In this work, we present a concurrent atomistic-continuum (CAC) method for modeling and simulation of crystalline materials. The CAC formulation extends the Irving-Kirkwood procedure for deriving transport equations and fluxes for homogenized molecular systems to that for polyatomic crystalline materials by employing a concurrent two-level description of the structure and dynamics of crystals. A multiscale representation of conservation laws is formulated, as a direct consequence of Newton's second law, in terms of instantaneous expressions of unit cell-averaged quantities using the mathematical theory of distributions. Finite element (FE) solutions to the conservation equations, as well as fluxes and temperature in the FE representation, are introduced, followed by numerical examples of the atomic-scale structure of interfaces, dynamics of fracture and dislocations, and phonon thermal transport across grain boundaries. In addition to providing a methodology for concurrent multiscale simulation of transport processes under a single theoretical framework, the CAC formulation can also be used to compute fluxes (stress and heat flux) in atomistic and coarse-grained atomistic simulations.
I. INTRODUCTION
Solid state physics describes the structure of all crystals in terms of primitive unit cells. These primitive unit cells, each containing an identical group of atoms, are the building blocks of crystals. When added continuously in space, they form the structure of a crystal and completely fill the space the crystal occupies,1 cf. Fig. 1.
Although the motion of an atom in a crystal is an irregularly fluctuating function of time, it turns out that the dynamics of a crystal can be most readily described, not in terms of individual atoms, but in terms of traveling waves, named lattice vibrations by Max Born. These vibrational modes are called phonons and are of two types: acoustic and optical, cf. Fig. 2. With the former, atoms in a unit cell move in the same phase, resulting in the deformation of the lattice; with the latter, atoms undergo relative motion within the lattice cells, leaving the lattice unchanged. Thus, the displacements of atoms in a crystal can be described as the sum of continuous lattice displacements and the internal displacements of the atoms relative to the lattice.2
It is seen that the solid state physics description of the structure and dynamics of crystals is based on a concurrent atomistic-continuum (CAC) approach: the structure is continuous at the lattice level but discrete at the atomic level; the lattice deformation is continuous until a defect is generated, cf. Fig. 3, while the internal motion is discrete.
Historically, atomistic and continuum descriptions offer two fundamentally different approaches to our understanding of crystalline matter. From the atomistic viewpoint, matter consists of discrete particles; while from the continuum viewpoint, matter is infinitely divisible. The continuum view leads to formulations of field equations of conservation laws. Supplemented by constitutive relations, such as Hooke's law and Fourier's law, these conservation equations serve as the governing equations in continuum modeling of materials.
This work introduces the concurrent atomistic-continuum (CAC) method that links and unifies the atomistic and continuum descriptions of crystalline materials. The Irving-Kirkwood procedure for linking the molecular description to hydrodynamical equations is reviewed in Sec. II; the CAC formalism is introduced in Sec. III; the finite element (FE) formulation of CAC as well as the fluxes and temperature in the FE representation is outlined in Sec. IV; numerical examples are presented in Sec. V, followed by a summary in Sec. VI. Detailed derivations of the conservation equations using the mathematical theory of distributions for monatomic crystals are included in Appendix A and that for polyatomic crystals in Appendix B.
II. THE IRVING-KIRKWOOD PROCEDURE
Formulas for fluxes (stress and heat flux) are then obtained through comparing the rate equations with the differential equations of hydrodynamics.
The IK formalism has inspired numerous efforts to link molecular variables to field quantities in classical continuum mechanics.5–8 To understand the motivation of a new formalism, we recall the following noted by Irving and Kirkwood3 and by Kirkwood.9
Local densities and fluxes in the IK formulation are point functions. “The point functions, although averaged neither over space nor time, satisfy equations that are identical in form to the equations of hydrodynamics for a single component, single phase system.”3 These equations were called “the hydrodynamical-like equations” in the IK paper.3 It was noted that “to obtain the hydrodynamical equations themselves it is merely necessary to perform the appropriate space and time averages.”3
The densities for mass, momentum, and energy were defined without restriction to a single component single phase system. For fluxes (stress and heat flux), IK stated that “we must impose this restriction” and the flux formulas obtained with their formalism are “only valid for a single component, single phase system.”3
The statistical mechanical theory of transport processes published in a series of 14 papers by Kirkwood and co-workers only considers rigid molecules, ignoring the internal degrees of freedom (DOFs) of the molecules. In the first one of the series, Kirkwood envisioned extension of the formulation to molecules possessing internal degrees of freedom (DOFs).9
III. THE CAC FORMALISM
The CAC formalism is an extension of the IK procedure for homogeneous hydrodynamic systems to a two-level structural description of crystals that includes the atomic DOFs within each lattice cell.10,11 With such an extension, the CAC formulation is applicable to polyatomic crystalline materials and can be used to solve atomic trajectories or quantify instantaneous fluxes, conferring full characteristics of a concurrent atomistic-continuum method.
A. Ensemble-averaged local densities
B. Space and time-averaged local densities
Note that the box functions defined in Eqs. (8) and (9) have a jump discontinuity at the enclosing surfaces V and Vα, respectively. These functions do not have derivatives in the classical sense. It thus requires a generalization of the concept of a function. The corresponding mathematical theory is known as the theory of distributions.17 This theory will be used throughout this work as the mathematical tool to derive the conservation laws for physical quantities described by nondifferentiable functions.
It is noted that by integrating Eq. (5) over V and invoking the divergence theorem, one obtains an integral equation in the form of Eq. (14). However, Eq. (5) is not valid instantaneously and it requires the fluxes to be differentiable. By contrast, the conservation equation in Eq. (14) holds instantaneously in the distributional sense and the fluxes are not required to be continuous functions.
C. Conservation equations and fluxes
Here, , , , defined in Eq. (A3), and , defined in Eq. (A4), represent the potential and kinetic flux as a line-plane intersection problem in space and time, respectively.
The link between atomistic and continuum descriptions at this point has fundamentally departed from the IK formalism. As distinct from classical continuum mechanics, this new formulation describes the structure and properties of a crystalline system as a continuous function in x at the lattice level, cf. Figs. 3 and 5, while discrete in α or y at the atomic level; fluxes (stress and heat flux) are composed of components resulting from the motion and deformation of lattice cells (denoted as t and q, respectively) and the rearrangement of atoms within the cells (denoted as τ and j, respectively). Most importantly, instead of linking molecular variables to classical continuum mechanics equations, the CAC formalism leads to a new representation of conservation laws and fluxes.
D. Temperature
An important difference between atomistic and classical continuum theories is the definition of temperature; in the former, temperature is a derived quantity, while in the latter, temperature is a fundamental state variable. Specifically, the abiding definition of temperature in classical molecular simulations is the kinetic temperature defined by the kinetic theory in terms of the mean kinetic energy of the random motion of atoms, while that in quantum calculations is based on the mean energy of phonons.
For a given quantum temperature, the corresponding classical temperature can be calculated using Eq. (28). Figure 6 presents such calculations for LiF and SrTiO3. As can be seen from Fig. 6, the two temperatures are different at low temperature, but the difference becomes negligible as the temperature increases. At 0 K quantum temperature, LiF and SrTiO3 have a zero-point energy equivalent to 237.1 K and 279.7 K classical temperature, respectively, and hence there are corresponding motions at 0 K, i.e., the zero-point vibrations.
IV. FINITE ELEMENT FORMULATION
As shown in Fig. 5, a local density such as the mass density in a polyatomic system is not a continuous function at the atomic scale. By decomposing the atomic position into the position of the lattice cell, x, and its internal position within the unit cell at x, we are able to express a density aα(x,t) or a(x, y, t) at an equilibrium or steady state system as a homogeneous and continuous function in x. Consequently, the field equations of the conservation laws can be discretized in x and solved using continuum-based numerical methods such as the finite element (FE) method.
A. Temperature in FE formulation
The kinetic energy of an element can also be expressed in terms of phonons using the phonon dispersion relations of the FE model.21 However, a FE model employing the usual trilinear shape functions cuts off phonons whose wavelengths are smaller than the element size. The motion of the FEs thus only represents a subset of the phonons of the system. This means that the FE mesh, i.e., the FE displacement approximation, uniquely determines the decomposition of total kinetic energy into and , where is represented by FE nodal velocities, while is related to through the temperature-dependent phonon number .
B. Finite element equations
This is the weak (Galerkin) form of Eq. (34). The integral can be numerically evaluated using Gaussian quadrature, and the equation can be solved for equilibrium or nonequilibrium processes.
For systems at thermal equilibrium, , is a fluctuating function of time with a zero mean everywhere except at the system's boundary if thermal expansion is constrained. It can thus be modeled as a constant force applied at the boundary. When atomic-scale fluctuations are important to the simulated phenomena such as thermal activation of dislocation motion or phase transition, can be modeled as a periodic or randomly fluctuating force in time with the frequencies of the phonons that are cut off by the FE shape functions.
For nonequilibrium processes, is a constant within an element if the usual linear interpolation is used. can then be modeled as a body force with a constant mean in each element but with periodic or random fluctuation in t. It is noted that, to model it as a random force, needs to be partitioned into a fluctuating part and a damping part, with the latter to be determined by and to balance the fluctuation force, according to the fluctuation-dissipation theorem.23
C. Fluxes in FEA solutions
Similar to that in MD,13–15,24 fluxes across a surface element in a CAC model can be computed through calculations of the interaction forces and atomic motions that cross the surface element. Not only can the stress and heat flux at any given surface element be calculated, their spatial distributions can also be plotted as contours for the entire model.
With the usual trilinear shape functions to approximate the displacement field in FE, maximum fluxes occur at the nodes of the elements. Thus, only fluxes at the nodes and a few other locations on the boundaries of the elements are needed for the flux contours, such as the unit cells shown in Fig. 7. In regions of structural disorders or discontinuities, such as those near a dislocation core or crack tip, atomic-scale fluxes can be calculated using Eqs. (18) and (19) and plotted as a contour at atomic resolution.
V. NUMERICAL EXAMPLES
Currently, there are two public versions of CAC codes, the CAC package in LAMMPS and PyCAC. The general procedure for a CAC simulation can be summarized as follows:
Select an interatomic potential(s) for the system;
Discretize the system based on its morphological length scales, using atomic resolution for atomic-scale structural disorders such as grain boundaries (GBs), while using FEs for single crystal regions with the mesh size determined by, e.g., the smallest possible dislocation spacing;
Relax the system with initial temperature. One may need to systematically change the initial configurations to obtain the dislocation or GB structures that have the minimal energy;
Apply loading and boundary conditions;
Run simulation to solve for and to visualize atomic trajectories, which are either atomically resolved or interpolated through FE nodal displacements, for observations of dynamic processes or emerging phenomena, using software such as Ovito for atomic visualization or Tecplot for FE visualization; and
Calculate fluxes (stress or heat flux) or other transport properties.
To illustrate the applicability of CAC, in this section, we present three sets of prior simulation results. These simulations were performed at low temperature without considering the fluctuations from phonons whose wavelengths are smaller than the element size.
A. Atomic-scale structures of interfaces
CAC has been employed to study the structure and energy of grain boundaries (GBs) and their dynamic interaction with cracks and dislocations in SrTiO3 polycrystals.42,43 In these studies, GBs are atomically resolved, while regions away from GBs are coarse-grained. Simulation results show that both the energy and atomic displacements of SrTiO3 GBs from CAC agree well with those that from MD if the width of the atomically resolved region for the GBs is larger than 12 Å.42 The ghost forces reported at the atomic-continuum interface in many multiscale models are not present in the CAC models.42 The atomic-scale GB structures from CAC42 are shown in Fig. 8 to compare well with those from experimental results.47
Figure 9 presents CAC simulation results of the misfit dislocation networks in the phase interfaces in heteroepitaxial thin films. CAC is shown to have reproduced the experimentally observed dislocation networks in PbTe/PbSe (001) interfaces.48 The dislocation spacing in the system as a function of the epilayer thickness obtained by CAC is also in good agreement with the experimental measurements. In addition, the critical thickness above which misfit dislocations are observed to form in the heteroepitaxial thin film compares very well with experimental observations.49
B. Fracture and dislocations
Since the only constitutive relation in CAC is the nonlocal force field, and CAC is of an integral form, continuity between FEs is not required. Consequently, nucleation and propagation of dislocations and cracks can be simulated in the FE region via sliding and separation between elements. Simulation results can be output in terms of elements or in terms of atomic trajectories. This makes CAC a convenient tool for the simulation and visualization of the dynamic processes of defect nucleation, interaction, and propagation.
Figure 10 presents CAC simulation results for the loading of a notched solid normal to the notch depth. The simulations have captured the dynamics processes of crack initiation, propagation, and branching. They also reveal that the interactions of the stress waves that emitted from the propagating crack with those that are reflected back by the boundaries of the specimen lead to a sharp increase of the crack speed, which in turn triggers the dynamic instability and hence the branching of cracks in micrometer-scale specimens.26 In addition, we have measured the critical stress intensity factor and observed a crack speed near the speed of Rayleigh wave before crack branching.
CAC has also been used to characterize the complex dynamics of fast moving dislocations, including the energy intensities and the wavelengths of phonons emitted from sonic dislocations, as well as the velocity-dependent stress fluctuations around the core of moving dislocations. Figure 11 presents snapshots comparing dislocation motion simulated using CAC with a uniform mesh and that using MD, with the number of DOFs of the CAC model being 1% of that of MD. The dislocation velocity is measured to be ∼2900 m/s in both CAC and MD simulations. Mach cones in a V-shaped pattern of the phonon wave-fronts are observed in the wake of the sonic dislocations. Analysis of simulation results based on a wavelet transform shows that the faster a dislocation is moving, the longer the emitted phonon wavelength.29–32
Figure 12 presents CAC simulation results of the sequential slip transfer of a0 ̸2 [110](111) dislocations across a Σ3 (111) coherent twin boundary (CTB) in Ni56 under ostensibly quasistatic strain rate conditions. Simulation results shows that CAC can serve as an effective tool for the study of slip transfer reactions of sequences of incoming and outgoing long-range dislocation pileups of mixed dislocation characters. Five embedded atom method (EAM) potentials51–55 were compared; results demonstrate the uncertainty in computed dislocation-interface reactions associated with the use of a variety of interatomic potentials and suggest that the applicability of dislocation/GB interaction criteria in the literature for slip transfer may have limitations.
C. Phonon thermal transport
CAC solves for approximate atomic trajectories. Collective motion of atoms, i.e., phonons, and the nucleation and propagation of defects, as well as phonon scattering by defects and other phonons, naturally emerges in the simulation. It thus provides a unified treatment for phonon transport and defect dynamics, as both are represented by the same atomic motion, without the need to assume the nature of phonon transport or scattering, or the mechanism for defect nucleation, propagation, and interactions.
Figure 13 presents CAC simulation results of a heat pulse propagating across GBs.46 A phonon representation of heat pulses composed of spatio-temporal Gaussian wave packets obeying the Bose-Einstein distribution57 is used to mimic the coherent lattice excitation by ultrashort lasers. As can be seen from Fig. 13, the simulation has captured the following:
The phenomenon of phonon focusing “caustics,”58 manifested as the concentration of energy flux in the sixfold symmetric focusing pattern shown in Fig. 13(a);
The Kapitza resistance, shown in Fig. 13(d) as the discontinuities in the kinetic energy at the GBs;
Simultaneous ballistic and diffusive phonon transport across GBs, and a co-existence of coherent-incoherent phonon scattering by the GBs. These are clearly shown in Figs. 13(b)–13(d) as both wavelike transport of the propagating heat pulse and the breaking of the 6-fold symmetry of the phonon focusing pattern due to diffusive transport and incoherent scattering by the GBs; and
Local reconstruction of the GB structures, as shown in Fig. 13(e), as a result of the phonon-GB interaction during the heat pulse propagation.
VI. SUMMARY AND DISCUSSIONS
We have presented the CAC method for simulation of transport processes in crystalline materials. The formalism extends the Irving-Kirkwood statistical mechanical theory of transport processes for single-phase, single-component molecular systems to a concurrently coupled atomic-continuum description of polyatomic crystals. Conservation equations are derived in terms of instantaneous expressions of cell-averaged quantities using the mathematical theory of distributions.59 Fluxes are then obtained that quantify the flow of conserved quantities across the lattice cells as well as those that flow back and forth within the cells, as direct consequences of the local density definition and Newton's second law.
Basic features of the CAC formulation, in comparison with classical continuum mechanics and molecular dynamics, may be summarized as follows:
As in classical continuum mechanics, the CAC governing equations are field equations, and hence can be discretized and solved using the finite element method. However, unlike the situation with continuum mechanics, each element node in CAC represents a unit cell that further contains a group of atoms; the atomic interaction and atomic-level crystal structure are thus fully built into the formulation, with no additional constitutive equations other than an interatomic potential being needed.
By including the internal DOFs of each lattice cell associated with atomic movement in the formulation, CAC can resolve the atomic structure of crystalline materials and reproduce both acoustic and optical phonon modes. It also can measure fluxes at the atomic scale, such as stress near a dislocation core or heat flux across an interface or a defect.
Since CAC is derived in a bottom-up manner from the atomistic, it becomes identical to the underlying atomistic model when discretized at the lattice level. CAC thus has the full applicability of MD in predicting materials behavior without the need for a priori assumption for mechanisms. It can also go beyond MD to scale up for mesoscale simulations by utilizing the continuous structure and deformation in regions where structure disorders or other discontinuities are absent or where atomic resolution is not needed.
CAC is thus naturally a concurrent multiscale tool that resolves details to full atomic resolution at regions of interest, while coarse-graining to thousands of atoms per finite element elsewhere; it admits the propagation of dislocations, addresses stacking fault structures in the FE regions, and provides a unified treatment for material microstructures, defects, and phonon transport.
It can serve as a multiscale theory for concurrent atomistic-continuum simulation of polyatomic crystalline materials under a single set of governing equations, thereby expanding the current atomic-interaction-based simulation capabilities from the nanoscale to the mesoscale crystalline materials.
It provides consistent formulas for calculations of continuum quantities such as stress and heat flux in atomistic or CG atomistic simulations of heterogeneous materials, noting that the majority of flux formulas in the literature or currently implemented in MD software are only applicable to homogenized, single-phase, single-component materials.
We have also presented three sets of simulation results in this tutorial to illustrate the applicability of CAC in reproducing atomic-scale structures of interfaces, dynamic processes of dislocations and fracture, and phonon transport across GBs. These are low-temperature simulations, in which thermal fluctuations with wavelength smaller than the size of the elements in the FE regions are ignored.
For simulation of defect dynamics at finite temperature, the FE mesh size in CAC is determined by the density of defects as well as by the dominant wavelengths of phonons that interact with the defects. In regions where atomic-scale accuracy is necessary to capture interface and defect reconstruction, full atomic resolution is needed.
For low-temperature transport processes in which phonon-phonon scattering has a negligible effect, the usual FE trilinear shape functions may be replaced by those that combine the trilinear shape functions and Bloch wave functions consisting of phonons whose wavelengths are cut off by the shape function. This allows short-wavelength phonons from an atomically resolved region to traverse a coarse-grained FE region and enter another atomically resolved region73 so as to model phonon scattering by dislocations, impurities, GB or phase interfaces with all possible phonons.
For finite-temperature phonon transport when high-frequency short-wavelength phonons are dominated by thermally resistive74 or diffusive processes, the surface integrals of the kinetic momentum fluxes, i.e., , in the momentum equation can be modeled as a body force with a constant mean but fluctuating in time in terms of the frequencies of the missing phonons. This is intended to reproduce the phonon density of state of the underlying atomistic model, thereby approximating the effect of the thermal fluctuations by the missing phonons. Phonon transport represented by the motion and deformation of the elements, whether it is diffusive, ballistic, or simultaneously ballistic and diffusive, will naturally emerge in the simulation as the consequence of the conservation laws, the interatomic potential, and the initial temperature.
Finally, we would like to note that, while the conservation equations and expressions of fluxes and temperature in the CAC formulation are identical to those in an atomistic model of particles, the FE solutions to the conservation equations are approximate numerical solutions. Consequently, simulated phenomena and calculated fluxes using the FE approximations feature a trade-off of efficiency and accuracy. There are thus coarse-graining errors that may increase with the FE mesh size, depending on the nature of the phenomena and the morphological length scales of the materials. Advanced algorithms that can improve the accuracy and efficiency of the simulation, such as adaptive mesh refinement and advanced multiple time step algorithm, are needed. Also, the current CAC formulation is based on the traditional interatomic potentials with well-defined site energies. Future work will be needed to extend the formulation to quantum-accurate, machine-learned potentials.
ACKNOWLEDGMENTS
This material is based upon research supported by the U.S. Department of Energy (DOE), Basic Energy Sciences under Award No. DE-SC0006539. The contributions of D.L.M. and Y.C. are partially supported by the National Science Foundation (NSF) under Grant Nos. CMMI 1761553 and 1761512, respectively. We thank Zexi Zheng for generating the data plotted in Fig. 6.
APPENDIX A: CONSERVATION EQUATIONS FOR MONATOMIC CRYSTALS
The classical formulations of boundary value problems are based on the assumption that their solutions are smooth (i.e., with continuous derivatives) and that the equation is satisfied at every point in the domain of the problems. For problems that involve point mass, point charge, or other discreteness or discontinuities, the smoothness assumption breaks down. The mathematical tool for such problems is the theory of distributions, also known as the theory of generalized functions, developed by Laurent Schwartz in the 1950s.17 One generalized function frequently used in science and engineering is the Dirac delta-function introduced by Paul Dirac in his research in quantum mechanics and is employed in most statistical mechanics formulations to link a particle description to a field representation. The theory of distributions enables a rigorous solution to the mathematical difficulties in linking discrete and continuum descriptions that have been bypassed by ad hoc constructions or by heuristic arguments.
Appendixes A and B present formal derivations of conservation laws based on the mathematical theory of distributions. A more detailed discussion of the integral form of conservation laws and atomistic formulas for fluxes for monatomic crystals can be found in Refs. 13–16. Due to additional quantities such as temperature discussed in this work, there are notational changes in this paper.
1. Local density definition and the balance equation of linear momentum for monatomic crystals
For the rest of the appendixes, any formal integral of a δ-function, like the one in the right-hand side of [Eq. (A2)], is understood as a distribution defined by such a limit. Any relation between distributions obtained in this way is proved to be independent of the choice of regularization.59
It is noted that the time-interval average of over a time step is itself.
2. Conservation equation of energy for monatomic crystals
APPENDIX B: CONSERVATION EQUATIONS FOR POLYATOMIC CRYSTALS
1. Balance equation of linear momentum for polyatomic crystals
2. Conservation equation of energy for polyatomic crystals
Irving and Kirkwood noted that a definition for the site energies of particles is required for definition of energy density as well as for the derivation of a field representation of energy conservation law in terms of particle variables.3 The conservation equations and the expressions for fluxes derived in the appendixes are valid for any analytical many-body potentials with well-defined site energies. A detailed comparison and discussion of the fluxes defined as a surface density presented in Appendix A and the fluxes formulas expressed as a volume density can be found in Ref. 13. Specifically, analytical and MD simulation results presented in Ref. 13 show that replacing the Dirac δ with a finite-sized volume weighting function changes the fundamental nature of fluxes as a surface density. Consequently, the dynamic balance between the rate of change of the total momentum or energy in a volume element and the fluxes across the surface boundary cannot be established, leading to the failure of the volume averaged flux formulas in satisfying the momentum and energy conservation laws as well as typical transport boundary conditions. The equivalence between flux formulas expressed as a pair density derived using the kinetic theory75,76 and that expressed as a single density derived using statistical mechanics,3 as ensemble averaged point functions or as surface-averages, can be found in Ref. 16.