SPECIAL TOPIC — Smart design of materials and design of smart materials

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    Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression
    Ming-Jun Li(励明君), Lina Yang(杨哩娜), Deng Wang(王登), Si-Yi Wang(王斯艺), Jing-Nan Tang(唐静楠), Yi Jiang(姜毅), and Jie Chen(陈杰)
    Chin. Phys. B, 2023, 32 (4): 046203.   DOI: 10.1088/1674-1056/acb76a
    Abstract167)   HTML215)    PDF (5791KB)(91)      
    The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment. In some cases, such as launching missiles or rockets, layered pavements are required to bear large impulse load. However, traditional methods cannot non-destructively and quickly detect the internal structural of pavements. Thus, accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity. In recent years, machine learning has shown great superiority in solving nonlinear problems. In this work, we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing. The regression coefficient R2 of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses, which indicates that the prediction results have great consistency with finite element simulation results. This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements, and has application potential in non-destructive evaluation of pavement structure.
    Forecasting solar still performance from conventional weather data variation by machine learning method
    Wenjie Gao(高文杰), Leshan Shen(沈乐山), Senshan Sun(孙森山), Guilong Peng(彭桂龙), Zhen Shen(申震), Yunpeng Wang(王云鹏), AbdAllah Wagih Kandeal, Zhouyang Luo(骆周扬), A. E. Kabeel, Jianqun Zhang(张坚群), Hua Bao(鲍华), and Nuo Yang(杨诺)
    Chin. Phys. B, 2023, 32 (4): 048801.   DOI: 10.1088/1674-1056/ac989f
    Abstract394)   HTML243)    PDF (1066KB)(118)      
    Solar stills are considered an effective method to solve the scarcity of drinkable water. However, it is still missing a way to forecast its production. Herein, it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data. The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm. The required data to train the model are obtained from daily measurements lasting 9 months. To validate the accuracy model, the determination coefficients of two types of solar stills are calculated as 0.935 and 0.929, respectively, which are much higher than the value of both multiple linear regression (0.767) and the traditional models (0.829 and 0.847). Moreover, by applying the model, we predicted the freshwater production of four cities in China. The predicted production is approved to be reliable by a high value of correlation (0.868) between the predicted production and the solar insolation. With the help of the forecasting model, it would greatly promote the global application of solar stills.
    Prediction of lattice thermal conductivity with two-stage interpretable machine learning
    Jinlong Hu(胡锦龙), Yuting Zuo(左钰婷), Yuzhou Hao(郝昱州), Guoyu Shu(舒国钰), Yang Wang(王洋), Minxuan Feng(冯敏轩), Xuejie Li(李雪洁), Xiaoying Wang(王晓莹), Jun Sun(孙军), Xiangdong Ding(丁向东), Zhibin Gao(高志斌), Guimei Zhu(朱桂妹), and Baowen Li(李保文)
    Chin. Phys. B, 2023, 32 (4): 046301.   DOI: 10.1088/1674-1056/acbaf4
    Abstract263)   HTML235)    PDF (2150KB)(165)      
    Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network (CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model-sure independence screening and sparsifying operator (SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database (OQMD) (https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultra-low lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.
    Stress effect on lattice thermal conductivity of anode material NiNb2O6 for lithium-ion batteries
    Ao Chen(陈奥), Hua Tong(童话), Cheng-Wei Wu(吴成伟), Guofeng Xie(谢国锋), Zhong-Xiang Xie(谢忠祥), Chang-Qing Xiang(向长青), and Wu-Xing Zhou(周五星)
    Chin. Phys. B, 2023, 32 (5): 058201.   DOI: 10.1088/1674-1056/acaa2d
    Abstract124)   HTML8)    PDF (3943KB)(57)      
    The thermal transport properties of NiNb$_{2}$O$_{6}$ as anode material for lithium-ion battery and the effect of strain were studied by machine learning interatomic potential combined with Boltzmann transport equation. The results show that the lattice thermal conductivity of NiNb$_{2}$O$_{6}$ along the three crystal directions [100], [010], and [001] are 0.947 W$\cdot$m$^{-1}\cdot$K$^{-1}$, 0.727 W$\cdot$m$^{-1}\cdot$K$^{-1}$, and 0.465 W$\cdot$m$^{-1}\cdot$K$^{-1}$, respectively, indicating the anisotropy of the lattice thermal conductivity of NiNb$_{2}$O$_{6}$. This anisotropy of the lattice thermal conductivity stems from the significant difference of phonon group velocities in different crystal directions of NiNb$_{2}$O$_{6}$. When the tensile strain is applied along the [001] crystal direction, the lattice thermal conductivity in all three directions decreases. However, when the compressive strain is applied, the lattice thermal conductivity in the [100] and [010] crystal directions is increased, while the lattice thermal conductivity in the [001] crystal direction is abnormally reduced due to the significant inhibition of compressive strain on the group velocity. These indicate that the anisotropy of thermal conductivity of NiNb$_{2}$O$_{6}$ can be enhanced by the compressive strain, and reduced by the tensile strain.
    Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles
    Xiaoyi Ma(马宵怡), Yufeng Luo(罗宇峰), Mengke Li(李梦可), Wenyan Jiao(焦文艳), Hongmei Yuan(袁红梅), Huijun Liu(刘惠军), and Ying Fang(方颖)
    Chin. Phys. B, 2023, 32 (5): 057306.   DOI: 10.1088/1674-1056/acb2c3
    Abstract111)   HTML9)    PDF (1172KB)(65)      
    The novel electronic properties of bilayer graphene can be fine-tuned via twisting, which may induce flat bands around the Fermi level with nontrivial topology. In general, the band structure of such twisted bilayer graphene (TBG) can be theoretically obtained by using first-principles calculations, tight-binding method, or continuum model, which are either computationally demanding or parameters dependent. In this work, by using the sure independence screening sparsifying operator method, we propose a physically interpretable three-dimensional (3D) descriptor which can be utilized to readily obtain the Γ-point gap of TBG at arbitrary twist angles and different interlayer spacings. The strong predictive power of the descriptor is demonstrated by a high Pearson coefficient of 99% for both the training and testing data. To go further, we adopt the neural network algorithm to accurately probe the flat bands of TBG at various twist angles, which can accelerate the study of strong correlation physics associated with such a fundamental characteristic, especially for those systems with a larger number of atoms in the unit cell.
    Reconstruction and stability of Fe3O4(001) surface: An investigation based on particle swarm optimization and machine learning
    Hongsheng Liu(柳洪盛), Yuanyuan Zhao(赵圆圆), Shi Qiu(邱实), Jijun Zhao(赵纪军), and Junfeng Gao(高峻峰)
    Chin. Phys. B, 2023, 32 (5): 056802.   DOI: 10.1088/1674-1056/acb9e4
    Abstract104)   HTML8)    PDF (2412KB)(59)      
    Magnetite nanoparticles show promising applications in drug delivery, catalysis, and spintronics. The surface of magnetite plays an important role in these applications. Therefore, it is critical to understand the surface structure of Fe3O4 at atomic scale. Here, using a combination of first-principles calculations, particle swarm optimization (PSO) method and machine learning, we investigate the possible reconstruction and stability of Fe3O4(001) surface. The results show that besides the subsurface cation vacancy (SCV) reconstruction, an A layer with Fe vacancy (A-layer-VFe) reconstruction of the (001) surface also shows very low surface energy especially at oxygen poor condition. Molecular dynamics simulation based on the iron-oxygen interaction potential function fitted by machine learning further confirms the thermodynamic stability of the A-layer-VFe reconstruction. Our results are also instructive for the study of surface reconstruction of other metal oxides.
    Designing radiative cooling metamaterials for passive thermal management by particle swarm optimization
    Shenshen Yan(闫申申), Yan Liu(刘岩), Zi Wang(王子), Xiaohua Lan(兰晓华), Yi Wang(汪毅), and Jie Ren(任捷)
    Chin. Phys. B, 2023, 32 (5): 057802.   DOI: 10.1088/1674-1056/acc061
    Abstract115)   HTML8)    PDF (1179KB)(140)      
    The passive radiative cooling technology shows a great potential application on reducing the enormous global energy consumption. The multilayer metamaterials could enhance the radiative cooling performance. However, it is a challenge to design the radiative cooler. In this work, based on the particle swarm optimization (PSO) evolutionary algorithm, we develop an intelligent workflow in designing photonic radiative cooling metamaterials. Specifically, we design two 10-layer ${\rm SiO_2}$ radiative coolers doped by cylindrical ${\rm MgF_2}$ or air impurities, possessing high emissivity within the selective (8-13 μm) and broadband (8-25 μm) atmospheric transparency windows, respectively. Our two kinds of coolers demonstrate power density as high as 119 W/m$^2$ and 132 W/m$^2$ at the room temperature (300 K). Our scheme does not rely on the usage of special materials, forming high-performing metamaterials with conventional poor-performing components. This significant improvement of the emission spectra proves the effectiveness of our inverse design algorithm in boosting the discovery of high-performing functional metamaterials.
    Lattice thermal conductivity switching via structural phase transition in ferromagnetic VI3
    Chao Wu(吴超) and Chenhan Liu(刘晨晗)
    Chin. Phys. B, 2023, 32 (5): 056502.   DOI: 10.1088/1674-1056/acb764
    Abstract147)   HTML9)    PDF (2481KB)(72)      
    The realization of reversible thermal conductivity through ferromagnetic ordering can improve the heat management and energy efficiency in magnetic materials-based devices. VI$_{3}$, as a new layered ferromagnetic semiconductor, exhibits a structural phase transition from monoclinic ($C2/m$) to rhombohedral ($R\bar{3}$) phase as temperature decreases, making it a suitable platform to investigate thermal switching in magnetic phase transition materials. This work reveals that the thermal switching ratio of VI$_{3}$ can reach 3.9 along the $a$-axis. Mechanical properties analysis indicates that the $C2/m$ structure is stiffer than the $R\bar{3}$ one, causing the larger phonon velocity in $C2/m$ phase. Moreover, due to the fewer phonon branches in $C2/m$ phase, the number of phonon-phonon scattering channels in $C2/m$ phase is smaller compared to that of $R\bar{3}$ phase. Both the larger phonon velocity and the longer phonon lifetime lead to larger lattice thermal conductivity in $C2/m$ phase. This study uncovers the mechanical and thermal properties of VI$_{3}$, which provides useful guides for designing magnetic materials-based devices such as thermal switch.
    Impeded thermal transport in aperiodic BN/C nanotube superlattices due to phonon Anderson localization
    Luyi Sun(孙路易), Fangyuan Zhai(翟方园), Zengqiang Cao(曹增强), Xiaoyu Huang(黄晓宇), Chunsheng Guo(郭春生), Hongyan Wang(王红艳), and Yuxiang Ni(倪宇翔)
    Chin. Phys. B, 2023, 32 (5): 056301.   DOI: 10.1088/1674-1056/acb9e7
    Abstract90)   HTML8)    PDF (1573KB)(55)      
    Anderson localization of phonons is a kind of phonon wave effect, which has been proved to occur in many structures with disorders. In this work, we introduced aperiodicity to boron nitride/carbon nanotube superlattices (BN/C NT SLs), and used molecular dynamics to calculate the thermal conductivity and the phonon transmission spectrum of the models. The existence of phonon Anderson localization was proved in this quasi one-dimensional structure by analyzing the phonon transmission spectra. Moreover, we introduced interfacial mixing to the aperiodic BN/C NT SLs and found that the coexistence of the two disorder entities (aperiodicity and interfacial mixing) can further decrease the thermal conductivity. In addition, we also showed that anharmonicity can destroy phonon localization at high temperatures. This work provides a reference for designing thermoelectric materials with low thermal conductivity by taking advantage of phonon localization.
    Thermal transport properties of two-dimensional boron dichalcogenides from a first-principles and machine learning approach
    Zhanjun Qiu(邱占均), Yanxiao Hu(胡晏箫), Ding Li(李顶), Tao Hu(胡涛), Hong Xiao(肖红),Chunbao Feng(冯春宝), and Dengfeng Li(李登峰)
    Chin. Phys. B, 2023, 32 (5): 054402.   DOI: 10.1088/1674-1056/acb9e6
    Abstract152)   HTML9)    PDF (10117KB)(106)      
    The investigation of thermal transport is crucial to the thermal management of modern electronic devices. To obtain the thermal conductivity through solution of the Boltzmann transport equation, calculation of the anharmonic interatomic force constants has a high computational cost based on the current method of single-point density functional theory force calculation. The recent suggested machine learning interatomic potentials (MLIPs) method can avoid these huge computational demands. In this work, we study the thermal conductivity of two-dimensional MoS$_{2}$-like hexagonal boron dichalcogenides (H-B$_{2}{VI}_{2}$; ${VI} = {\rm S}$, Se, Te) with a combination of MLIPs and the phonon Boltzmann transport equation. The room-temperature thermal conductivity of H-B$_{2}$S$_{2}$ can reach up to 336 W$\cdot $m$^{-1}\cdot $K$^{-1}$, obviously larger than that of H-B$_{2}$Se$_{2}$ and H-B$_{2}$Te$_{2}$. This is mainly due to the difference in phonon group velocity. By substituting the different chalcogen elements in the second sublayer, H-B$_{2}{VI}{VI}^\prime $ have lower thermal conductivity than H-B$_{2}{VI}_{2}$. The room-temperature thermal conductivity of B$_{2}$STe is only 11% of that of H-B$_{2}$S$_{2}$. This can be explained by comparing phonon group velocity and phonon relaxation time. The MLIP method is proved to be an efficient method for studying the thermal conductivity of materials, and H-B$_{2}$S$_{2}$-based nanodevices have excellent thermal conduction.
    A thermal conductivity switch via the reversible 2H-1T' phase transition in monolayer MoTe2
    Dingbo Zhang(张定波), Weijun Ren(任卫君), Ke Wang(王珂), Shuai Chen(陈帅),Lifa Zhang(张力发), Yuxiang Ni(倪宇翔), and Gang Zhang(张刚)
    Chin. Phys. B, 2023, 32 (5): 050505.   DOI: 10.1088/1674-1056/acbaf0
    Abstract154)   HTML9)    PDF (5908KB)(132)      
    The two-dimensional (2D) material-based thermal switch is attracting attention due to its novel applications, such as energy conversion and thermal management, in nanoscale devices. In this paper, we observed that the reversible 2H-1T' phase transition in MoTe2 is associated with about a fourfold/tenfold change in thermal conductivity along the X/Y direction by using first-principles calculations. This phenomenon can be profoundly understood by comparing the Mo-Te bonding strength between the two phases. The 2H-MoTe2 has one stronger bonding type, while 1T'-MoTe2 has three weaker types of bonds, suggesting bonding inhomogeneity in 1T'-MoTe2. Meanwhile, the bonding inhomogeneity can induce more scattering of vibration modes. The weaker bonding indicates a softer structure, resulting in lower phonon group velocity, a shorter phonon relaxation lifetime and larger Grüneisen constants. The impact caused by the 2H to 1T' phase transition in MoTe2 hinders the propagation of phonons, thereby reducing thermal conductivity. Our study describes the possibility for the provision of the MoTe2-based controllable and reversible thermal switch device.
    Enhanced mechanical and thermal properties of two-dimensional SiC and GeC with temperature and size dependence
    Lei Huang(黄磊), Kai Ren(任凯), Huanping Zhang(张焕萍), and Huasong Qin(覃华松)
    Chin. Phys. B, 2023, 32 (7): 076103.   DOI: 10.1088/1674-1056/acc78f
    Abstract95)   HTML2)    PDF (2748KB)(76)      
    Two-dimensional materials with novel mechanical and thermal properties are available for sensors, photodetectors, thermoelectric, crystal diode and flexible nanodevices. In this investigation, the mechanical and thermal properties of pristine SiC and GeC are explored by molecular dynamics simulations. First, the fracture strength and fracture strain behaviors are addressed in the zigzag and armchair directions at 300 K. The excellent toughness of SiC and GeC is demonstrated by the maximal fracture strain of 0.43 and 0.47 in the zigzag direction, respectively. The temperature-tunable tensile strength of SiC and GeC is also investigated. Then, using non-equilibrium molecular dynamics (NEMD) calculations, the thermal performances of SiC and GeC are explored. In particular, the thermal conductivity of SiC and GeC shows a pronounced size dependence and reaches up to 85.67 W·m-1·K-1 and 34.37 W·m-1·K-1, respectively. The goal of our work is to provide a theoretical framework that can be used in the near future. This will enable us to design an efficient thermal management scheme for two-dimensional materials in electronics and optoelectronics.
    Size effect on transverse free vibrations of ultrafine nanothreads
    Zhuoqun Zheng(郑卓群), Han Li(李晗), Zhu Su(宿柱), Nan Ding(丁楠), Xu Xu(徐旭),Haifei Zhan(占海飞), and Lifeng Wang(王立峰)
    Chin. Phys. B, 2023, 32 (9): 096202.   DOI: 10.1088/1674-1056/ace037
    Abstract68)   HTML0)    PDF (1679KB)(27)      
    Due to their unique properties and appealing applications, low dimensional sp3 carbon nanostructures have attracted increasing attention recently. Based on the beam theory and atomistic studies, this work carries out a comprehensive investigation on the vibrational properties of the ultrathin carbon nanothreads (NTH). Size effect is observed in transverse free vibrations of NTHs. To quantify such effects, the modified couple stress theory (MCST) is utilized to modify the Timoshenko beam theory. According to the first four order frequencies of NTHs from atomistic simulations, the critical length scale parameter of MCST is calibrated as 0.1 nm. It is shown that MCST has minor effect on the first four order modal shapes, except for the clamped boundary. MCST makes the modal shapes at the clamped boundary closer to those observed in atomistic simulations. This study suggests that to some extent the MCST-based Timoshenko beam theory can well describe the transverse vibration characteristics of the ultrafine NTHs, which are helpful for designing and fabricating the NTH-based nanoscale mechanical resonators.
    Crysformer: An attention-based graph neural network for properties prediction of crystals
    Tian Wang(王田), Jiahui Chen(陈家辉), Jing Teng(滕婧), Jingang Shi(史金钢),Xinhua Zeng(曾新华), and Hichem Snoussi
    Chin. Phys. B, 2023, 32 (9): 090703.   DOI: 10.1088/1674-1056/ace247
    Abstract72)   HTML0)    PDF (591KB)(34)      
    We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory (DFT)-based calculations. Instead, we utilize an attention-based graph neural network that yields high-accuracy predictions. Our approach employs two attention mechanisms that allow for message passing on the crystal graphs, which in turn enable the model to selectively attend to pertinent atoms and their local environments, thereby improving performance. We conduct comprehensive experiments to validate our approach, which demonstrates that our method surpasses existing methods in terms of predictive accuracy. Our results suggest that deep learning, particularly attention-based networks, holds significant promise for predicting crystal material properties, with implications for material discovery and the refined intelligent systems.
    Two-dimensional dumbbell silicene as a promising anode material for (Li/Na/K)-ion batteries
    Man Liu(刘曼), Zishuang Cheng(程子爽), Xiaoming Zhang(张小明), Yefeng Li(李叶枫), Lei Jin(靳蕾),Cong Liu(刘丛), Xuefang Dai(代学芳), Ying Liu(刘影), Xiaotian Wang(王啸天), and Guodong Liu(刘国栋)
    Chin. Phys. B, 2023, 32 (9): 096303.   DOI: 10.1088/1674-1056/acd623
    Abstract74)   HTML3)    PDF (1376KB)(33)      
    Rechargeable ion batteries require anode materials with excellent performance, presenting a key challenge for researchers. This paper explores the potential of using two-dimensional dumbbell silicene as an anode material for alkali metal ion batteries through density functional theory (DFT) calculations. Our findings demonstrate that alkali metal ions have negative adsorption energies on dumbbell silicene, and the energy barriers for Li/Na/K ion diffusion are as low as 0.032 eV/0.055 eV/0.21 eV, indicating that metal ions can easily diffuse across the entire surface of dumbbell silicene. Additionally, the average open circuit voltages of dumbbell silicene as anode for Li-ion, Na-ion, and K-ion batteries are 0.42 V, 0.41 V, and 0.60 V, respectively, with corresponding storage capacities of 716 mAh/g, 622 mAh/g, and 716 mAh/g. These results suggest that dumbbell silicene is an ideal anode material for Li-ion, Na-ion, and K-ion batteries, with high capacity, low open circuit voltage, and high ion diffusion kinetics. Moreover, our calculations show that the theoretical capacities obtained using DFT-D2 are higher than those obtained using DFT-D3, providing a valuable reference for subsequent theoretical calculations.