Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-14T13:22:01.941Z Has data issue: false hasContentIssue false
  • English
  • Français

PyCAC: The concurrent atomistic-continuum simulation environment

Published online by Cambridge University Press:  30 January 2018

Shuozhi Xu Open the ORCID record for Shuozhi Xu [Opens in a new window] ,
Thomas G. Payne ,
Hao Chen ,
Yongchao Liu ,
Liming Xiong ,
Youping Chen  and
David L. McDowell
Shuozhi Xu*
Affiliation:
California NanoSystems Institute, University of California, Santa Barbara, Santa Barbara, California 93106-6105, USA
Thomas G. Payne
Affiliation:
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0245, USA
Hao Chen
Affiliation:
Department of Aerospace Engineering, Iowa State University, Ames, Iowa 50011, USA
Yongchao Liu
Affiliation:
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
Liming Xiong
Affiliation:
Department of Aerospace Engineering, Iowa State University, Ames, Iowa 50011, USA
Youping Chen
Affiliation:
Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida 32611-6250, USA
David L. McDowell
Affiliation:
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0245, USA; and GWW School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0405, USA
*
a)Address all correspondence to this author. e-mail: [email protected]
Get access

Abstract

We present a novel distributed-memory parallel implementation of the concurrent atomistic-continuum (CAC) method. Written mostly in Fortran 2008 and wrapped with a Python scripting interface, the CAC simulator in PyCAC runs in parallel using Message Passing Interface with a spatial decomposition algorithm. Built upon the underlying Fortran code, the Python interface provides a robust and versatile way for users to build system configurations, run CAC simulations, and analyze results. In this paper, following a brief introduction to the theoretical background of the CAC method, we discuss the serial algorithms of dynamic, quasistatic, and hybrid CAC, along with some programming techniques used in the code. We then illustrate the parallel algorithm, quantify the parallel scalability, and discuss some software specifications of PyCAC; more information can be found in the PyCAC user’s manual that is hosted on www.pycac.org.

Keywords

metaldislocationssimulation
Type
Article
Information
Journal of Materials Research , Volume 33 , Issue 7: Focus Issue: Advanced Atomistic Algorithms in Materials Science , 13 April 2018 , pp. 857 - 871
Check for updates
Copyright
Copyright © Materials Research Society 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Contributing Editor: Vikram Gavini

References

REFERENCES

Abraham, F.F., Broughton, J.Q., Bernstein, N., and Kaxiras, E.: Spanning the continuum to quantum length scales in a dynamic simulation of brittle fracture. Europhys. Lett. 44, 783–787 (1998).CrossRefGoogle Scholar
McDowell, D.L.: A perspective on trends in multiscale plasticity. Int. J. Plast. 26, 1280–1309 (2010).CrossRefGoogle Scholar
Bulatov, V., Abraham, F.F., Kubin, L., Devincre, B., and Yip, S.: Connecting atomistic and mesoscale simulations of crystal plasticity. Nature 391, 669–672 (1998).CrossRefGoogle Scholar
Spearot, D.E. and Sangid, M.D.: Insights on slip transmission at grain boundaries from atomistic simulations. Curr. Opin. Solid State Mater. Sci. 18, 188–195 (2014).CrossRefGoogle Scholar
Phillips, R.: Multiscale modeling in the mechanics of materials. Curr. Opin. Solid State Mater. Sci. 3, 526–532 (1998).CrossRefGoogle Scholar
Tadmor, E.B. and Miller, R.E.: Modeling Materials: Continuum, Atomistic and Multiscale Techniques (Cambridge University Press, New York, 2012).Google Scholar
Xiong, L., Tucker, G., McDowell, D.L., and Chen, Y.: Coarse-grained atomistic simulation of dislocations. J. Mech. Phys. Solids, 59, 160–177 (2011).CrossRefGoogle Scholar
Xu, S., Che, R., Xiong, L., Chen, Y., and McDowell, D.L.: A quasistatic implementation of the concurrent atomistic-continuum method for FCC crystals. Int. J. Plast. 72, 91–126 (2015).CrossRefGoogle Scholar
Chen, Y., Zimmerman, J., Krivtsov, A., and McDowell, D.L.: Assessment of atomistic coarse-graining methods. Int. J. Eng. Sci. 49, 1337–1349 (2011).CrossRefGoogle Scholar
Chen, Y., Lee, J., and Xiong, L.: A generalized continuum theory and its relation to micromorphic theory. J. Eng. Mech. 135, 149–155 (2009).CrossRefGoogle Scholar
Xu, S.: The concurrent atomistic-continuum method: Advancements and applications in plasticity of face-centered cubic metals. Ph.D. thesis, Georgia Institute of Technology, 2016.Google Scholar
Knap, J. and Ortiz, M.: An analysis of the quasicontinuum method. J. Mech. Phys. Solids 49, 1899–1923 (2001).CrossRefGoogle Scholar
Eidel, B. and Stukowski, A.: A variational formulation of the quasicontinuum method based on energy sampling in clusters. J. Mech. Phys. Solids 57, 87–108 (2009).CrossRefGoogle Scholar
Xu, S., Xiong, L., Chen, Y., and McDowell, D.L.: An analysis of key characteristics of the Frank–Read source process in FCC metals. J. Mech. Phys. Solids 96, 460–476 (2016).CrossRefGoogle Scholar
Xu, S., Xiong, L., Deng, Q., and McDowell, D.L.: Mesh refinement schemes for the concurrent atomistic-continuum method. Int. J. Solids Struct. 90, 144–152 (2016).CrossRefGoogle Scholar
Xiong, L., Rigelesaiyin, J., Chen, X., Xu, S., McDowell, D.L., and Chen, Y.: Coarse-grained elastodynamics of fast moving dislocations. Acta Mater. 104, 143–155 (2016).CrossRefGoogle Scholar
Xu, S., Xiong, L., Chen, Y., and McDowell, D.L.: Shear stress- and line length-dependent screw dislocation cross-slip in FCC Ni. Acta Mater. 122, 412–419 (2017).CrossRefGoogle Scholar
Xiong, L., Xu, S., McDowell, D.L., and Chen, Y.: Concurrent atomistic-continuum simulations of dislocation-void interactions in fcc crystals. Int. J. Plast. 65, 33–42 (2015).CrossRefGoogle Scholar
Xu, S., Xiong, L., Chen, Y., and McDowell, D.L.: Validation of the concurrent atomistic-continuum method on screw dislocation/stacking fault interactions. Crystals 7, 120 (2017).CrossRefGoogle Scholar
Xu, S., Xiong, L., Chen, Y., and McDowell, D.L.: Edge dislocations bowing out from a row of collinear obstacles in Al. Scr. Mater. 123, 135–139 (2016).CrossRefGoogle Scholar
Xu, S., Xiong, L., Chen, Y., and McDowell, D.L.: Sequential slip transfer of mixed-character dislocations across Σ3 coherent twin boundary in FCC metals: A concurrent atomistic-continuum study. npj Comput. Mater. 2, 15016 (2016).CrossRefGoogle Scholar
Xu, S., Xiong, L., Chen, Y., and McDowell, D.L.: Comparing EAM potentials to model slip transfer of sequential mixed character dislocations across two symmetric tilt grain boundaries in Ni. JOM 69, 814–821 (2017).CrossRefGoogle Scholar
Chen, Y. and Lee, J.: Atomistic formulation of a multiscale field theory for nano/micro solids. Philos. Mag. 85, 4095–4126 (2005).CrossRefGoogle Scholar
Chen, Y.: Reformulation of microscopic balance equations for multiscale materials modeling. J. Chem. Phys. 130, 134706 (2009).CrossRefGoogle ScholarPubMed
Irving, J.H. and Kirkwood, J.G.: The statistical mechanical theory of transport processes. IV. The equations of hydrodynamics. J. Chem. Phys. 18, 817–829 (1950).CrossRefGoogle Scholar
Kittel, C.: Introduction to Solid State Physics, 8th ed. (Wiley, Hoboken, NJ, 2004).Google Scholar
Xiong, L., Chen, Y., and Lee, J.D.: Atomistic simulation of mechanical properties of diamond and silicon carbide by a field theory. Modell. Simul. Mater. Sci. Eng. 15, 535–551 (2007).CrossRefGoogle Scholar
Xiong, L. and Chen, Y.: Coarse-grained simulations of single-crystal silicon. Modell. Simul. Mater. Sci. Eng. 17, 035002 (2009).CrossRefGoogle Scholar
Chen, Y.: The origin of the distinction between microscopic formulas for stress and Cauchy stress. Europhys. Lett. 116, 34003 (2016).CrossRefGoogle Scholar
Chen, Y. and Diaz, A.: Local momentum and heat fluxes in transient transport processes and inhomogeneous systems. Phys. Rev. E 94, 053309 (2016).CrossRefGoogle ScholarPubMed
Swope, W.C., Andersen, H.C., Berens, P.H., and Wilson, K.R.: A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters. J. Chem. Phys. 76, 637–649 (1982).CrossRefGoogle Scholar
Sheppard, D., Terrell, R., and Henkelman, G.: Optimization methods for finding minimum energy paths. J. Chem. Phys. 128, 134106 (2008).CrossRefGoogle ScholarPubMed
Brünger, A., Brooks, C.L. III, and Karplus, M.: Stochastic boundary conditions for molecular dynamics simulations of ST2 water. Chem. Phys. Lett. 105, 495–500 (1984).CrossRefGoogle Scholar
Evans, D.J. and Morriss, G.: Statistical Mechanics of Nonequilibrium Liquids, 2nd ed. (Cambridge University Press, Cambridge, 2008).CrossRefGoogle Scholar
Tuckerman, M.E.: Statistical Mechanics: Theory and Molecular Simulation, 1st ed. (Oxford University Press, Oxford, New York, 2010).Google Scholar
Chen, X., Diaz, A., Xiong, L., McDowell, D.L., and Chen, Y.: Passing waves from atomistic to continuum. J. Comput. Phys. 354, 393–402 (2018).CrossRefGoogle Scholar
Berendsen, H.J.C., Postma, J.P.M., van Gunsteren, W.F., DiNola, A., and Haak, J.R.: Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984).CrossRefGoogle Scholar
Deng, Q., Xiong, L., and Chen, Y.: Coarse-graining atomistic dynamics of brittle fracture by finite element method. Int. J. Plast. 26, 1402–1414 (2010).CrossRefGoogle Scholar
Deng, Q. and Chen, Y.: A coarse-grained atomistic method for 3D dynamic fracture simulation. Int. J. Multiscale Comput. Eng. 11, 227–237 (2013).CrossRefGoogle Scholar
Xiong, L. and Chen, Y.: Coarse-grained atomistic modeling and simulation of inelastic material behavior. Acta Mech. Solida Sin. 25, 244–261 (2012).CrossRefGoogle Scholar
Xiong, L., Deng, Q., Tucker, G., McDowell, D.L., and Chen, Y.: A concurrent scheme for passing dislocations from atomistic to continuum domains. Acta Mater. 60, 899–913 (2012).CrossRefGoogle Scholar
Yang, S., Xiong, L., Deng, Q., and Chen, Y.: Concurrent atomistic and continuum simulation of strontium titanate. Acta Mater. 61, 89–102 (2013).CrossRefGoogle Scholar
Xiong, L., McDowell, D.L., and Chen, Y.: Nucleation and growth of dislocation loops in Cu, Al, and Si by a concurrent atomistic-continuum method. Scr. Mater. 67, 633–636 (2012).CrossRefGoogle Scholar
Yang, S., Zhang, N., and Chen, Y.: Concurrent atomistic-continuum simulation of polycrystalline strontium titanate. Philos. Mag. 95, 2697–2716 (2015).CrossRefGoogle Scholar
Yang, S. and Chen, Y.: Concurrent atomistic and continuum simulation of bi-crystal strontium titanate with tilt grain boundary. Proc. R. Soc. London, Ser. A 471, 20140758 (2015).Google ScholarPubMed
Xiong, L., McDowell, D.L., and Chen, Y.: Sub-THz phonon drag on dislocations by coarse-grained atomistic simulations. Int. J. Plast. 55, 268–278 (2014).CrossRefGoogle Scholar
Chen, X., Xiong, L., McDowell, D.L., and Chen, Y.: Effects of phonons on mobility of dislocations and dislocation arrays. Scr. Mater. 137, 22–26 (2017).CrossRefGoogle Scholar
Chen, X., Li, W., Xiong, L., Li, Y., Yang, S., Zheng, Z., McDowell, D.L., and Chen, Y.: Ballistic-diffusive phonon heat transport across grain boundaries. Acta Mater. 136, 355–365 (2017).CrossRefGoogle Scholar
Chen, X., Li, W., Diaz, A., Li, Y., Chen, Y., and McDowell, D.L.: Recent progress in the concurrent atomistic-continuum method and its application in phonon transport. MRS Commun., 7, 785–797 (2017).CrossRefGoogle Scholar
Chapra, S. and Canale, R.: Numerical Methods for Engineers, 6th ed. (McGraw-Hill Science/Engineering/Math, Boston, 2009).Google Scholar
Bitzek, E., Koskinen, P., Gähler, F., Moseler, M., and Gumbsch, P.: Structural relaxation made simple. Phys. Rev. Lett. 97, 170201 (2006).CrossRefGoogle ScholarPubMed
Eidel, B., Hartmaier, A., and Gumbsch, P.: Atomistic simulation methods and their application on fracture. In Multiscale Modelling of Plasticity and Fracture by Means of Dislocation Mechanics, 1st ed., Gumbsch, P. and Pippan, R., eds.; CISM International Centre for Mechanical Sciences (Springer, Vienna, 2010); pp. 1–57. doi: 10.1007/978-3-7091-0283-1_1.Google Scholar
Tadmor, E.B. and Miller, R.E.: Modeling Materials: Continuum, Atomistic and Multiscale Techniques, 1st ed. (Cambridge University Press, Cambridge, New York, 2012).Google Scholar
Allen, M.P. and Tildesley, D.J.: Computer Simulation of Liquids (Oxford University Press, New York, 1989).Google Scholar
Verlet, L.: Computer “experiments” on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules. Phys. Rev. 159, 98–103 (1967).CrossRefGoogle Scholar
Jones, J.E.: On the determination of molecular fields. II. From the equation of state of a gas. Proc. R. Soc. London, Ser. A 106, 463–477 (1924).Google Scholar
Daw, M.S. and Baskes, M.I.: Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals. Phys. Rev. B, 29, 6443–6453 (1984).CrossRefGoogle Scholar
Stukowski, A.: Visualization and analysis of atomistic simulation data with OVITO—The open visualization tool. Modell. Simul. Mater. Sci. Eng. 18, 015012 (2010).CrossRefGoogle Scholar
Li, J.: AtomEye: An efficient atomistic configuration viewer. Modell. Simul. Mater. Sci. Eng. 11, 173 (2003).CrossRefGoogle Scholar
Humphrey, W., Dalke, A., and Schulten, K.: VMD: Visual molecular dynamics. J. Mol. Graphics 14, 33–38 (1996).CrossRefGoogle ScholarPubMed
Begau, C., Hartmaier, A., George, E.P., and Pharr, G.M.: Atomistic processes of dislocation generation and plastic deformation during nanoindentation. Acta Mater. 59, 934–942 (2011).CrossRefGoogle Scholar
Begau, C., Hua, J., and Hartmaier, A.: A novel approach to study dislocation density tensors and lattice rotation patterns in atomistic simulations. J. Mech. Phys. Solids 60, 711–722 (2012).CrossRefGoogle Scholar
Schroeder, W., Martin, K., and Lorensen, B.: Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, 4th ed. (Kitware, Clifton Park, New York, 2006).Google Scholar
Gropp, W., Hoefler, T., Thakur, R., and Lusk, E.: Using Advanced MPI: Modern Features of the Message-Passing Interface, 1st ed. (The MIT Press, Cambridge, Massachusetts, 2014).Google Scholar
Plimpton, S.: Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).CrossRefGoogle Scholar
Tadmor, E.B., Ortiz, M., and Phillips, R.: Quasicontinuum analysis of defects in solids. Philos. Mag. A 73, 1529–1563 (1996).CrossRefGoogle Scholar
Pearce, O., Gamblin, T., de Supinski, B.R., Arsenlis, T., and Amato, N.M.: Load balancing N-body simulations with highly non-uniform density. In Proceedings of the 28th ACM International Conference on Supercomputing, ICS’14 (ACM, New York, NY, 2014); pp. 113–122.Google Scholar
Pavia, F. and Curtin, W.A.: Parallel algorithm for multiscale atomistic/continuum simulations using LAMMPS. Modell. Simul. Mater. Sci. Eng. 23, 055002 (2015).CrossRefGoogle Scholar
Biyikli, E. and To, A.C.: Multiresolution molecular mechanics: Implementation and efficiency. J. Comput. Phys. 328, 27–45 (2017).CrossRefGoogle Scholar
Hunter, A., Saied, F., Le, C., and Koslowski, M.: Large-scale 3D phase field dislocation dynamics simulations on high-performance architectures. Int. J. High Perform. Comput. Appl. 25, 223–235 (2011).CrossRefGoogle Scholar
Towns, J., Cockerill, T., Dahan, M., Foster, I., Gaither, K., Grimshaw, A., Hazlewood, V., Lathrop, S., Lifka, D., Peterson, G.D., Roskies, R., Scott, J.R., and Wilkins-Diehr, N.: XSEDE: Accelerating scientific discovery. Comput. Sci. Eng. 16, 62–74 (2014).CrossRefGoogle Scholar
Mishin, Y., Farkas, D., Mehl, M.J., and Papaconstantopoulos, D.A.: Interatomic potentials for monoatomic metals from experimental data and ab initio calculations. Phys. Rev. B 59, 3393–3407 (1999).CrossRefGoogle Scholar
Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In Proceedings of the April 18–20, 1967, Spring Joint Computer Conference, AFIPS’67 (ACM, Spring, New York, NY, 1967); pp. 483–485.Google Scholar
Chen, Y. and Lee, J.D.: Connecting molecular dynamics to micromorphic theory. II. Balance laws. Phys. A 322, 377–392 (2003).CrossRefGoogle Scholar
Chen, Y. and Lee, J.D.: Connecting molecular dynamics to micromorphic theory. I. Instantaneous and averaged mechanical variables. Phys. A 322, 359–376 (2003).CrossRefGoogle Scholar
Xiong, L., Chen, X., Zhang, N., McDowell, D.L., and Chen, Y.: Prediction of phonon properties of 1D polyatomic systems using concurrent atomistic-continuum simulation. Arch. Appl. Mech. 84, 1665–1675 (2014).CrossRefGoogle Scholar
Kalidindi, S.R., Brough, D.B., Li, S., Cecen, A., Blekh, A.L., Congo, F.Y.P., and Campbell, C.: Role of materials data science and informatics in accelerated materials innovation. MRS Bull. 41, 596–602 (2016).CrossRefGoogle Scholar
H. Chen, S. Xu, W. Li, J. Rigelesaiyin, T. Phan, and L. Xiong: A spatial decomposition parallel algorithm for a concurrent atomistic-continuum simulator and its preliminary applications, Comput. Mater. Sci. 144, 1–10 (2018).CrossRefGoogle Scholar
Supplementary material: PDF

Xu et al. supplementary material

Figures S1-S2

Download Xu et al. supplementary material(PDF)
PDF 1.4 MB