Showing papers on "Interatomic potential published in 2023"

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials.

Abstract: Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a growing interest has been developed in the replacement of empirical interatomic potentials (EIPs) with MLIPs, in order to conduct more accurate and reliable molecular dynamics calculations. As an exciting novel progress, in the last couple of years the applications of MLIPs have been extended towards the analysis of mechanical and failure responses, providing novel opportunities not heretofore efficiently achievable, neither by EIPs nor by density functional theory (DFT) calculations. In this minireview, we first briefly discuss the basic concepts of MLIPs and outline popular strategies for developing a MLIP. Next, by considering several examples of recent studies, the robustness of MLIPs in the analysis of the mechanical properties will be highlighted, and their advantages over EIP and DFT methods will be emphasized. MLIPs furthermore offer astonishing capabilities to combine the robustness of the DFT method with continuum mechanics, enabling the first-principles multiscale modeling of mechanical properties of nanostructures at the continuum level. Last but not least, the common challenges of MLIP-based molecular dynamics simulations of mechanical properties are outlined and suggestions for future investigations are proposed.

A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles.

Abstract: A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and nanostructured platinum, in particular nanoparticles. Across the range of tested properties, which include bulk elasticity, surface energetics, and nanoparticle stability, this potential shows excellent transferability and agreement with DFT, providing state-of-the-art accuracy at a low computational cost. We showcase the possibilities for modeling of Pt systems enabled by this potential with two examples: the pressure-temperature phase diagram of Pt calculated using nested sampling and a study of the spontaneous crystallization of a large Pt nanoparticle based on classical dynamics simulations over several nanoseconds.

Effect of Interatomic Potential on Simulation of Fracture Behavior of Cu/Graphene Composite: A Molecular Dynamics Study

Abstract: Interatomic interaction potentials are compared using a molecular dynamics modeling method to choose the simplest, but most effective, model to describe the interaction of copper nanoparticles and graphene flakes. Three potentials are considered: (1) the bond-order potential; (2) a hybrid embedded-atom-method and Morse potential; and (3) the Morse potential. The interaction is investigated for crumpled graphene filled with copper nanoparticles to determine the possibility of obtaining a composite and the mechanical properties of this material. It is observed that not all potentials can be applied to describe the graphene–copper interaction in such a system. The bond-order potential potential takes into account various characteristics of the bond (for example, the angle of rotation and bond lengths); its application increases the simulation time and results in a strong interconnection between a metal nanoparticle and a graphene flake. The hybrid embedded-atom-method/Morse potential and the Morse potential show different results and lower bonding between graphene and copper. All the potentials enable a composite structure to be obtained; however, the resulting mechanical properties, such as strength, are different.

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Abstract: Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set poses a significant challenge. Here, we compare the transferability of three MLIP approaches: (i) neural network potentials (NNP), (ii) physical LassoLars interactions potential (PLIP) and (iii) linear potentials with Belher-Parrinello descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We compared the obtained models with density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states and showed the superiority of both NNP and PLIP models. However, the NNP model performed poorly when compared to the other two linear models for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training set. While providing less accurate prediction for solid Zinc Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale systems and liquid phases. Our results are finally rationalized by a combination of different statistical analysis including spread in force evaluation, information imbalance, convex hull calculation, and density in descriptor space.

Hybrid interatomic potential for Sn

Abstract: To design materials for extreme applications, it is important to understand and predict phase transitions and their influence on material properties under high pressures and temperatures. Atomistic modeling can be a useful tool to assess these behaviors. However, this can be difficult due to the lack of fidelity of the interatomic potentials in reproducing this high pressure and temperature extreme behavior. Here, a hybrid EAM-R---which is the combination of embedded atom method (EAM) and rapid artificial neural network potential---for Tin (Sn) is described which is capable of accurately modeling the complex sequence of phase transitions between different metallic polymorphs as a function of pressure. This hybrid approach ensures that a basic empirical potential like EAM is used as a lower energy bound. By using the final activation function, the neural network contribution to energy must be positive, assuring stability over the whole configuration space. This implementation has the capacity to reproduce density functional theory results at 6 orders of magnitude slower than a pair potential for molecular dynamics simulation, including elastic and plastic characteristics and relative energies of each phase. Using calculations of the Gibbs free energy, it is demonstrated that the potential precisely predicts the experimentally observed phase changes at temperatures and pressures across the whole phase diagram. At 10.2 GPa, the present potential predicts a first-order phase transition between body-centered tetragonal (BCT) $\ensuremath{\beta}$-Sn and another polymorph of BCT-Sn. This structure transforms into body-centered cubic near the experimentally reported value at 33 GPa. Thus, the Sn potential developed in this paper can be used to study complex deformation mechanisms under extreme conditions of high pressure and strain rates unlike existing potentials. Moreover, the framework developed in this paper can be extended for different material systems with complex phase diagrams.

Comparative studies of interatomic potentials for modeling point defects in wurtzite GaN

Abstract: In this paper, a new version of the Stillinger–Weber (SW) potential for wurtzite GaN is presented, by which we systematically explore the structural and thermodynamical properties of native point defects and their complexes. In parallel, the semi-empirical Modified Embedded-Atom Method (MEAM) potential is selected for comparison. The SW and MEAM potentials are assessed by the reproduction of the fundamental properties of wurtzite GaN and by the ability to describe the inversion domain boundaries and the wurtzite–rocksalt phase transition. Then the structural search of native point defects and their complexes in GaN is implemented using both SW and MEAM potentials with the benchmark of Density Functional Theory (DFT) calculations. Besides vacancies and antisites, four N and five Ga interstitials are confirmed by refining the DFT calculations, among which two N split interstitials [Formula: see text] and [Formula: see text], and two Ga split interstitials, [Formula: see text] and [Formula: see text], are observed for the first time. The SW potential correctly predicts the octahedral occupation GaOct to be the most stable Ga interstitial, while the MEAM potential predicts the ground state of the [Formula: see text] split interstitial [Formula: see text] as the most stable N interstitial. However, neither of the two potentials could simultaneously generate the most stable configurations of N and Ga interstitials. The investigations of point defect complexes reveal that N octahedral Frenkel [FrenkelOct(N)] and paired antisite (NGaGaN) defects are unstable and get converted into [Formula: see text] configurations with different separations between VN and [Formula: see text] point defects based on the DFT calculations. The formation energies calculated by the DFT and SW potential demonstrate that Schottky, Ga octahedral Frenkel [FrenkelOct(Ga)], and [Formula: see text] point defect complexes are energetically feasible and that they should not dissociate into two isolated point defects. In contrast, the MEAM potential predicts the dissociation to be exothermic for Schottky and [Formula: see text]. Overall, the structural features concerned with N–N or Ga–Ga bonds relaxed by the SW potential are more consistent with DFT calculations than the MEAM counterpart.

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Abstract: Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training-set poses a significant challenge. Here, we compare the transferability of three MLIP approaches: i) Neural Network Potentials (NNP), ii) Physical LassoLars Interactions Potential (PLIP) and iii) Linear Potentials with Belher-Parrinello descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We obtained good agreement between each MLIP models and density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states. However, the NNP model performed poorly when compared to the other two linear models for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training-set. While providing less accurate prediction for solid Zinc Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale systems and liquid phases

A machine learning potential for simulating infrared spectra of nanosilicate clusters.

Abstract: The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here, we apply an accurate and efficient protocol to collect training data for constructing a neural network-based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium and in circumstellar environments.

Machine Learning Interatomic Potential to Investigate Fundamentals of Electrolytes for Li-ion Solid-State Batteries

Abstract: Machine learning and deep learning are used to construct interatomic potential with superior performance by satisfying the accuracy of density functional theory (DFT) calculations while requiring computational resources comparable to those required for classical molecular dynamics simulations. In this study, the machine learning interatomic potential (MLIP) is successfully constructed using moment tensor potential (MTP) for predicting the ionic conductivity of Li-ion solid-state electrolytes with Li-Ge-P-X′ and Li-X″-P-S structures, where X′ = O, S, or Se and X″ = Ge, Si, or Sn. Ab initio molecular dynamics (AIMD) simulations are performed to construct the initial training database for MTP; the constructed MLIP exhibits excellent accuracy with an R2 value of 0.98 for predicting the potential energy value. The excellent performance of MLIP is further validated by calculating the lattice constant and bulk modulus. Finally, the ionic conductivity is obtained by performing MTP-based molecular dynamics (MD); the predicted value exhibits good agreement with previous AIMD results. Further, MTP-MD evidently runs three orders of magnitude faster than AIMD. The obtained results clearly demonstrate that MLIP can be used to rapidly determine promising solid-state electrolytes with accuracy comparable to that of DFT while greatly reducing the computational cost.

Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>KNb</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> perovskite

Abstract: Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of KNbO3 (as a representative system of (K,Na)NbO3) by using a deep neural network model. Including first-principles calculation data into the training dataset ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefits the theoretical study of ferroelectrics.

Nanoindentation of tungsten: From interatomic potentials to dislocation plasticity mechanisms

Abstract: In this study, we employed molecular dynamics simulations, both traditional and machine learned, to emulate spherical nanoindentation experiments of crystalline W matrices at different temperatures and loading rates using different approaches, such as EAM, EAM with Ziegler, Biersack, and Littmark corrections, modified EAM, analytic bond-order approach, and a recently developed machine-learned tabulated Gaussian approximation potential (tabGAP) framework for describing the W-W interaction and plastic deformation mechanisms. Results showed similarities between the recorded load-displacement curves and dislocation densities, for different interatomic potentials and crystal orientations at low and room temperature. However, we observe concrete differences in the early stages of elastic-to-plastic deformation transition, revealing different mechanisms for dislocation nucleation and dynamics during loading, especially at higher temperatures. This is attributed to the particular features of orientation dependence in crystal plasticity mechanisms and, characteristically, the stacking fault and dislocation glide energies information in the interatomic potentials, with tabGAP being the one with the most well-trained results compared to density functional theory calculations and experimental data.

Ab initio calculations of the interaction potentials and thermodynamic functions for ArN and ArN+

Abstract: Argon compounds play an important role in the mass spectrometry with inductively coupled plasma and other applications. At the same time, there is a little knowledge of their electronic terms and thermodynamic functions due to the complexity of experimental observations. In this work, the ab initio simulations are performed to obtain the interatomic interaction potentials for the ground and excited states of ArN and ArN+. Using these potentials, the vibrational‐rotational partition functions and thermodynamic properties in the gas phase are calculated for these molecules at the temperature range of 298.15–10,000 K. The errors of the thermodynamic functions associated with the approximation of interatomic interaction potentials are estimated.

High accuracy interatomic potential model for binary collision approximation and its application into sputtering yield estimation for amorphous carbon

Abstract: Abstract Although binary collision approximation (BCA) and molecular dynamics (MD) are well used for plasma-material interaction simulation, the atomic collision in the energy above 10 eV is often out of scope in general potential model for MD. For BCA, the Ziegler–Biersack–Littmark (ZBL) potential has been often employed for high energy collision. In the present work, as one of modernizations of BCA, more accurate potential model for high energy collision, which is named ReGenerated ZBL (ReGZ) potential, was proposed. The function from of the ReGZ potential was analytically derived from the spherical electron density of an independent atom. To evaluate the potential, sputtering yield and reflection coefficient were compared between the BCA simulations using the ReGZ potential and the ZBL potential. In addition, by also improving the surface binding energy by density functional theory, the sputtering yield using the ReGZ potential becomes consistent with existing results.

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Abstract: Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training-set poses a significant challenge. Here, we compare the transferability of three MLIP approaches: i) Neural Network Potentials (NNP), ii) Physical LassoLars Interactions Potential (PLIP) and iii) Linear Potentials with Belher-Parrinello descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We compared the obtained models with density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states and showed the superiority of both NNP and PLIP models. However, the NNP model performed poorly when compared to the other two linear models for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training-set. While providing less accurate prediction for solid Zinc Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale systems and liquid phases. Our results are finally rationalized by a combination of different statistical analysis including spread in force evaluation, information imbalance, convex hull calculation and density in descriptor space.

Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk

Abstract: We analyse the efficacy of machine learning (ML) interatomic potentials (IP) in modelling gold (Au) nanoparticles. We have explored the transferability of these ML models to larger systems and established simulation times and size thresholds necessary for accurate interatomic potentials. To achieve this, we compared the energies and geometries of large Au nanoclusters using VASP and LAMMPS and gained better understanding of the number of VASP simulation timesteps required to generate ML-IPs that can reproduce the structural properties. We also investigated the minimum atomic size of the training set necessary to construct ML-IPs that accurately replicate the structural properties of large Au nanoclusters, using the LAMMPS-specific heat of the Au147 icosahedral as reference. Our findings suggest that minor adjustments to a potential developed for one system can render it suitable for other systems. These results provide further insight into the development of accurate interatomic potentials for modelling Au nanoparticles through machine learning techniques.

Derivation of phase field model for the second nearest interatomic potential of D0 structure and sedimentation process in NAlV alloy

Abstract: This paper obtains a new phase field method that can simultaneously calculate the interaction potential between the first and second neighbor atoms of the D022 structure Ni3V phase in the Ni0.75AlxV0.25−x alloy, and analyzes the interaction potential between the two neighbor atoms of the D022 structure with the change trend of temperature and concentration. The feasibility of using the phase field method to invert the interaction potential between two neighboring atoms of the D022 structure in the Ni0.75AlxV0.25−x alloy laws and the precipitation of D022 and the types of interfaces formed by D022 and L12 during the aging process to be used in the microscopic phase field equation is studied. The results show that with the increase/decrease of the temperature or the concentration of solute atoms, the first neighbor interaction potential between the first neighbor atoms of the D022 structure increases/decreases and the neighbor interaction potential between the second neighbor atoms of the D022 structure decreases/increases; Ni0.75AlxV0.25−x alloys form two ordered phase interfaces between L12 and D022 phases during high temperature precipitation; at the same temperature and the concentration, the calculated interatomic potential is similar to the interatomic potential obtained by other methods. The calculated interatomic potential is substituted into the phase field model, and the simulated alloy precipitation results are consistent with the experimental results.

First-principles molten salt phase diagrams through thermodynamic integration

Abstract: Precise prediction of phase diagrams in molecular dynamics (MD) simulations is challenging due to the simultaneous need for long time scales, large length scales and accurate interatomic potentials. We show that thermodynamic integration (TI) from low-cost force fields to neural network potentials (NNPs) trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid-liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange-correlation functionals for predicting the NaCl phase boundary, and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid-liquid phase boundaries for any material at an ab-initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

A machine learning potential for simulating infrared spectra of nanosilicate clusters

Abstract: The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here we apply an accurate and efficient protocol to collect training data for constructing a neural network based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics (MD) simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium (ISM) and in circumstellar environments.

Molecular dynamics simulation of Fe-Si alloys using a neural network machine learning potential

Abstract: Interatomic potential development using machine learning (ML) approaches has attracted a lot of attention in recent years because these potentials can effectively describe the structural and dynamical properties of complex materials at the atomistic level. In this work, we present the development of a neural network (NN) deep ML interatomic potential for Fe-Si alloys, and we demonstrate the effectiveness of the NN-ML potential in predicting the structures and energies of liquid and crystalline phases of Fe-Si alloys in comparison with the results from ab initio molecular dynamics simulations or experimental data. The developed NN-ML potential is also used to perform molecular dynamics simulations to study the structures of Fe-Si alloys with various compositions under rapid solidification conditions. The short-ranged orders in the rapidly solidified Fe-Si alloys are also analyzed by a cluster alignment method.

Fast proper orthogonal descriptors for many-body interatomic potentials

Abstract: The development of differentiable invariant descriptors for accurate representations of atomic environments plays a central role in the success of interatomic potentials for chemistry and materials science. We introduce a method to generate fast proper orthogonal descriptors for the construction of many-body interatomic potentials and discuss its relation to exising empirical and machine learning interatomic potentials. A traditional way of implementing the proper orthogonal descriptors has a computational complexity that scales exponentially with the body order in terms of the number of neighbors. We present an algorithm to compute the proper orthogonal descriptors with a computational complexity that scales linearly with the number of neighbors irrespective of the body order. We show that our method can enable a more efficient implementation for a number of existing potentials and provide a scalable systematic framework to construct new many-body potentials. The new potentials are demonstrated on a data set of density functional theory calculations for Tantalum and compared with other interatomic potentials.

Gaussian approximation potentials for accurate thermal properties of two-dimensional materials.

Abstract: Two-dimensional materials (2DMs) continue to attract a lot of attention, particularly for their extreme flexibility and superior thermal properties. Molecular dynamics simulations are among the most powerful methods for computing these properties, but their reliability depends on the accuracy of interatomic interactions. While first principles approaches provide the most accurate description of interatomic forces, they are computationally expensive. In contrast, classical force fields are computationally efficient, but have limited accuracy in interatomic force description. Machine learning interatomic potentials, such as Gaussian Approximation Potentials, trained on density functional theory (DFT) calculations offer a compromise by providing both accurate estimation and computational efficiency. In this work, we present a systematic procedure to develop Gaussian approximation potentials for selected 2DMs, graphene, buckled silicene, and h-XN (X = B, Al, and Ga, as binary compounds) structures. We validate our approach through calculations that require various levels of accuracy in interatomic interactions. The calculated phonon dispersion curves and lattice thermal conductivity, obtained through harmonic and anharmonic force constants (including fourth order) are in excellent agreement with DFT results. HIPHIVE calculations, in which the generated GAP potentials were used to compute higher-order force constants instead of DFT, demonstrated the first-principles level accuracy of the potentials for interatomic force description. Molecular dynamics simulations based on phonon density of states calculations, which agree closely with DFT-based calculations, also show the success of the generated potentials in high-temperature simulations.

Improved Singum Model Based on Finite Deformation of Crystals with the Thermodynamic Equation of State

Abstract: The recently published simplified singum model has been improved by using the thermodynamics-based equation of state (EOS) of solids to derive a new interatomic potential based on elastic constants. The finite deformation formulation under hydrostatic load has been used to evaluate the pressure-volume (p-v) relationship for the EOS of a solid. Using the bulk modulus and its derivatives at the free-stress state, one can construct the EOS, from which a new form of interatomic potential is derived for the singum, which exhibits much higher accuracy than the previous one obtained from the Fermi energy and provides a general approach to construct the interatomic potential. The long-range atomic interactions are approximated to be proportional to the pressure. This improved singum model is demonstrated for the face-centered cubic (FCC) lattice of single-crystalline aluminum. The elastic properties at different pressures are subsequently predicted through the bond length change and compared with the available experimental data. The model can be straightforwardly extended to higher-order terms of EOS with better accuracy and other types of lattices.