结合的方法,对不同氮掺杂浓度的NG的稳定结构和能量进行了研究,揭示了目前NG合成中氮掺杂浓度低和不同类型氮原子混合掺杂的原因,提出了调控氮原子掺杂浓度和类型的有效方法。研究发现,NG的稳定结构与其中的氮原子浓度密切相关,低氮掺杂浓度下,NG中的石墨氮和吡啶氮具有相近的形成能,因而更易形成石墨氮和吡啶氮共掺杂的结构。随着氮原子掺杂浓度的增加,石墨氮掺杂石墨烯的形成能要高于吡啶氮掺杂石墨烯的形成能,因而更易形成吡啶氮掺杂的石墨烯结构。特别是,当N原子掺杂浓度高于0.25时,NG中以吡啶氮掺杂为主。此外,该项研究还表明,低氮掺杂浓度的NG具有更低的形成能。这一系列研究结果解释了目前实验上NG中氮原子掺杂浓度低,以及多种类型的氮混合掺杂的实验现象。基于理论研究结果,研究人员进一步提出可通过控制NG合成过程中的前驱体种类、生长温度和压强等实现碳和氮原子化学势的调控,从而实现NG中氮原子的掺杂类型和浓度的调控。该研究通过对不同氮掺杂浓度下NG的结构和能量进行研究,为NG的可控合成提供了理论依据

Nitrogen doping of graphene is one of the important methods to open the bandgap of graphene, increasing its carrier density, making graphene active catalysts for many reactions, and thus expand the applications of graphene. However, the doping ratio of nitrogen atoms in experimentally synthesized nitrogen-doped graphene (NG) is generally low. Moreover, the doped nitrogen atoms usually coexist in various forms such as pyridinic, pyrrolic, and graphitic N, and the doped N atoms are usually randomly distributed in graphene. The uncontrollable doping greatly limits the applications of NG and thus understanding the mechanism of N doping in graphene and finding out potential means for the controllable doping is highly desired. In this work, based on theoretical calculations, we revealed the inherent reasons of low doping concentration and co-doping of different types of N centers, and proposed effective methods to control the N doping concentration and the type of nitrogen-dopants.  

Saiyu Bu, a PhD student from State Key Laboratory of Precision Spectroscopy, East China Normal University, (she was also a visiting student at The University of Queensland) and her supervisor Prof. Qinghong Yuan, together with Professor Debra J. Searles, from the Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, Australia, et al., studied the stable structures and formation energies of NG with different doping concentration and types , revealed the reasons for the low nitrogen doping concentration and mixed doping of different types of nitrogen centers in the synthesized NG, and proposed methods to regulate the concentration and/or type of N dopant. It was found that the stable structures of NGs are highly dependent on their N doping concentration. At low N doping concentration, pyridinic N and graphitic N doped in graphene have similar formation energies, thus the NGs are more likely to be co-doped with both pyridinic and graphitic N centers. With the increase of doping concentration, the formation energy of graphitic N doped graphene becomes higher than that of pyridinic N doped graphene, leading to the preference of pyridinic N centers. Moreover, it is found that NGs with low a N concentration have lower formation energies and thus better stability. The theoretical calculations explained the current experimental observations of NGs with low N concentration and co-dopants of graphitic and pyridinic N centers. On the basis of the calculation results, the researchers proposed that the structure of NG can be well modulated by controlling the N and C feedstock, growth temperature and pressure etc.  

This study provides theoretical guideline for the synthesis of NG in a manner.
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