All-solid-state batteries(ASSBs)represent a next-generation energy storage technology,offering enhanced safety,higher energy density,and improved cycling stability compared to conventional liquid-electrolyte-based lit...All-solid-state batteries(ASSBs)represent a next-generation energy storage technology,offering enhanced safety,higher energy density,and improved cycling stability compared to conventional liquid-electrolyte-based lithium-ion batteries.Understanding and optimizing the complex chemistries and interfaces that underpin ASSB performance present significant challenges from both experimental and modeling perspectives.In particular,atomistic simulations face difficulties in capturing the complex structure,disorder,and dynamic evolution of materials and interfaces under practically relevant conditions.While established methods such as density functional theory and classical force fields have provided valuable insights,some questions remain difficult to address,particularly those involving large system sizes or long timescales.Recently,machine learning interatomic potentials(MLIPs)have emerged as a transformative tool,enabling atomistic simulations at length and time scales that were previously challenging to access with conventional approaches.By delivering near first-principles accuracy with much greater efficiency,MLIPs open new avenues for large-scale,long-timescale,and high-throughput simulations of solid-state battery materials.In this review,we present a comparative overview of density functional theory,classical force fields,and MLIPs,highlighting their respective strengths and limitations in ASSB research.We then discuss how MLIPs enable simulations that reach longer timescales,larger system sizes,and support high-throughput calculations,providing unique insights into ion transport and interfacial evolution in ASSBs.Finally,we conclude with a summary and outlook on current challenges and future opportunities for expanding MLIP capabilities and accelerating their impact in solid-state battery research.展开更多
Molecular dynamics(MD)is a powerful method widely used in materials science and solid-state physics.The accuracy of MD simulations depends on the quality of the interatomic potentials.In this work,a special class of e...Molecular dynamics(MD)is a powerful method widely used in materials science and solid-state physics.The accuracy of MD simulations depends on the quality of the interatomic potentials.In this work,a special class of exact solutions to the equations of motion of atoms in a body-centered cubic(bcc)lattice is analyzed.These solutions take the form of delocalized nonlinear vibrational modes(DNVMs)and can serve as an excellent test of the accuracy of the interatomic potentials used in MD modeling for bcc crystals.The accuracy of the potentials can be checked by comparing the frequency response of DNVMs calculated using this or that interatomic potential with that calculated using the more accurate ab initio approach.DNVMs can also be used to train new,more accurate machine learning potentials for bcc metals.To address the above issues,it is important to analyze the properties of DNVMs,which is the main goal of this work.Considering only the point symmetry groups of the bcc lattice,34 DNVMs are found.Since interatomic potentials are not used in finding DNVMs,they are exact solutions for any type of potential.Here,the simplest interatomic potentials with cubic anharmonicity are used to simplify the analysis and to obtain some analytical results.For example,the dispersion relations for small-amplitude phonon modes are derived,taking into account interactions between up to the fourth nearest neighbor.The frequency response of the DNVMs is calculated numerically,and for some DNVMs examples of analytical analysis are given.The energy stored by the interatomic bonds of different lengths is calculated,which is important for testing interatomic potentials.The pros and cons of using DNVMs to test and improve interatomic potentials for metals are discussed.Since DNVMs are the natural vibrational modes of bcc crystals,any reliable interatomic potential must reproduce their properties with reasonable accuracy.展开更多
To explore atomic-level phenomena in the Cu-Ni-Sn alloy,a second nearest-neighbor modified embedded-atom method(2NN MEAM)potential has been developed for the Cu-Ni-Sn system,building upon the work of other researchers...To explore atomic-level phenomena in the Cu-Ni-Sn alloy,a second nearest-neighbor modified embedded-atom method(2NN MEAM)potential has been developed for the Cu-Ni-Sn system,building upon the work of other researchers.This potential demonstrates remarkable accuracy in predicting the lattice constant,with a relative error of less than 0.5%when compared to density functional theory(DFT)results,and it achieves a 10%relative error in the enthalpy of formation compared to experimental data,marking substantial advancements over prior models.The bulk modulus is predicted with a relative error of 8%compared to DFT.Notably,the potential effectively simulates the processes of melting and solidification of Cu-15Ni-8Sn,with a simulated melting point that closely aligns with the experimental value,within a 7.5%margin.This serves as a foundation for establishing a 2NN MEAM potential for a flawless Cu-Ni-Sn system and its microalloying systems.展开更多
Al,Ca,and Zn are representative commercial alloying elements for Mg alloys.To investigate the effects of these elements on the deformation and recrystallization behaviors of Mg alloys,we develop interatomic potentials...Al,Ca,and Zn are representative commercial alloying elements for Mg alloys.To investigate the effects of these elements on the deformation and recrystallization behaviors of Mg alloys,we develop interatomic potentials for the Al-Ca,Al-Zn,Mg-Al-Ca and Mg-Al-Zn systems based on the second nearest-neighbor modified embedded-atom method formalism.The developed potentials describe structural,elastic,and thermodynamic properties of compounds and solutions of associated alloy systems in reasonable agreement with experimental data and higher-level calculations.The applicability of these potentials to the present investigation is confirmed by calculating the generalized stacking fault energy for various slip systems and the segregation energy on twin boundaries of the Mg-Al-Ca and Mg-Al-Zn alloys,accompanied with the thermal expansion coefficient and crystal structure maintenance of stable compounds in those alloys.展开更多
One of the major tasks in a molecular dynamics （MD） simulation is the selection of adequate potential functions, from which forces are derived. If the potentials do not model the behaviour of the atoms correctly, th...One of the major tasks in a molecular dynamics （MD） simulation is the selection of adequate potential functions, from which forces are derived. If the potentials do not model the behaviour of the atoms correctly, the results produced from the simulation would be useless. Three popular potentials, namely, Lennard-Jones （L J）, Morse, and embedded-atom method （EAM） potentials, were employed to model copper workpiece and diamond tool in nanometric machining. From the simulation results and further analysis, the EAM potential was found to be the most suitable of the three potentials. This is because it best describes the metallic bonding of the copper atoms; it demonstrated the lowest cutting force variation, and the potential energy is most stable for the EAM.展开更多
Abstract The process of γ＇ phase precipitating from Ni75Al14MO11 is studied by a computational simulation technique based on microscopic phase-field kinetics model. We studied the phase transformation with the purpo...Abstract The process of γ＇ phase precipitating from Ni75Al14MO11 is studied by a computational simulation technique based on microscopic phase-field kinetics model. We studied the phase transformation with the purpose of clarifying the influence of the nearest interatomic potential V Ni-Al （the nearest interatomic potential） on the precipitation process of γ＇ phase. The result demonstrates that there are two kinds of ordered phases, respective Llo and L12 in the early stage, and Llo phase transforms into L12 phase subsequently. For L12 phase, Ni atoms mainly occupy α site （face center positions）, while Al and Mo atoms occupy fl sites （the vertex positions）. When VNi-Al is increased by 10 MeV, the occupation probability of Ni atoms on α sites and Al atoms on β sites are enhanced. Enhanced VNi-Al facilitates clustering and ordering of Al atom, which promotes the formation of the γ＇ phase. At last, the simulation result was discussed by employing the thermodynamic stability.展开更多
Molecular Dynamics(MD)simulation for computing Interatomic Potential(IAP)is a very important High-Performance Computing(HPC)application.MD simulation on particles of experimental relevance takes huge computation time,...Molecular Dynamics(MD)simulation for computing Interatomic Potential(IAP)is a very important High-Performance Computing(HPC)application.MD simulation on particles of experimental relevance takes huge computation time,despite using an expensive high-end server.Heterogeneous computing,a combination of the Field Programmable Gate Array(FPGA)and a computer,is proposed as a solution to compute MD simulation efficiently.In such heterogeneous computation,communication between FPGA and Computer is necessary.One such MD simulation,explained in the paper,is the(Artificial Neural Network)ANN-based IAP computation of gold(Au_(147)&Au_(309))nanoparticles.MD simulation calculates the forces between atoms and the total energy of the chemical system.This work proposes the novel design and implementation of an ANN IAP-based MD simulation for Au_(147)&Au_(309) using communication protocols,such as Universal Asynchronous Receiver-Transmitter(UART)and Ethernet,for communication between the FPGA and the host computer.To improve the latency of MD simulation through heterogeneous computing,Universal Asynchronous Receiver-Transmitter(UART)and Ethernet communication protocols were explored to conduct MD simulation of 50,000 cycles.In this study,computation times of 17.54 and 18.70 h were achieved with UART and Ethernet,respectively,compared to the conventional server time of 29 h for Au_(147) nanoparticles.The results pave the way for the development of a Lab-on-a-chip application.展开更多
The lattice-inversion embedded-atom-method interatomic potential developed previously by us is extended to alkaline metals including Li,Na,and K.It is found that considering interatomic interactions between neighborin...The lattice-inversion embedded-atom-method interatomic potential developed previously by us is extended to alkaline metals including Li,Na,and K.It is found that considering interatomic interactions between neighboring atoms of an appropriate distance is a matter of great significance in constructing accurate embedded-atom-method interatomic potentials,especially for the prediction of surface energy.The lattice-inversion embedded-atom-method interatomic potentials for Li,Na,and K are successfully constructed by taking the fourth-neighbor atoms into consideration.These angular-independent potentials markedly promote the accuracy of predicted surface energies,which agree well with experimental results.In addition,the predicted structural stability,elastic constants,formation and migration energies of vacancy,and activation energy of vacancy diffusion are in good agreement with available experimental data and first-principles calculations,and the equilibrium condition is satisfied.展开更多
We applied an approach to the development of many-body interatomic potentials for NiZr alloys,gaining an improved accuracy and reliability.The functional form of the potential is that of the embedded method,but it has...We applied an approach to the development of many-body interatomic potentials for NiZr alloys,gaining an improved accuracy and reliability.The functional form of the potential is that of the embedded method,but it has been improved as follows. (1) The database used for the development of the potential includes both experimental data and a large set of energies of different structures of the alloys generated by Fab initio calculations. (2) The optimum parametrization of the potential for the given database is obtained by fitting. Using this approach we developed reliable interatomic potentials for Ni and Zr. The potential accurately reproduces basic equilibrium properties of the alloys.展开更多
Motivated by the special theory of gradient elasticity (GradEla), a proposal is advanced for extending it to construct gradient models for interatomic potentials, commonly used in atomistic simulations. Our focus is o...Motivated by the special theory of gradient elasticity (GradEla), a proposal is advanced for extending it to construct gradient models for interatomic potentials, commonly used in atomistic simulations. Our focus is on London’s quantum mechanical potential which is an analytical expression valid until a certain characteristic distance where “attractive” molecular interactions change character and become “repulsive” and cannot be described by the classical form of London’s potential. It turns out that the suggested internal length gradient (ILG) generalization of London’s potential generates both an “attractive” and a “repulsive” branch, and by adjusting the corresponding gradient parameters, the behavior of the empirical Lennard-Jones potentials is theoretically captured.展开更多
We present MP-ALOE,a dataset of nearly 1 million DFT calculations using the accurate r^(2)SCAN metageneralized gradient approximation.Covering 89 elements,MP-ALOE was created using active learning and primarily consis...We present MP-ALOE,a dataset of nearly 1 million DFT calculations using the accurate r^(2)SCAN metageneralized gradient approximation.Covering 89 elements,MP-ALOE was created using active learning and primarily consists of off-equilibrium structures.We benchmark a machine learning interatomic potential trained on MP-ALOE,and evaluate its performance on a series of benchmarks,including predicting the thermochemical properties of equilibrium structures;predicting forces of farfrom-equilibrium structures;maintaining physical soundness under static extreme deformations;and molecular dynamic stability under extreme temperatures and pressures.MP-ALOE shows strong performance on all of these benchmarks and is made public for the broader community to utilize.展开更多
Machine learning interatomic potentials(MLIPs)have introduced a new paradigm for atomic simulations.Recent advancements have led to universal MLIPs(uMLIPs)that are pre-trained on diverse datasets,providing opportuniti...Machine learning interatomic potentials(MLIPs)have introduced a new paradigm for atomic simulations.Recent advancements have led to universal MLIPs(uMLIPs)that are pre-trained on diverse datasets,providing opportunities for universal force fields and foundational machine learning models.However,their performance in extrapolating to out-of-distribution complex atomic environments remains unclear.In this study,we highlight a consistent potential energy surface(PES)softening effect in three uMLIPs:M3GNet,CHGNet,and MACE-MP-0,which is characterized by energy and force underprediction in atomic-modeling benchmarks including surfaces,defects,solid-solution energetics,ion migration barriers,phonon vibration modes,and general high-energy states.The PES softening behavior originates primarily from the systematically underpredicted PES curvature,which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets.Our findings suggest that a considerable fraction of uMLIP errors are highly systematic,and can therefore be efficiently corrected.We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs.展开更多
Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with a...Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with ab initio accuracy.By replacing the costly density functional theory(DFT)computation of phonon modes with much faster MLIP phonon mode calculations,our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss.We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states.The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface.Across all the systems,we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes.This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity,opening pathways to high-throughput screening of defect-engineered materials.Ourwork not only demonstrates accelerated calculation of PL spectra with DFT accuracy,but also makes such calculations tractable for more complex materials.展开更多
Emerging machine learning interatomic potentials(MLIPs)offer a promising solution for large-scale accurate material simulations,but stringent tests related to the description of vibrational dynamics in molecular cryst...Emerging machine learning interatomic potentials(MLIPs)offer a promising solution for large-scale accurate material simulations,but stringent tests related to the description of vibrational dynamics in molecular crystals remain scarce.Here,we develop a general MLIP by leveraging the graph neural network-based MACE architecture and active-learning strategies to accurately capture vibrational dynamics across a range of polyacene-based molecular crystals,namely naphthalene,anthracene,tetracene and pentacene.Through careful error propagation,we show that these potentials are accurate and enable the study of anharmonic vibrational features,vibrational lifetimes,and vibrational coupling.In particular,we investigate large-scale host-vip systems based on these molecular crystals,showing the capacity of molecular-dynamics-based techniques to explain and quantify vibrational coupling between host and vip nuclear motion.Our results establish a framework for understanding vibrational signatures in large-scale complex molecular systems and thus represent an important step for engineering vibrational interactions in molecular environments.展开更多
Universal machine learning interatomic potentials(uMLIPs)have emerged as powerful tools for accelerating atomistic simulations,offering scalable and efficient modeling with accuracy close to quantum calculations.Howev...Universal machine learning interatomic potentials(uMLIPs)have emerged as powerful tools for accelerating atomistic simulations,offering scalable and efficient modeling with accuracy close to quantum calculations.However,their reliability and effectiveness in practical,real-world applications remain an open question.Metal-organic frameworks(MOFs)and related nanoporous materials are highly porous crystals with critical relevance in carbon capture,energy storage,and catalysis applications.Modeling nanoporous materials presents distinct challenges for uMLIPs due to their diverse chemistry,structural complexity,including porosity and coordination bonds,and the absence from existing training datasets.Here,we introduce MOFSimBench,a benchmark for evaluating uMLIPs on key materials modeling tasks for nanoporous materials,including structural optimization,molecular dynamics(MD)stability,bulk property prediction,and host-vip interactions.Evaluating 20 models from various architectures,we find that top-performing uMLIPs consistently outperform classical force fields and fine-tuned machine learning potentials across all tasks,demonstrating their readiness for deployment in nanoporous materials modeling.Our analysis highlights that data quality plays a more critical role than model architecture in determining performance across all evaluated uMLIPs.We release our modular and extensible benchmarking framework at https://github.com/AI4ChemS/mofsim-bench,providing an open resource to guide the adoption for nanoporous materials modeling and further development of uMLIPs.展开更多
Machine learning interatomic potentials(MLIPs)have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency.While leading MLIPs rely on represe...Machine learning interatomic potentials(MLIPs)have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency.While leading MLIPs rely on representing atomic environments using spherical tensors,Cartesian representations offer potential advantages in simplicity and efficiency.Here,we introduce the Cartesian Atomic Moment Potential(CAMP),an approach to building MLIPs entirely in Cartesian space.CAMP constructs atomic moment tensors from neighboring atoms and employs tensor products to incorporate higher body-order interactions,providing a complete description of local atomic environments.Integrated into a graph neural network(GNN)framework,CAMP enables physically motivated,systematically improvable potentials.The model demonstrates excellent performance across diverse systems,including periodic structures,small organic molecules,and two-dimensional materials,achieving accuracy,efficiency,and stability in molecular dynamics simulations that rival or surpass current leadingmodels.CAMPprovides apowerful tool for atomistic simulations to accelerate materials understanding and discovery.展开更多
Modeling the response of material and chemical systems to electric fields remains a longstanding challenge.Machine learning interatomic potentials(MLIPs)offer an efficient and scalable alternative to quantum mechanica...Modeling the response of material and chemical systems to electric fields remains a longstanding challenge.Machine learning interatomic potentials(MLIPs)offer an efficient and scalable alternative to quantum mechanical methods,but do not by themselves incorporate electrical response.Here,we show that polarization and Born effective charge(BEC)tensors can be directly extracted from longrange MLIPs within the Latent Ewald Summation(LES)framework,solely by learning from energy and force data.Using this approach,we predict the infrared spectra of bulk water under zero or finite external electric fields,ionic conductivities of high-pressure superionic ice,and the phase transition and hysteresis in ferroelectric PbTiO_(3)perovskite.This work thus extends the capability of MLIPs to predict electrical response–without training on charges or polarization or BECs–and enables accurate modeling of electric-field-driven processes in diverse systems at scale.展开更多
In modern computational materials,machine learning has shown the capability to predict interatomic potentials,thereby supporting and accelerating conventional molecular dynamics(MD)simulations.However,existing models ...In modern computational materials,machine learning has shown the capability to predict interatomic potentials,thereby supporting and accelerating conventional molecular dynamics(MD)simulations.However,existing models typically sacrifice either accuracy or efficiency.Moreover,efficient models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs.Here,we introduce an efficient equivariant graph neural network(E^(2)GNN)that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals.Rather than relying on higher-order representations,E^(2)GNN employs a scalar-vector dual representation to encode equivariant features.By learning geometric symmetry information,our model remains efficient while ensuring prediction accuracy and robustness through the equivariance.Our results show that E^(2)GNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets,which include catalysts,molecules,and organic isomers.Furthermore,we conductMDsimulations using the E^(2)GNN force field across solid,liquid,and gas systems.It is found that E^(2)GNN can achieve the accuracy of ab initio MD across all examined systems.展开更多
There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and str...There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and stresses,combining innovative architectures with big data.Here,we benchmark these models on their ability to predict harmonic phonon properties,which are critical for understanding the vibrational and thermal behavior of materials.Using around 10000 ab initio phonon calculations,we evaluate model performance across various phonon-related parameters to test the universal applicability of these models.The results reveal that some models achieve high accuracy in predicting harmonic phonon properties.However,others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium.These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.展开更多
文摘All-solid-state batteries(ASSBs)represent a next-generation energy storage technology,offering enhanced safety,higher energy density,and improved cycling stability compared to conventional liquid-electrolyte-based lithium-ion batteries.Understanding and optimizing the complex chemistries and interfaces that underpin ASSB performance present significant challenges from both experimental and modeling perspectives.In particular,atomistic simulations face difficulties in capturing the complex structure,disorder,and dynamic evolution of materials and interfaces under practically relevant conditions.While established methods such as density functional theory and classical force fields have provided valuable insights,some questions remain difficult to address,particularly those involving large system sizes or long timescales.Recently,machine learning interatomic potentials(MLIPs)have emerged as a transformative tool,enabling atomistic simulations at length and time scales that were previously challenging to access with conventional approaches.By delivering near first-principles accuracy with much greater efficiency,MLIPs open new avenues for large-scale,long-timescale,and high-throughput simulations of solid-state battery materials.In this review,we present a comparative overview of density functional theory,classical force fields,and MLIPs,highlighting their respective strengths and limitations in ASSB research.We then discuss how MLIPs enable simulations that reach longer timescales,larger system sizes,and support high-throughput calculations,providing unique insights into ion transport and interfacial evolution in ASSBs.Finally,we conclude with a summary and outlook on current challenges and future opportunities for expanding MLIP capabilities and accelerating their impact in solid-state battery research.
基金support of the RSF Grant No.24-11-00139(analytics,numerical results,manuscript writing)Daxing Xiong acknowledges the support of the NNSF Grant No.12275116,the NSF Grant No.2021J02051,and the startup fund Grant No.MJY21035For Aleksey A.Kudreyko,this work was supported by the Bashkir StateMedicalUniversity StrategicAcademic Leadership Program(PRIORITY-2030)(analytics).
文摘Molecular dynamics(MD)is a powerful method widely used in materials science and solid-state physics.The accuracy of MD simulations depends on the quality of the interatomic potentials.In this work,a special class of exact solutions to the equations of motion of atoms in a body-centered cubic(bcc)lattice is analyzed.These solutions take the form of delocalized nonlinear vibrational modes(DNVMs)and can serve as an excellent test of the accuracy of the interatomic potentials used in MD modeling for bcc crystals.The accuracy of the potentials can be checked by comparing the frequency response of DNVMs calculated using this or that interatomic potential with that calculated using the more accurate ab initio approach.DNVMs can also be used to train new,more accurate machine learning potentials for bcc metals.To address the above issues,it is important to analyze the properties of DNVMs,which is the main goal of this work.Considering only the point symmetry groups of the bcc lattice,34 DNVMs are found.Since interatomic potentials are not used in finding DNVMs,they are exact solutions for any type of potential.Here,the simplest interatomic potentials with cubic anharmonicity are used to simplify the analysis and to obtain some analytical results.For example,the dispersion relations for small-amplitude phonon modes are derived,taking into account interactions between up to the fourth nearest neighbor.The frequency response of the DNVMs is calculated numerically,and for some DNVMs examples of analytical analysis are given.The energy stored by the interatomic bonds of different lengths is calculated,which is important for testing interatomic potentials.The pros and cons of using DNVMs to test and improve interatomic potentials for metals are discussed.Since DNVMs are the natural vibrational modes of bcc crystals,any reliable interatomic potential must reproduce their properties with reasonable accuracy.
基金sponsored by the Science and Technology Foundation of Guizhou Provincial Education Department(No.QJJ[2024]60)Guizhou Provincial Basic Research Program(Natural Science)(No.QKHJC[2024]Youth 214)+1 种基金Science and Technology Foundation of Guizhou Minzu University(No.GZMUZK[2024]QD21)Research Projects of Anshun University(No.asxybsjj202413).
文摘To explore atomic-level phenomena in the Cu-Ni-Sn alloy,a second nearest-neighbor modified embedded-atom method(2NN MEAM)potential has been developed for the Cu-Ni-Sn system,building upon the work of other researchers.This potential demonstrates remarkable accuracy in predicting the lattice constant,with a relative error of less than 0.5%when compared to density functional theory(DFT)results,and it achieves a 10%relative error in the enthalpy of formation compared to experimental data,marking substantial advancements over prior models.The bulk modulus is predicted with a relative error of 8%compared to DFT.Notably,the potential effectively simulates the processes of melting and solidification of Cu-15Ni-8Sn,with a simulated melting point that closely aligns with the experimental value,within a 7.5%margin.This serves as a foundation for establishing a 2NN MEAM potential for a flawless Cu-Ni-Sn system and its microalloying systems.
文摘Al,Ca,and Zn are representative commercial alloying elements for Mg alloys.To investigate the effects of these elements on the deformation and recrystallization behaviors of Mg alloys,we develop interatomic potentials for the Al-Ca,Al-Zn,Mg-Al-Ca and Mg-Al-Zn systems based on the second nearest-neighbor modified embedded-atom method formalism.The developed potentials describe structural,elastic,and thermodynamic properties of compounds and solutions of associated alloy systems in reasonable agreement with experimental data and higher-level calculations.The applicability of these potentials to the present investigation is confirmed by calculating the generalized stacking fault energy for various slip systems and the segregation energy on twin boundaries of the Mg-Al-Ca and Mg-Al-Zn alloys,accompanied with the thermal expansion coefficient and crystal structure maintenance of stable compounds in those alloys.
文摘One of the major tasks in a molecular dynamics （MD） simulation is the selection of adequate potential functions, from which forces are derived. If the potentials do not model the behaviour of the atoms correctly, the results produced from the simulation would be useless. Three popular potentials, namely, Lennard-Jones （L J）, Morse, and embedded-atom method （EAM） potentials, were employed to model copper workpiece and diamond tool in nanometric machining. From the simulation results and further analysis, the EAM potential was found to be the most suitable of the three potentials. This is because it best describes the metallic bonding of the copper atoms; it demonstrated the lowest cutting force variation, and the potential energy is most stable for the EAM.
基金financially supported by the National Natural Science Foundation of China (Nos. 51,204,147 and 51274175)International Cooperation Project Supported by Ministry of Science and Technology of China (No. 2014DFA50320)International Science and Technology Cooperation Project of Shanxi Province (Nos. 2013081017 and 2012081013)
文摘Abstract The process of γ＇ phase precipitating from Ni75Al14MO11 is studied by a computational simulation technique based on microscopic phase-field kinetics model. We studied the phase transformation with the purpose of clarifying the influence of the nearest interatomic potential V Ni-Al （the nearest interatomic potential） on the precipitation process of γ＇ phase. The result demonstrates that there are two kinds of ordered phases, respective Llo and L12 in the early stage, and Llo phase transforms into L12 phase subsequently. For L12 phase, Ni atoms mainly occupy α site （face center positions）, while Al and Mo atoms occupy fl sites （the vertex positions）. When VNi-Al is increased by 10 MeV, the occupation probability of Ni atoms on α sites and Al atoms on β sites are enhanced. Enhanced VNi-Al facilitates clustering and ordering of Al atom, which promotes the formation of the γ＇ phase. At last, the simulation result was discussed by employing the thermodynamic stability.
文摘Molecular Dynamics(MD)simulation for computing Interatomic Potential(IAP)is a very important High-Performance Computing(HPC)application.MD simulation on particles of experimental relevance takes huge computation time,despite using an expensive high-end server.Heterogeneous computing,a combination of the Field Programmable Gate Array(FPGA)and a computer,is proposed as a solution to compute MD simulation efficiently.In such heterogeneous computation,communication between FPGA and Computer is necessary.One such MD simulation,explained in the paper,is the(Artificial Neural Network)ANN-based IAP computation of gold(Au_(147)&Au_(309))nanoparticles.MD simulation calculates the forces between atoms and the total energy of the chemical system.This work proposes the novel design and implementation of an ANN IAP-based MD simulation for Au_(147)&Au_(309) using communication protocols,such as Universal Asynchronous Receiver-Transmitter(UART)and Ethernet,for communication between the FPGA and the host computer.To improve the latency of MD simulation through heterogeneous computing,Universal Asynchronous Receiver-Transmitter(UART)and Ethernet communication protocols were explored to conduct MD simulation of 50,000 cycles.In this study,computation times of 17.54 and 18.70 h were achieved with UART and Ethernet,respectively,compared to the conventional server time of 29 h for Au_(147) nanoparticles.The results pave the way for the development of a Lab-on-a-chip application.
基金Project supported by the National Basic Research Program of China (Grant No. 2011CB606401)
文摘The lattice-inversion embedded-atom-method interatomic potential developed previously by us is extended to alkaline metals including Li,Na,and K.It is found that considering interatomic interactions between neighboring atoms of an appropriate distance is a matter of great significance in constructing accurate embedded-atom-method interatomic potentials,especially for the prediction of surface energy.The lattice-inversion embedded-atom-method interatomic potentials for Li,Na,and K are successfully constructed by taking the fourth-neighbor atoms into consideration.These angular-independent potentials markedly promote the accuracy of predicted surface energies,which agree well with experimental results.In addition,the predicted structural stability,elastic constants,formation and migration energies of vacancy,and activation energy of vacancy diffusion are in good agreement with available experimental data and first-principles calculations,and the equilibrium condition is satisfied.
基金Supported by the National Natural Science Foundation of China(No.2 9892 16 6 ,2 980 30 0 6 ,2 99830 0 1)
文摘We applied an approach to the development of many-body interatomic potentials for NiZr alloys,gaining an improved accuracy and reliability.The functional form of the potential is that of the embedded method,but it has been improved as follows. (1) The database used for the development of the potential includes both experimental data and a large set of energies of different structures of the alloys generated by Fab initio calculations. (2) The optimum parametrization of the potential for the given database is obtained by fitting. Using this approach we developed reliable interatomic potentials for Ni and Zr. The potential accurately reproduces basic equilibrium properties of the alloys.
文摘Motivated by the special theory of gradient elasticity (GradEla), a proposal is advanced for extending it to construct gradient models for interatomic potentials, commonly used in atomistic simulations. Our focus is on London’s quantum mechanical potential which is an analytical expression valid until a certain characteristic distance where “attractive” molecular interactions change character and become “repulsive” and cannot be described by the classical form of London’s potential. It turns out that the suggested internal length gradient (ILG) generalization of London’s potential generates both an “attractive” and a “repulsive” branch, and by adjusting the corresponding gradient parameters, the behavior of the empirical Lennard-Jones potentials is theoretically captured.
基金led by the Materials Project program KC23MP,supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Materials Sciences and Engineering Division under contract No.DE-AC02-05-CH11231Matthew Kuner was supported in part by the National Science Foundation Graduate Research Fellowship Program under Grant No.DGE-2146752+1 种基金Any opinions,findings,and conclusions or recommendations expressed in this material are those of the author(s)and do not necessarily reflect the views of the National Science Foundation.This research used the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California,Berkeley(supported by the UC Berkeley Chancellor,Vice Chancellor for Research,and Chief Information Officer)This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory(Supported by the Director,Office of Science,Office of Basic Energy Sciences,of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231)This research used resources of the National Energy Research Scientific Computing Center(NERSC),a U.S.Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory,operated under Contract No.DE-AC02-05CH11231 using NERSC award BES-ERCAP-0022838.The authors appreciate the insightful discussions from Mr.Yuan Chiang,Dr.Shivani Srivastava,Professor Bingqing Cheng,and Professor Mary Scott at UC Berkeley,and from Dr.Anubhav Jain at Lawrence Berkeley National Laboratory.
文摘We present MP-ALOE,a dataset of nearly 1 million DFT calculations using the accurate r^(2)SCAN metageneralized gradient approximation.Covering 89 elements,MP-ALOE was created using active learning and primarily consists of off-equilibrium structures.We benchmark a machine learning interatomic potential trained on MP-ALOE,and evaluate its performance on a series of benchmarks,including predicting the thermochemical properties of equilibrium structures;predicting forces of farfrom-equilibrium structures;maintaining physical soundness under static extreme deformations;and molecular dynamic stability under extreme temperatures and pressures.MP-ALOE shows strong performance on all of these benchmarks and is made public for the broader community to utilize.
基金funded by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Materials Sciences and Engineering Division under Contract No.DE-AC0205CH11231(Materials Project program KC23MP)supported by the computational resources provided by the Extreme Science and Engineering Discovery Environment(XSEDE),supported by National Science Foundation grant number ACI1053575+1 种基金the National Energy Research Scientific Computing Center(NERSC),a U.S.Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratorythe Swift Cluster resource provided by the National Renewable Energy Laboratory(NREL).
文摘Machine learning interatomic potentials(MLIPs)have introduced a new paradigm for atomic simulations.Recent advancements have led to universal MLIPs(uMLIPs)that are pre-trained on diverse datasets,providing opportunities for universal force fields and foundational machine learning models.However,their performance in extrapolating to out-of-distribution complex atomic environments remains unclear.In this study,we highlight a consistent potential energy surface(PES)softening effect in three uMLIPs:M3GNet,CHGNet,and MACE-MP-0,which is characterized by energy and force underprediction in atomic-modeling benchmarks including surfaces,defects,solid-solution energetics,ion migration barriers,phonon vibration modes,and general high-energy states.The PES softening behavior originates primarily from the systematically underpredicted PES curvature,which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets.Our findings suggest that a considerable fraction of uMLIP errors are highly systematic,and can therefore be efficiently corrected.We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs.
基金funding from the Horizon Europe MSCA Doctoral network grant n.101073486, EUSpecLabfunded by the European Union, and from the Novo Nordisk Foundation Data Science Research Infrastructure 2022 Grant: A high-performance computing infrastructure for data-driven research on sustainable energy materials, Grant no. NNF22OC0078009+1 种基金F.N. has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 899987K.S.T. is a Villum Investigator supported by VILLUM FONDEN (grant no. 37789).
文摘Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with ab initio accuracy.By replacing the costly density functional theory(DFT)computation of phonon modes with much faster MLIP phonon mode calculations,our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss.We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states.The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface.Across all the systems,we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes.This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity,opening pathways to high-throughput screening of defect-engineered materials.Ourwork not only demonstrates accelerated calculation of PL spectra with DFT accuracy,but also makes such calculations tractable for more complex materials.
基金support from the Cluster of Excellence“CUI:Advanced Imaging of Matter”—EXC 2056—project ID 390715994BiGmax,the Max Planck Society Research Network on Big-Data-Driven Materials-Science and the Max Planck-New York City Center for Non-Equilibrium Quantum Phenomena.The Flatiron Institute is a division of the Simons Foundation+1 种基金We also acknowledge support from the European Research Council MSCA-ITN TIMES under grant agreement 101118915S.S.and P.L.acknowledge support from the UFAST International Max Planck Research School.
文摘Emerging machine learning interatomic potentials(MLIPs)offer a promising solution for large-scale accurate material simulations,but stringent tests related to the description of vibrational dynamics in molecular crystals remain scarce.Here,we develop a general MLIP by leveraging the graph neural network-based MACE architecture and active-learning strategies to accurately capture vibrational dynamics across a range of polyacene-based molecular crystals,namely naphthalene,anthracene,tetracene and pentacene.Through careful error propagation,we show that these potentials are accurate and enable the study of anharmonic vibrational features,vibrational lifetimes,and vibrational coupling.In particular,we investigate large-scale host-vip systems based on these molecular crystals,showing the capacity of molecular-dynamics-based techniques to explain and quantify vibrational coupling between host and vip nuclear motion.Our results establish a framework for understanding vibrational signatures in large-scale complex molecular systems and thus represent an important step for engineering vibrational interactions in molecular environments.
基金support provided by the SciNet HPC Consortium and the Digital Research Alliance of Canadaas well as support from the state of Baden-Württemberg through bwHPC+2 种基金The project received financial support from the University of Toronto's Acceleration Consortium through the Canada First Research Excellence Fund under Grant number CFREF-2022-00042S.M.M. research program receives financial support from Natural Sciences and Engineering Research Council of Canada (NSERC) through the discovery programJ.H. acknowledges support from the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Sciences.
文摘Universal machine learning interatomic potentials(uMLIPs)have emerged as powerful tools for accelerating atomistic simulations,offering scalable and efficient modeling with accuracy close to quantum calculations.However,their reliability and effectiveness in practical,real-world applications remain an open question.Metal-organic frameworks(MOFs)and related nanoporous materials are highly porous crystals with critical relevance in carbon capture,energy storage,and catalysis applications.Modeling nanoporous materials presents distinct challenges for uMLIPs due to their diverse chemistry,structural complexity,including porosity and coordination bonds,and the absence from existing training datasets.Here,we introduce MOFSimBench,a benchmark for evaluating uMLIPs on key materials modeling tasks for nanoporous materials,including structural optimization,molecular dynamics(MD)stability,bulk property prediction,and host-vip interactions.Evaluating 20 models from various architectures,we find that top-performing uMLIPs consistently outperform classical force fields and fine-tuned machine learning potentials across all tasks,demonstrating their readiness for deployment in nanoporous materials modeling.Our analysis highlights that data quality plays a more critical role than model architecture in determining performance across all evaluated uMLIPs.We release our modular and extensible benchmarking framework at https://github.com/AI4ChemS/mofsim-bench,providing an open resource to guide the adoption for nanoporous materials modeling and further development of uMLIPs.
基金supported by the National Science Foundation under Grant No.2316667supported by the Center for HPC at the University of Electronic Science and Technology of China.This work also uses computational resources provided by the Research Computing Data Core at the University of Houston.
文摘Machine learning interatomic potentials(MLIPs)have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency.While leading MLIPs rely on representing atomic environments using spherical tensors,Cartesian representations offer potential advantages in simplicity and efficiency.Here,we introduce the Cartesian Atomic Moment Potential(CAMP),an approach to building MLIPs entirely in Cartesian space.CAMP constructs atomic moment tensors from neighboring atoms and employs tensor products to incorporate higher body-order interactions,providing a complete description of local atomic environments.Integrated into a graph neural network(GNN)framework,CAMP enables physically motivated,systematically improvable potentials.The model demonstrates excellent performance across diverse systems,including periodic structures,small organic molecules,and two-dimensional materials,achieving accuracy,efficiency,and stability in molecular dynamics simulations that rival or surpass current leadingmodels.CAMPprovides apowerful tool for atomistic simulations to accelerate materials understanding and discovery.
基金funding from the BIDMaP Postdoctoral Fellowship.
文摘Modeling the response of material and chemical systems to electric fields remains a longstanding challenge.Machine learning interatomic potentials(MLIPs)offer an efficient and scalable alternative to quantum mechanical methods,but do not by themselves incorporate electrical response.Here,we show that polarization and Born effective charge(BEC)tensors can be directly extracted from longrange MLIPs within the Latent Ewald Summation(LES)framework,solely by learning from energy and force data.Using this approach,we predict the infrared spectra of bulk water under zero or finite external electric fields,ionic conductivities of high-pressure superionic ice,and the phase transition and hysteresis in ferroelectric PbTiO_(3)perovskite.This work thus extends the capability of MLIPs to predict electrical response–without training on charges or polarization or BECs–and enables accurate modeling of electric-field-driven processes in diverse systems at scale.
基金supported by the National Natural Science Foundation of China(Grant No.62176272)Research and Development Program of Guangzhou Science and Technology Bureau(No.2023B01J1016)+2 种基金Key-Area Research and Development Program of Guangdong Province(No.2020B1111100001)Singapore MOE Tier 1(No.A-8001194-00-00)Singapore MOE Tier 2(No.A-8001872-00-00).
文摘In modern computational materials,machine learning has shown the capability to predict interatomic potentials,thereby supporting and accelerating conventional molecular dynamics(MD)simulations.However,existing models typically sacrifice either accuracy or efficiency.Moreover,efficient models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs.Here,we introduce an efficient equivariant graph neural network(E^(2)GNN)that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals.Rather than relying on higher-order representations,E^(2)GNN employs a scalar-vector dual representation to encode equivariant features.By learning geometric symmetry information,our model remains efficient while ensuring prediction accuracy and robustness through the equivariance.Our results show that E^(2)GNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets,which include catalysts,molecules,and organic isomers.Furthermore,we conductMDsimulations using the E^(2)GNN force field across solid,liquid,and gas systems.It is found that E^(2)GNN can achieve the accuracy of ab initio MD across all examined systems.
基金funding from the Horizon Europe MSCA Doctoral network grant n.101073486, EUSpecLab, funded by the European UnionS.B. and D.S. acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the project BO4280/11-1. H.C.W and M.A.L.M would like to thank the NHR Center PC2 for providing computing time on the Noctua 2 supercomputers.
文摘There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and stresses,combining innovative architectures with big data.Here,we benchmark these models on their ability to predict harmonic phonon properties,which are critical for understanding the vibrational and thermal behavior of materials.Using around 10000 ab initio phonon calculations,we evaluate model performance across various phonon-related parameters to test the universal applicability of these models.The results reveal that some models achieve high accuracy in predicting harmonic phonon properties.However,others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium.These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.
基金funding from a studentship jointly by ICASE and AWE-Nuclear Security Technologiestraining support by the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (EP/S022848/1). A.B.P acknowledges support from the CASTEP-USER grant funded by UK Research and Innovation (EP/W030438/1)+1 种基金L.B.P. acknowledges support from the EPSRC through the individual Early Career Fellowship (EP/T000163/1)Computing facilities were provided by the Scientific Computing Research Technology Platform of the University of Warwick. Part of the calculations were performed using the Sulis Tier 2 HPC platform hosted by the Scientific Computing Research Technology Platform at the University of Warwick. Sulis is funded by EPSRC Grant EP/T022108/1 and the HPC Midlands+ consortium.
