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Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential
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作者 王玺 唐孟 +3 位作者 蒋明璇 陈阳春 刘智骁 邓辉球 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第7期456-465,共10页
Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of dis... Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2). 展开更多
关键词 zirconium hydride deep learning potential radiation defects molecular dynamics threshold energy of displacement
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Geometries and electronic structures of Zr_(n)Cu(n=2–12) clusters: A joint machine-learning potential density functional theory investigation
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作者 王一志 崔秀花 +3 位作者 刘静 井群 段海明 曹海宾 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第1期595-602,共8页
Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedra... Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds. 展开更多
关键词 geometries and electronic structures magnetic and chemical bonds machine learning potentials Zr–Cu clusters
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Thermal conductivity of GeTe crystals based on machine learning potentials
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作者 张健 张昊春 +1 位作者 李伟峰 张刚 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第4期104-107,共4页
GeTe has attracted extensive research interest for thermoelectric applications.In this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussi... GeTe has attracted extensive research interest for thermoelectric applications.In this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussian approximation potential(GAP)as a reference.The phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results,with the GAP potential having higher accuracy.Next,the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials,and both of them are in good agreement with the experimental results;however,the calculation speed when using the NEP potential is about 500 times faster than when using the GAP potential.Finally,the lattice thermal conductivity in the range of 300 K-600 K is calculated using the NEP potential.The lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic effect.This study provides a theoretical tool for the study of the thermal conductivity of GeTe. 展开更多
关键词 machine learning potentials thermal conductivity molecular dynamics
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Machine learning potential aided structure search for low-lying candidates of Au clusters
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作者 Tonghe Ying Jianbao Zhu Wenguang Zhu 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第7期613-619,共7页
A machine learning(ML)potential for Au clusters is developed through training on a dataset including several different sized clusters.This ML potential accurately covers the whole configuration space of Au clusters in... A machine learning(ML)potential for Au clusters is developed through training on a dataset including several different sized clusters.This ML potential accurately covers the whole configuration space of Au clusters in a broad size range,thus expressing a good performance in search of their global minimum energy structures.Based on our potential,the low-lying structures of 17 different sized Au clusters are identified,which shows that small sized Au clusters tend to form planar structures while large ones are more likely to be stereo,revealing the critical size for the two-dimensional(2D)to three-dimensional(3D)structural transition.Our calculations demonstrate that ML is indeed powerful in describing the interaction of Au atoms and provides a new paradigm on accelerating the search of structures. 展开更多
关键词 machine learning potential gold cluster first-principles calculation
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Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning 被引量:1
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作者 Fu-Qiang Gong Ke Xiong Jun Cheng 《National Science Open》 2024年第2期7-20,共14页
Nanoparticles,distinguished by their unique chemical and physical properties,have emerged as focal points within the realm of materials science.Traditional theoretical approaches for atomic simulations mainly include ... Nanoparticles,distinguished by their unique chemical and physical properties,have emerged as focal points within the realm of materials science.Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations,with the former offering efficiency but limited reliability,and the latter providing accuracy but restricted to systems of relatively small sizes.Herein,we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a training dataset.This includes small nanoclusters,nanoparticles,as well as surface and bulk systems with periodic boundary conditions.The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes.Through rigorous validation,we have shown that our trained machine learning potential is capable of effectively driving molecular dynamics simulations of nanoparticles across a wide temperature range,especially within the nanoscale regime.Remarkably,this is achieved while preserving the accuracy typically associated with ab initio methods. 展开更多
关键词 condensed matter physics nanoparticles machine learning potential WORKFLOW
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Theoretical prediction on thermal and mechanical properties of high entropy(Zr(0.2)Hf(0.2)Ti(0.2)Nb(0.2)Ta(0.2))C by deep learning potential 被引量:15
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作者 Fu-Zhi Dai Bo Wen +2 位作者 Yinjie Sun Huimin Xiang Yanchun Zhou 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2020年第8期168-174,共7页
High entropy materials(HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials. T... High entropy materials(HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials. Though a large number of HEMs have emerged, there is still in lack of theoretical predictions and simulations on HEMs, which is probably caused by the chemical complexity of HEMs. In this work,we demonstrate that the machine learning potentials developed in recent years can overcome the complexity of HEMs, and serve as powerful theoretical tools to simulate HEMs. A deep learning potential(DLP) for high entropy(Zr(0.2) Hf(0.2) Ti(0.2) Nb(0.2) Ta(0.2))C is fitted with the prediction error in energy and force being 9.4 me V/atom and 217 me V/?, respectively. The reliability and generality of the DLP are affirmed,since it can accurately predict lattice parameters and elastic constants of mono-phase carbides TMC(TM = Ti, Zr, Hf, Nb and Ta). Lattice constants(increase from 4.5707 ? to 4.6727 ?), thermal expansion coefficients(increase from 7.85×10-6 K^(-1) to 10.58×10-6 K^(-1)), phonon thermal conductivities(decrease from 2.02 W·m-1·K^(-1) to 0.95 W·m-1·K^(-1)), and elastic properties of high entropy(Zr(0.2) Hf(0.2) Ti(0.2) Nb(0.2) Ta(0.2))C in temperature ranging from 0°C to 2400°C are predicted by molecular dynamics simulations. The predicted room temperature properties agree well with experimental measurements, indicating the high accuracy of the DLP. With introducing of machine learning potentials, many problems that are intractable by traditional methods can be handled now. It is hopeful that deep insight into HEMs can be obtained in the future by such powerful methods. 展开更多
关键词 High entropy ceramics Machine learning potential Thermal properties Mechanical properties Molecular dynamics Simulation
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Temperature Dependent Thermal and Elastic Properties of High Entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2):Molecular Dynamics Simulation by Deep Learning Potential 被引量:9
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作者 Fu-Zhi Dai Yinjie Sun +2 位作者 Bo Wen Huimin Xiang Yanchun Zhou 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2021年第13期8-15,共8页
High entropy diborides are new categories of ultra-high temperature ceramics,which are believed promising candidates for applications in hypersonic vehicles.However,knowledge on high temperature thermal and mechanical... High entropy diborides are new categories of ultra-high temperature ceramics,which are believed promising candidates for applications in hypersonic vehicles.However,knowledge on high temperature thermal and mechanical properties of high entropy diborides is still lacking unit now.In this work,variations of thermal and elastic properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) with respect to temperature were predicted by molecular dynamics simulations.Firstly,a deep learning potential for Ti-Zr-Hf-Nb-Ta-B diboride system was fitted with its prediction error in energy and force respectively being 9.2 meV/atom and 208 meV/A,in comparison with first-principles calculations.Then,temperature dependent lattice constants,anisotropic thermal expansions,anisotropic phonon thermal conductivities,and elastic properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) from 0℃to 2400℃were evaluated,where the predicted room temperature values agree well with experimental measurements.In addition,intrinsic lattice distortions of(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) were analyzed by displacements of atoms from their ideal positions,which are in an order of 10^(-3) A and one order of magnitude smaller than those in(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))C.It indicates that lattice distortions in(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) is not so severe as expected.With the new paradigm of machine learning potential,deep insight into high entropy materials can be achieved in the future,since the chemical and structural complexly in high entropy materials can be well handled by machine learning potential. 展开更多
关键词 High entropy diborides Machine learning potential Thermal properties Elastic properties Molecular dynamics
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Nanotwinning induced decreased lattice thermal conductivity of high temperature thermoelectric boron subphosphide (B12P2) from deep learning potential simulations
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作者 Xiaona Huang Yidi Shen Qi An 《Energy and AI》 2022年第2期5-12,共8页
Boron subphosphide(B_(12)P_(2))is a promising high temperature thermoelectric material due to its good thermal stability,and chemical inertness.However,the thermal properties of B_(12)P_(2) have not been well revealed... Boron subphosphide(B_(12)P_(2))is a promising high temperature thermoelectric material due to its good thermal stability,and chemical inertness.However,the thermal properties of B_(12)P_(2) have not been well revealed so far.Here,we first develop a deep learning potential for B_(12)P_(2) based on quantum mechanical calculations.Then the isotropic lattice thermal conductivity(LTC)of crystalline B_(12)P_(2) is predicted to be 39.70±4.38 W/m⋅K from molecular dynamics simulations using this deep learning potential.The LTC exhibits the relationship ofκL~1/T in the temperature range of 300~1500 K.More important,a twin boundary strategy is proposed to reduce the LTC of B_(12)P_(2).In nanotwinned B_(12)P_(2),the phonon transport in all directions is significantly suppressed by twin boundaries(TBs)with the isotropic LTC of 15.85±2.70 W/m⋅K,especially in the direction normal to the TB plane.The decrease of vibrational density of states and phonon participation ratio due to TBs’phonon scattering is the main reason of the low LTC in nanotwinned B_(12)P_(2).In addition,the elastic moduli(B and G)of B_(12)P_(2) crystal decrease by less than 7%after inducing TBs,which suggests that the mechanical properties are not significantly affected by TBs.Overall,this work enriches our understanding of the thermal properties of B_(12)P_(2) and offers a promising approach,i.e.,introducing TBs,to design high-performance thermoelectric materials. 展开更多
关键词 Nanotwinned B_(12)P_(2) Lattice thermal conductivity High temperature thermoelectric material Deep learning potential
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An artificial neural network potential for uranium metal at low pressures
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作者 郝茂生 管鹏飞 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第9期514-521,共8页
Based on machine learning,the high-dimensional fitting of potential energy surfaces under the framework of first principles provides density-functional accuracy of atomic interaction potential for high-precision and l... Based on machine learning,the high-dimensional fitting of potential energy surfaces under the framework of first principles provides density-functional accuracy of atomic interaction potential for high-precision and large-scale simulation of alloy materials.In this paper,we obtained the high-dimensional neural network(NN)potential function of uranium metal by training a large amount of first-principles calculated data.The lattice constants of uranium metal with different crystal structures,the elastic constants,and the anisotropy of lattice expansion of alpha-uranium obtained based on this potential function are highly consistent with first-principles calculation or experimental data.In addition,the calculated formation energy of vacancies in alpha-and beta-uranium also matches the first-principles calculation.The calculated site of the most stable self-interstitial and its formation energy is in good agreement with the findings from density functional theory(DFT)calculations.These results show that our potential function can be used for further large-scale molecular dynamics simulation studies of uranium metal at low pressures,and provides the basis for further construction of potential model suitable for a wide range of pressures. 展开更多
关键词 machine learning potential uranium metal first-principles calculation
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Thermal transport properties of two-dimensional boron dichalcogenides from a first-principles and machine learning approach
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作者 邱占均 胡晏箫 +4 位作者 李顶 胡涛 肖红 冯春宝 李登峰 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第5期7-13,共7页
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 anhar... 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);V I=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·m^(-1)·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)VIV I′have lower thermal conductivity than H-B_(2)VI_(2).The room-temperature thermal conductivity of B2STe 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. 展开更多
关键词 boron dichalcogenides thermal conductivity machine learning interatomic potentials first-principles calculation
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The mechanism of external pressure suppressing dendrites growth in Li metal batteries 被引量:1
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作者 Genming Lai Yunxing Zuo +8 位作者 Junyu Jiao Chi Fang Qinghua Liu Fan Zhang Yao Jiang Liyuan Sheng Bo Xu Chuying Ouyang Jiaxin Zheng 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2023年第4期489-494,共6页
Li metal is considered an ideal anode material for application in the next-generation secondary batteries.However,the commercial application of Li metal batteries has not yet been achieved due to the safety concern ca... Li metal is considered an ideal anode material for application in the next-generation secondary batteries.However,the commercial application of Li metal batteries has not yet been achieved due to the safety concern caused by Li dendrites growth.Despite the fact that many recent experimental studies found that external pressure suppresses the Li dendrites growth,the mechanism of the external pressure effect on Li dendrites remains poorly understood on the atomic scale.Herein,the large-scale molecular dynamics simulations of Li dendrites growth under different external pressure were performed with a machine learning potential,which has the quantum-mechanical accuracy.The simulation results reveal that the external pressure promotes the process of Li self-healing.With the increase of external pressure,the hole defects and Li dendrites would gradually fuse and disappear.This work provides a new perspective for understanding the mechanism for the impact of external pressure on Li dendrites. 展开更多
关键词 Li metal Machine learning potential Molecular dynamic simulation DENDRITE External pressure
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Stress effect on lattice thermal conductivity of anode material NiNB_(2)O_(6)for lithium-ion batteries
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作者 陈奥 童话 +4 位作者 吴成伟 谢国锋 谢忠祥 向长青 周五星 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第5期43-48,共6页
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... 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·m^(-1)·K^(-1),0.727 W·m^(-1)·K^(-1),and 0.465 W·m^(-1)·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. 展开更多
关键词 nickel niobate lattice thermal conductivity uniaxial strain machine learning potential
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Atomic-scale simulations in multi-component alloys and compounds:A review on advances in interatomic potential 被引量:1
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作者 Feiyang Wang Hong-Hui Wu +8 位作者 Linshuo Dong Guangfei Pan Xiaoye Zhou Shuize Wang Ruiqiang Guo Guilin Wu Junheng Gao Fu-Zhi Dai Xinping Mao 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2023年第34期49-65,共17页
Multi-component alloys have demonstrated excellent performance in various applications,but the vast range of possible compositions and microstructures makes it challenging to identify optimized alloys for specific pur... Multi-component alloys have demonstrated excellent performance in various applications,but the vast range of possible compositions and microstructures makes it challenging to identify optimized alloys for specific purposes.To overcome this challenge,large-scale atomic simulation techniques have been widely used for the design and optimization of multi-component alloys.The capability and reliability of large-scale atomic simulations essentially rely on the quality of interatomic potentials that describe the interactions between atoms.This work provides a comprehensive summary of the latest advances in atomic simulation techniques for multi-component alloys.The focus is on interatomic potentials,including both conventional empirical potentials and newly developed machine learning potentials(MLPs).The fitting processes for different types of interatomic potentials applied to multi-component alloys are also discussed.Finally,the challenges and future perspectives in developing MLPs are thoroughly addressed.Overall,this review provides a valuable resource for researchers interested in developing optimized multicomponent alloys using atomic simulation techniques. 展开更多
关键词 Multi-component alloys Atomic simulation Empirical potentials Machine learning potentials
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Understanding the solvation structures of glyme-based electrolytes by machine learning molecular dynamics 被引量:1
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作者 Feng Wang Jun Cheng 《Chinese Journal of Structural Chemistry》 SCIE CAS CSCD 2023年第9期11-17,共7页
Glyme-based electrolytes are of great interest for rechargeable lithium metal batteries due to their high stability,low vapor pressure,and non-flammability.Understanding the solvation structures of these electrolytes ... Glyme-based electrolytes are of great interest for rechargeable lithium metal batteries due to their high stability,low vapor pressure,and non-flammability.Understanding the solvation structures of these electrolytes at the atomic level will facilitate the design of new electrolytes with novel properties.Recently,classical molecular dynamics(CMD)and ab initio molecular dynamics(AIMD)have been applied to investigate electrolytes with complex solvation structures.On one hand,CMD may not provide reliable results as it requires complex parameterization to ensure the accuracy of the classical force field.On the other hand,the time scale of AIMD is limited by the high cost of ab initio calculations,which causes that solvation structures from AIMD simulations depend on the initial configurations.In order to solve the dilemma,machine learning method is applied to accelerate AIMD,and its time scale can be extended dramatically.In this work,we present a computational study on the solvation structures of triglyme(G3)based electrolytes by using machine learning molecular dynamics(MLMD).Firstly,we investigate the effects of density functionals on the accuracy of machine learning potential(MLP),and find that PBE-D3 shows better accuracy compared to BLYP-D3.Then,the densities of electrolytes with different concentration of LiTFSI are computed with MLMD,which shows good agreement with experiments.By analyzing the solvation structures of 1 ns MLMD trajectories,we found that Li+prefers to coordinate with a G3 and a TFSI−in equimolar electrolytes.Our work demonstrates the significance of long-time scale MLMD simulations for clarifying the chemistry of non-ideal electrolytes. 展开更多
关键词 Glyme-based electrolytes Machine learning potential Molecular dynamics Solvation structure
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Teaching French Through Dynamic Assessment:The Case of The First Year Undergraduate Students FLE
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作者 Sahraoui Lafrid 《Psychology Research》 2020年第11期438-452,共15页
It is well established that at the university,one forms the critical spirit,the spirit of analysis and the spirit of synthesis.What we advocate is a spirit of evaluation.The process we followed is part of a problemati... It is well established that at the university,one forms the critical spirit,the spirit of analysis and the spirit of synthesis.What we advocate is a spirit of evaluation.The process we followed is part of a problematic of teaching French and especially in didactics of writing.We have implemented an experimental device in our teaching practice.This is the dynamic evaluation.This evaluation allows the measurement of the initial level of achievement of a written production.And also the introduction of elements likely to help the subject to modify his usual strategies involved in the realization of a failed written production.But above all the appreciation of the way new strategies are involved.It is a four-phase experience that lasted a whole year.We first put our sample audience to a pre-test;with them,we determined the teaching objectives;then we set up the training workshops for the dynamic assessment,and finally we closed the process with a final test of measurement and evaluation.Two questionnaires were used and an observation grid. 展开更多
关键词 dynamic assessment learning potential SKILLS transferable macrocompetence strategies MEDIATION
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Fragility crossover mediated by covalent-like electronic interactions in metallic liquids
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作者 Hui-Ru Zhang Liang Gao +7 位作者 Yu-Hao Ye Jia-Xin Zhang Tao Zhang Qing-Zhou Bu Qun Yang Zeng-Wei Zhu Shuai Wei Hai-Bin Yu 《Materials Futures》 2024年第2期117-130,共14页
Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervent... Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervention of crystallization often prevents the assessment of fragility in poor glass-formers,such as supercooled metallic liquids.Hence experimental data on their compositional dependence are scarce,let alone fundamentally understood.In this work,we use fast scanning calorimetry to overcome this obstacle and systematically study the fragility in a ternary La–Ni–Al system,over previously inaccessible composition space.We observe fragility dropped in a small range with the Al alloying,indicating an alloying-induced fragility crossover.We use x-ray photoelectron spectroscopy,resistance measurements,electronic structure calculations,and DFT-based deep-learning atomic simulations to investigate the cause of this fragility drop.These results show that the fragility crossover can be fundamentally ascribed to the electronic covalency associated with the unique Al–Al interactions.Our findings provide insight into the origin of fragility in metallic liquids from an electronic structure perspective and pave a new way for the design of metallic glasses. 展开更多
关键词 metallic glass FRAGILITY fast scanning calorimetry density functional theory deep learning potential
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Deep potentials for materials science 被引量:6
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作者 Tongqi Wen Linfeng Zhang +2 位作者 Han Wang Weinan E David J Srolovitz 《Materials Futures》 2022年第2期89-115,共27页
To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and be... To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials. 展开更多
关键词 deep potential atomistic simulation machine learning potential neural network
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Grain boundary segregation induced strong UHTCs at elevated temperatures:A universal mechanism from conventional UHTCs to high entropy UHTCs
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作者 Fu-Zhi Dai Bo Wen +3 位作者 Yinjie Sun Yixiao Ren Huimin Xiang Yanchun Zhou 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2022年第28期26-33,共8页
Ultra-high temperature ceramics(UHTCs)exhibit a unique combination of excellent properties,including ultra-high melting point,excellent chemical stability,and good oxidation resistance,which make them promising candid... Ultra-high temperature ceramics(UHTCs)exhibit a unique combination of excellent properties,including ultra-high melting point,excellent chemical stability,and good oxidation resistance,which make them promising candidates for aerospace and nuclear applications.However,the degradation of hightemperature strength is one of the main limitations for their ultra-high temperature applications.Thus,searching for mechanisms that can help to develop high-performance UHTCs with good high-temperature mechanical properties is urgently needed.To achieve this goal,grain boundary segregation of a series of carbides,including conventional,medium entropy,and high entropy transition metal carbides,i.e.,Zr_(0.95)W_(0.05)C,TiZrHfC_(3),ZrHfNbTaC_(4),TiZrHfNbTaC_(5),were studied by atomistic simulations with a fitted Deep Potential(DP),and the effects of segregation on grain boundary strength were emphasized.For all the studied carbides,grain boundary segregations are realized,which are dominated by the atomic size effect.In addition,tensile simulations indicate that grain boundaries(GBs)will usually be strengthened due to segregation.Our simulation results reveal that grain boundary segregation may be a universal mechanism in enhancing the high-temperature strength of both conventional UHTCs and medium/high entropy UHTCs,since GBs play a key role in controlling the fracture of UHTCs at elevated temperatures. 展开更多
关键词 UHTCs High entropy ceramics Grain boundary segregation High-temperature strength Machine learning potential
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