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
NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large lan...NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.展开更多
Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreov...Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreover,surface supported atoms are also widely explored for high active and selecting catalysts.Severe deformation,even dipping into the surface,of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces.However,most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions.Here,we develop an accurate graph neural network machine learning potential(MLP)from ab initio data by active learning architecture through fine-tuning pre-trained models,and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters(1-8 atoms)on perfect and various defective MoS2 monolayers.Interestingly,Mo clusters can always sink and embed themselves into MoS2 layers.In contrast,S clusters float on perfect surfaces.On the defective surface,a few S atoms will fill the vacancy and rest S clusters float on the top.Such significant structural reconstructions should be carefully taken into account.展开更多
LiFePO_(4) is a cathode material with good thermal stability,but low thermal conductivity is a critical problem.In this study,we employ a machine learning potential approach based on first-principles methods combined ...LiFePO_(4) is a cathode material with good thermal stability,but low thermal conductivity is a critical problem.In this study,we employ a machine learning potential approach based on first-principles methods combined with the Boltzmann transport theory to investigate the influence of Na substitution on the thermal conductivity of LiFePO_(4) and the impact of Li-ion de-embedding on the thermal conductivity of Li_(3/4)Na_(1/4)FePO_(4),with the aim of enhancing heat dissipation in Li-ion batteries.The results show a significant increase in thermal conductivity due to an increase in phonon group velocity and a decrease in phonon anharmonic scattering by Na substitution.In addition,the thermal conductivity increases significantly with decreasing Li-ion concentration due to the increase in phonon lifetime.Our work guides the improvement of the thermal conductivity of Li FePO_4,emphasizing the crucial roles of both substitution and Li-ion detachment/intercalation for the thermal management of electrochemical energy storage devices.展开更多
This Perspective explores the integration of machine learning potentials(MLPs)in the research of heterogeneous catalysis,focusing on their role in identifying in situ active sites and enhancing the understanding of ca...This Perspective explores the integration of machine learning potentials(MLPs)in the research of heterogeneous catalysis,focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes.MLPs utilize extensive databases from high-throughput density functional theory(DFT)calculations to train models that predict atomic configurations,energies,and forces with near-DFT accuracy.These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods.Coupled with global optimization algorithms,MLPs enable systematic investigations across vast structural spaces,making substantial contributions to the modeling of catalyst surface structures under reactive conditions.The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis.The prevailing challenges faced by this approach are also discussed.展开更多
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
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
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
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
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
Boron arsenide(BAs)is a promising integrated circuit thermal management material with ultra-high thermal conductivity(κ)and exceptional semiconductor properties.In practical devices,mechanical stress and strain cause...Boron arsenide(BAs)is a promising integrated circuit thermal management material with ultra-high thermal conductivity(κ)and exceptional semiconductor properties.In practical devices,mechanical stress and strain caused by thermal expansion inevitably affect the thermodynamic properties of heat sink materials.Consequently,investigation of the effects of stress and strain on theκof BAs is essential.Accurate assessment and regulation of theκand phonon heat transport of BAs requires consideration of both three-phonon(3ph)and four-phonon(4ph)interactions.However,anharmonicity computation using density functional theory is computationally expensive,particularly for the fourth-order interatomic force constants.This work uses a machine learning-driven moment tensor potential method to evaluate higher-order anharmonicity and extends it to include fourth-order forces,accelerating the calculations significantly.Results show that theκof BAs calculated using the moment tensor potential method when considering both 3ph and 4ph processes agrees well with experimental data.In contrast,considering the 3ph interaction alone causesκto be overestimated.The machine learning approach reduces computational costs by approximately 94%while maintaining comparable accuracy to density functional theory.By solving the phonon Boltzmann transport equation,the thermal transport properties of BAs under biaxial tensile strains ranging up to 7%are evaluated.BAs shows an anomalousκenhancement when including both 3ph and 4ph interactions,with a maximum enhancement of 15%at a small strain of 1%,primarily because of the weakening of the 3ph scattering.Beyond 2%strain,both the 3ph and 4ph scattering rates increase significantly,causing shorter phonon lifetimes and a monotonic reduction inκ.Additionally,theκtemperature dependence under strain is explored,highlighting temperature’s role in modulating phonon scattering.This study provides machine learning-driven insights into the BAs thermal response under biaxial tensile strain and offers a new perspective for understanding similar anomalous strain-induced phenomena in other materials.展开更多
As a representative of wide-bandgap semiconductors,wurtzite gallium nitride(GaN)has been widely utilized in highpower devices due to its high breakdown voltage and low specific on-resistance.Accurate prediction of wur...As a representative of wide-bandgap semiconductors,wurtzite gallium nitride(GaN)has been widely utilized in highpower devices due to its high breakdown voltage and low specific on-resistance.Accurate prediction of wurtzite GaN’s thermal conductivity is a prerequisite for designing effective thermal management systems for electronic applications.Machine learning-driven molecular dynamics simulation offers a promising approach to predicting the thermal conductivity of large-scale systems without requiring predefined parameters.However,these methods often underestimate the thermal conductivity of materials with inherently high thermal conductivity due to the large predicted force error compared with first-principles calculations,posing a critical challenge for their broader application.In this study,we successfully developed a neuroevolution potential for wurtzite GaN and accurately predicted its thermal conductivity,259±6 W/(m·K)at room temperature,achieving excellent agreement with reported experimental measurements.The hyperparameters of the neuroevolution potential(NEP)were optimized based on a systematic analysis of reproduced energy and force,structural features,and computational efficiency.Furthermore,a force error correction method was implemented,effectively reducing the error caused by the additional force noise in the Langevin thermostat by extrapolating to the zero-force error limit.This study provides valuable insights and holds significant implications for advancing efficient thermal management technologies in wide-bandgap semiconductor devices.展开更多
The formation of interphase layers,including the cathode-electrolyte interphase(CEI)and solidelectrolyte interphase(SEI),exhibits significant chemical complexity and plays a pivotal role in determining the performance...The formation of interphase layers,including the cathode-electrolyte interphase(CEI)and solidelectrolyte interphase(SEI),exhibits significant chemical complexity and plays a pivotal role in determining the performance of lithium batteries.Despite considerable advances in simulating the bulk phase properties of battery materials,the understanding of interfaces,including crystalline interfaces that represent the simplest case,remains limited.This is primarily due to challenges in performing ground-state searches for interface microstructures and the high computational costs associated with first-principles methods.Herein,we introduce InterOptimus,an automated workflow designed to efficiently search for ground-state heterogeneous interfaces.InterOptimus incorporates a rigorous,symmetry-aware equivalence analysis for lattice matching and termination scanning.Additionally,it introduces stereographic projection as an intuitive and comprehensive framework for visualizing and classifying interface structures.By integrating universal machine learning interatomic potentials(MLIPs),InterOptimus enables rapid predictions of interface energy and stability,significantly reducing the necessary computational cost in density functional theory(DFT)by over 90%.We benchmarked several MLIPs at three critical lithium battery interfaces,Li_(2)S|Ni_(3)S_(2),LiF|NCM,and Li_(3)PS_(4)|Li,and demonstrated that the MLIPs achieve accuracy comparable to DFT in modeling potential energy surfaces and ranking interface stabilities.Thus,InterOptimus facilitates the efficient determination of ground-state heterogeneous interface structures and subsequent studies of structure-property relationships,accelerating the interface engineering of novel battery materials.展开更多
The liquid metal embrittlement(LME)of advanced high-strength steels caused by zinc(Zn)has become a critical issue hindering their widespread application in the automotive industry.In this study,atomic-scale simulation...The liquid metal embrittlement(LME)of advanced high-strength steels caused by zinc(Zn)has become a critical issue hindering their widespread application in the automotive industry.In this study,atomic-scale simulations are carried out to elucidate the underlying cause of this phenomenon,namely grain boundary embrittlement due to Zn segregation at iron(Fe)grain boundaries.A machine learning moment tensor interatomic potential for the Fe-Zn binary system is developed,based on which the thermodynamics of grain boundary segregation is evaluated.The yielded segregation energy spectrum of Zn in BCC Fe reveals the quantitative relationship between the average segregation concentration of Zn at Fe grain boundaries and the macroscopic Zn content,temperature,and fraction of grain boundary atoms.It suggests a strong thermodynamic driving force for Zn segregation at the Fe grain boundaries,which correlates directly with the grain boundary energy:high-energy grain boundaries can accommodate a large amount of Zn atoms,while low-energy grain boundaries exhibit a certain degree of repulsion to Zn.Kinetically,Zn enters the grain boundaries more easily through diffusion than by penetration.Nonetheless,the grain boundary embrittlement caused by Zn penetration is more severe than that by Zn diffusion.The embrittlement effect generally increases linearly with the increase in Zn concentration at the grain boundary.Altogether,it suggests that the LME in steels induced by grain boundary segregation of Zn emerges as a combined consequence of Zn melt penetration and solid-state diffusion of Zn atoms.展开更多
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
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
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
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
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
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
基金Project supported by the A*STAR Computational Resource Centre through the use of its high-performance computing facilitiesfinancial support from the China Scholarship Council (Grant No.202206120136)。
文摘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.
基金supported by the Jiangsu Provincial Science and Technology Project Basic Research Program(Natural Science Foundation of Jiangsu Province)(No.BK20211283).
文摘NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.
基金supported by the National Natural Science Foundation of China(Grant No.12374253,12074053,12004064)J.G.thanks the Foreign talents project(G2022127004L),The authors also acknowledge computer support from the Shanghai Supercomputer Center,the DUT Supercomputing Center,and the Tianhe supercomputer of Tianjin Center.
文摘Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreover,surface supported atoms are also widely explored for high active and selecting catalysts.Severe deformation,even dipping into the surface,of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces.However,most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions.Here,we develop an accurate graph neural network machine learning potential(MLP)from ab initio data by active learning architecture through fine-tuning pre-trained models,and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters(1-8 atoms)on perfect and various defective MoS2 monolayers.Interestingly,Mo clusters can always sink and embed themselves into MoS2 layers.In contrast,S clusters float on perfect surfaces.On the defective surface,a few S atoms will fill the vacancy and rest S clusters float on the top.Such significant structural reconstructions should be carefully taken into account.
基金supported by the National Natural Science Foundation of China(Grant No.12074115)the Science and Technology Innovation Program of Hunan Province(Grant No.2023RC3176)。
文摘LiFePO_(4) is a cathode material with good thermal stability,but low thermal conductivity is a critical problem.In this study,we employ a machine learning potential approach based on first-principles methods combined with the Boltzmann transport theory to investigate the influence of Na substitution on the thermal conductivity of LiFePO_(4) and the impact of Li-ion de-embedding on the thermal conductivity of Li_(3/4)Na_(1/4)FePO_(4),with the aim of enhancing heat dissipation in Li-ion batteries.The results show a significant increase in thermal conductivity due to an increase in phonon group velocity and a decrease in phonon anharmonic scattering by Na substitution.In addition,the thermal conductivity increases significantly with decreasing Li-ion concentration due to the increase in phonon lifetime.Our work guides the improvement of the thermal conductivity of Li FePO_4,emphasizing the crucial roles of both substitution and Li-ion detachment/intercalation for the thermal management of electrochemical energy storage devices.
基金the NKRDPC(2021YFA1500700)and NSFC(92045303).X.C.is grateful for financial support from ShanghaiTech University.
文摘This Perspective explores the integration of machine learning potentials(MLPs)in the research of heterogeneous catalysis,focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes.MLPs utilize extensive databases from high-throughput density functional theory(DFT)calculations to train models that predict atomic configurations,energies,and forces with near-DFT accuracy.These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods.Coupled with global optimization algorithms,MLPs enable systematic investigations across vast structural spaces,making substantial contributions to the modeling of catalyst surface structures under reactive conditions.The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis.The prevailing challenges faced by this approach are also discussed.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.11864040,11964037,and 11664038)。
文摘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.
基金Scientific and Technological Research of Chongqing Municipal Education Commission(Grant No.KJZD-K202100602)the funding of Institute for Advanced Sciences of Chongqing University of Posts and Telecommunications(Grant No.E011A2022326)。
文摘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.
基金Computational support was provided by Supercomputing Center in USTC and National Supercomputing Center in Tianjinthe National Key Research and Development Program of China(Grant Nos.2017YFA0204904 and 2019YFA0210004)。
文摘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.
基金supported financially by the National Natural Science Foundation of China(Nos.51672064 and No.U1435206)。
文摘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.
基金supported by Natural Sciences Foundation of China under Grant No.51972089 and No.51672064。
文摘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.
基金supported by the National Key R&D Program of China(Grant No.2020YFA0709700).
文摘Boron arsenide(BAs)is a promising integrated circuit thermal management material with ultra-high thermal conductivity(κ)and exceptional semiconductor properties.In practical devices,mechanical stress and strain caused by thermal expansion inevitably affect the thermodynamic properties of heat sink materials.Consequently,investigation of the effects of stress and strain on theκof BAs is essential.Accurate assessment and regulation of theκand phonon heat transport of BAs requires consideration of both three-phonon(3ph)and four-phonon(4ph)interactions.However,anharmonicity computation using density functional theory is computationally expensive,particularly for the fourth-order interatomic force constants.This work uses a machine learning-driven moment tensor potential method to evaluate higher-order anharmonicity and extends it to include fourth-order forces,accelerating the calculations significantly.Results show that theκof BAs calculated using the moment tensor potential method when considering both 3ph and 4ph processes agrees well with experimental data.In contrast,considering the 3ph interaction alone causesκto be overestimated.The machine learning approach reduces computational costs by approximately 94%while maintaining comparable accuracy to density functional theory.By solving the phonon Boltzmann transport equation,the thermal transport properties of BAs under biaxial tensile strains ranging up to 7%are evaluated.BAs shows an anomalousκenhancement when including both 3ph and 4ph interactions,with a maximum enhancement of 15%at a small strain of 1%,primarily because of the weakening of the 3ph scattering.Beyond 2%strain,both the 3ph and 4ph scattering rates increase significantly,causing shorter phonon lifetimes and a monotonic reduction inκ.Additionally,theκtemperature dependence under strain is explored,highlighting temperature’s role in modulating phonon scattering.This study provides machine learning-driven insights into the BAs thermal response under biaxial tensile strain and offers a new perspective for understanding similar anomalous strain-induced phenomena in other materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.52376063 and 52306116)the Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology Prevention Fund(Grant No.2022-K03)the China Postdoctoral Science Foundation(Grant No.2023MD744223).
文摘As a representative of wide-bandgap semiconductors,wurtzite gallium nitride(GaN)has been widely utilized in highpower devices due to its high breakdown voltage and low specific on-resistance.Accurate prediction of wurtzite GaN’s thermal conductivity is a prerequisite for designing effective thermal management systems for electronic applications.Machine learning-driven molecular dynamics simulation offers a promising approach to predicting the thermal conductivity of large-scale systems without requiring predefined parameters.However,these methods often underestimate the thermal conductivity of materials with inherently high thermal conductivity due to the large predicted force error compared with first-principles calculations,posing a critical challenge for their broader application.In this study,we successfully developed a neuroevolution potential for wurtzite GaN and accurately predicted its thermal conductivity,259±6 W/(m·K)at room temperature,achieving excellent agreement with reported experimental measurements.The hyperparameters of the neuroevolution potential(NEP)were optimized based on a systematic analysis of reproduced energy and force,structural features,and computational efficiency.Furthermore,a force error correction method was implemented,effectively reducing the error caused by the additional force noise in the Langevin thermostat by extrapolating to the zero-force error limit.This study provides valuable insights and holds significant implications for advancing efficient thermal management technologies in wide-bandgap semiconductor devices.
基金supported by the National Natural Science Foundation of China(92470110)the Special Funds for the Development of Strategic Emerging Industries in Shenzhen(XMHT20240108008)the Shenzhen Stable Support Program for Higher Education Institutions(WDZC20231126215806001)。
文摘The formation of interphase layers,including the cathode-electrolyte interphase(CEI)and solidelectrolyte interphase(SEI),exhibits significant chemical complexity and plays a pivotal role in determining the performance of lithium batteries.Despite considerable advances in simulating the bulk phase properties of battery materials,the understanding of interfaces,including crystalline interfaces that represent the simplest case,remains limited.This is primarily due to challenges in performing ground-state searches for interface microstructures and the high computational costs associated with first-principles methods.Herein,we introduce InterOptimus,an automated workflow designed to efficiently search for ground-state heterogeneous interfaces.InterOptimus incorporates a rigorous,symmetry-aware equivalence analysis for lattice matching and termination scanning.Additionally,it introduces stereographic projection as an intuitive and comprehensive framework for visualizing and classifying interface structures.By integrating universal machine learning interatomic potentials(MLIPs),InterOptimus enables rapid predictions of interface energy and stability,significantly reducing the necessary computational cost in density functional theory(DFT)by over 90%.We benchmarked several MLIPs at three critical lithium battery interfaces,Li_(2)S|Ni_(3)S_(2),LiF|NCM,and Li_(3)PS_(4)|Li,and demonstrated that the MLIPs achieve accuracy comparable to DFT in modeling potential energy surfaces and ranking interface stabilities.Thus,InterOptimus facilitates the efficient determination of ground-state heterogeneous interface structures and subsequent studies of structure-property relationships,accelerating the interface engineering of novel battery materials.
基金financially supported by the National Natural Science Foundation of China(No.52071204)Natural Science Foundation of Shanghai Municipal(No.22ZR1428700)SJTU Kunpeng&Ascend Center of Excellence,and MaGIC of Shanghai Jiao Tong University.
文摘The liquid metal embrittlement(LME)of advanced high-strength steels caused by zinc(Zn)has become a critical issue hindering their widespread application in the automotive industry.In this study,atomic-scale simulations are carried out to elucidate the underlying cause of this phenomenon,namely grain boundary embrittlement due to Zn segregation at iron(Fe)grain boundaries.A machine learning moment tensor interatomic potential for the Fe-Zn binary system is developed,based on which the thermodynamics of grain boundary segregation is evaluated.The yielded segregation energy spectrum of Zn in BCC Fe reveals the quantitative relationship between the average segregation concentration of Zn at Fe grain boundaries and the macroscopic Zn content,temperature,and fraction of grain boundary atoms.It suggests a strong thermodynamic driving force for Zn segregation at the Fe grain boundaries,which correlates directly with the grain boundary energy:high-energy grain boundaries can accommodate a large amount of Zn atoms,while low-energy grain boundaries exhibit a certain degree of repulsion to Zn.Kinetically,Zn enters the grain boundaries more easily through diffusion than by penetration.Nonetheless,the grain boundary embrittlement caused by Zn penetration is more severe than that by Zn diffusion.The embrittlement effect generally increases linearly with the increase in Zn concentration at the grain boundary.Altogether,it suggests that the LME in steels induced by grain boundary segregation of Zn emerges as a combined consequence of Zn melt penetration and solid-state diffusion of Zn atoms.
基金the National Key Research and Development Program of China(No.2022YFB3709000)the National Natural Science Foundation of China(Nos.52122408,52071023,52101019,and 51901013)the Fundamental Research Funds for the Central Universities(University of Science and Technology Beijing,Nos.06500135 and FRF-TP-2021-04C1).
文摘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.
基金National Natural Science Foundation of China(Nos.21991151,21991150,21861132015,22225302 and 22021001)the Fundamental Research Funds for the Central Universities(No.20720220009)Xiamen Science and Technology Plan Project(No.3502Z20203027)for financial support.
文摘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.
基金supported by the National Natural Science Foundation of China(No.52272180,No.12174162,No.51962010)the Shenzhen Science and Technology Research Grant(No.20220810123501001)the IER Foundation 2021(IERF202104)。
文摘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.
基金supported by the National Natural Science Foundation of China(No.51672064)。
文摘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.
基金Beijing Computational Science Research Center(CSRC)the National Natural Science Foundation of China(Grant Nos.52161160330 and U2230402)。
文摘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.
基金supported by the National Science Fund for Distinguished Young Scholars(22225302)the National Natural Science Foundation of China(92161113,21991151,21991150 and 22021001)+2 种基金the Fundamental Research Funds for the Central Universities(20720220008,20720220009 and 20720220010)the Laboratory of AI for Electrochemistry(AI4EC)IKKEM(RD2023100101 and RD2022070501)
文摘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.
