Applications of High-Performance Computing (HPC) in Materials Simulation
Abstract:
High-Performance Computing (HPC) has become a fundamental pillar of modern computational materials science, enabling the simulation and analysis of increasingly complex material systems with unprecedented accuracy and efficiency. The rapid advancement of HPC architectures, including petascale and exascale computing platforms, has significantly expanded the capabilities of materials modeling and simulation. This review provides a comprehensive overview of the role of HPC in materials research, covering its applications in first-principles calculations based on Density Functional Theory (DFT), molecular dynamics (MD) simulations, high-throughput materials discovery, and the investigation of nanomaterials and two-dimensional materials. The integration of HPC with advanced computational techniques has facilitated the exploration of electronic, magnetic, optical, mechanical, and thermodynamic properties of materials across multiple length and time scales. In addition, the review discusses the growing synergy between HPC and Artificial Intelligence (AI), highlighting the emergence of Materials Informatics as a powerful paradigm for data-driven materials design and accelerated materials discovery. Major materials simulation software packages optimized for HPC environments, including VASP, Quantum ESPRESSO, CASTEP, ABINIT, SIESTA, LAMMPS, GROMACS, CP2K, and nanoDCAL, are also examined. Furthermore, current challenges related to computational scalability, energy consumption, and infrastructure costs are analyzed, together with future perspectives involving exascale computing, next-generation GPU architectures, machine learning, and quantum computing. Overall, HPC continues to play a pivotal role in advancing materials research and is expected to remain an indispensable tool for the discovery, design, and optimization of advanced materials for future technological applications.
KeyWords:
High-Performance Computing; Density Functional Theory; Molecular Dynamics; Materials Discovery; Computational Materials Science
References:
- Curtarolo, S., Setyawan, W., Hart, G. L. W., Jahnatek, M., Chepulskii, R. V., Taylor, R. H., Wang, S., Xue, J., Yang, K., Levy, O., Mehl, M. J., Stokes, H. T., Demchenko, D. O., & Morgan, D. (2013). AFLOW: An automatic framework for high-throughput materials discovery. Computational Materials Science, 58, 218–226. DOI: 10.1016/j.commatsci.2012.02.005
- Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002. DOI: 10.1063/1.4812323
- Keal, T. W., Elena, A. M., Sokol, A. A., Stoneham, K., Probert, M. I. J., Cucinotta, C. S., Willock, D. J., Logsdail, A. J., Zen, A., Hasnip, P. J., Bush, I. J., Watkins, M., Alfè, D., Skylaris, C.-K., Curchod, B.
- F. E., Cai, Q., & Woodley, S. M. (2022). Materials and Molecular Modelling at the Exascale. Computing in Science & Engineering, 24(3), 16–31. DOI: 10.1109/MCSE.2022.3141328.
- Chang, C., Kaxiras, E., Schleife, A., & Marzari, N. (2023). Simulations in the era of exascale computing. Nature Reviews Materials, 8, 309–310. DOI: 10.1038/s41578-023-00546-0.
- Chen, C., Nguyen, D. T., Lee, S. J., Baker, N. A., Karakoti, A. S., Lauw, L., Owen, C., Mueller, K. T., Bilodeau, B. A., Murugesan, V., & Troyer, M. (2024). Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation. DOI: 10.48550/arXiv.2401.04070.
- Dongarra, J., Beckman, P., Moore, T., Aerts, P., Aloisio, G., Andre, J. C., Barkai, D., Berzins, M., Boku, T., Braunschweig, B., et al. (2021). The International Exascale Software Project Roadmap. International Journal of High Performance Computing Applications, 25(1), 3–60. DOI: 10.1177/1094342010391989
- Hager, G., Wellein, G., Habich, J., & Zeiser, T. (2021). Performance engineering for CPU and GPU architectures in high-performance computing. Computing and Visualization in Science, 24, 1–15. DOI: 10.1007/s00791-021-00360-8
- Kurzak, J., Dongarra, J., & Luszczek, P. (2022). The dawn of the exascale era in high-performance computing. Computing in Science & Engineering, 24(3), 7–15. DOI: 10.1109/MCSE.2022.3142840
- Hohenberg, P., & Kohn, W. (1964). Inhomogeneous Electron Gas. Physical Review, 136(3B), B864– B871. DOI: 10.1103/PhysRev.136.B864
- Kohn, W., & Sham, L. J. (1965). Self-Consistent Equations Including Exchange and Correlation Effects. Physical Review, 140(4A), A1133–A1138. DOI: 10.1103/PhysRev.140.A1133
- Kresse, G., & Furthmüller, J. (1996). Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Physical Review B, 54(16), 11169–11186. DOI: 10.1103/PhysRevB.54.11169
- Kresse, G., & Furthmüller, J. (1996). Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Computational Materials Science, 6(1), 15–50. DOI: 10.1016/0927-0256(96)00008-0
- Giannozzi, P., Baroni, S., Bonini, N., Calandra, M., Car, R., Cavazzoni, C., Cococcioni, M., Dabo, I., Dal Corso, A., de Gironcoli, S., et al. (2009). QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. Journal of Physics: Condensed Matter, 21(39), 395502. DOI: 10.1088/0953-8984/21/39/395502
- Frenkel, D., & Smit, B. (2002). Understanding Molecular Simulation: From Algorithms to Applications (2nd ed.). Academic Press. ISBN: 9780122673511
- Plimpton, S. (1995). Fast Parallel Algorithms for Short-Range Molecular Dynamics. Journal of Computational Physics, 117(1), 1–19. DOI: 10.1006/jcph.1995.1039
- Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1–2, 19–25. DOI: 10.1016/j.softx.2015.06.001
- Allen, M. P., & Tildesley, D. J. (2017). Computer Simulation of Liquids (2nd ed.). Oxford University Press. ISBN: 9780198803195
- Karplus, M., & McCammon, J. A. (2002). Molecular dynamics simulations of biomolecules. Nature Structural Biology, 9(9), 646–652. DOI: 10.1038/nsb0902-646
- Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002. DOI: 10.1063/1.4812323
- Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013). Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD). JOM, 65, 1501–1509. DOI: 10.1007/s11837-013-0755-4
- Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R. H., Nelson, L. J., Hart, G. L. W., Sanvito, S., Buongiorno-Nardelli, M., & Mingo, N. (2012). AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations. Computational Materials Science, 58, 227–235. DOI: 10.1016/j.commatsci.2012.02.005
- Hautier, G., Fischer, C., Ehrlacher, V., Jain, A., & Ceder, G. (2010). Data mined ionic substitutions for the discovery of new compounds. Inorganic Chemistry, 50(2), 656–663. DOI: 10.1021/ic102031h
- Novoselov, K. S., Geim, A. K., Morozov, S. V., Jiang, D., Zhang, Y., Dubonos, S. V., Grigorieva, I. V., & Firsov, A. A. (2004). Electric Field Effect in Atomically Thin Carbon Films. Science, 306(5696), 666–669. DOI: 10.1126/science.1102896
- Castro Neto, A. H., Guinea, F., Peres, N. M. R., Novoselov, K. S., & Geim, A. K. (2009). The electronic properties of graphene. Reviews of Modern Physics, 81(1), 109–162. DOI: 10.1103/RevModPhys.81.109
- Xu, M., Liang, T., Shi, M., & Chen, H. (2013). Graphene-like two-dimensional materials. Chemical Reviews, 113(5), 3766–3798. DOI: 10.1021/cr300263a
- Balendhran, S., Walia, S., Nili, H., Sriram, S., & Bhaskaran, M. (2015). Elemental analogues of graphene: silicene, germanene, stanene, and phosphorene. Small, 11(6), 640–652. DOI: 10.1002/smll.201402041
- Naguib, M., Kurtoglu, M., Presser, V., Lu, J., Niu, J., Heon, M., Hultman, L., Gogotsi, Y., & Barsoum, M. W. (2011). Two-Dimensional Nanocrystals Produced by Exfoliation of Ti₃AlC₂. Advanced Materials, 23(37), 4248–4253. DOI: 10.1002/adma.201102306
- Anasori, B., Lukatskaya, M. R., & Gogotsi, Y. (2017). 2D metal carbides and nitrides (MXenes) for energy storage. Nature Reviews Materials, 2, 16098. DOI: 10.1038/natrevmats.2016.98
- Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559, 547–555. DOI: 10.1038/s41586-018-0337-2
- Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5, 83. DOI: 10.1038/s41524-019-0221-0
- Agrawal, A., & Choudhary, A. (2019). Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Materials, 4(5), 053208. DOI: 10.1063/1.4946894
- Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002. DOI: 10.1063/1.4812323
- Draxl, C., & Scheffler, M. (2019). NOMAD: The FAIR concept for big data-driven materials science. MRS Bulletin, 43(9), 676–682. DOI: 10.1557/mrs.2018.208
- Kresse, G., & Joubert, D. (1999). From ultrasoft pseudopotentials to the projector augmented-wave method. Physical Review B, 59(3), 1758–1775. DOI: 10.1103/PhysRevB.59.1758
- Giannozzi, P., Baroni, S., Bonini, N., et al. (2009). QUANTUM ESPRESSO: a modular and opensource software project for quantum simulations of materials. Journal of Physics: Condensed Matter, 21, 395502. DOI: 10.1088/0953-8984/21/39/395502
- Giannozzi, P., Andreussi, O., Brumme, T., et al. (2017). Advanced capabilities for materials modelling with Quantum ESPRESSO. Journal of Physics: Condensed Matter, 29, 465901. DOI: 10.1088/1361-648X/aa8f79
- Clark, S. J., Segall, M. D., Pickard, C. J., et al. (2005). First principles methods using CASTEP. Zeitschrift für Kristallographie, 220, 567–570. DOI: 10.1524/zkri.220.5.567.65075
- Gonze, X., Jollet, F., Abreu Araujo, F., et al. (2020). The ABINIT project: Impact, environment and recent developments. Computer Physics Communications, 248, 107042. DOI: 10.1016/j.cpc.2019.107042
- Soler, J. M., Artacho, E., Gale, J. D., et al. (2002). The SIESTA method for ab initio order-N materials simulation. Journal of Physics: Condensed Matter, 14, 2745–2779. DOI: 10.1088/09538984/14/11/302
- Plimpton, S. (1995). Fast Parallel Algorithms for Short-Range Molecular Dynamics. Journal of Computational Physics, 117(1), 1–19. DOI: 10.1006/jcph.1995.1039
- Abraham, M. J., Murtola, T., Schulz, R., et al. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1–2, 19–25. DOI: 10.1016/j.softx.2015.06.001
- Kühne, T. D., Iannuzzi, M., Del Ben, M., et al. (2020). CP2K: An electronic structure and molecular dynamics software package. The Journal of Chemical Physics, 152, 194103. DOI: 10.1063/5.0007045
- Taylor, J., Guo, H., & Wang, J. (2001). Ab initio modeling of quantum transport properties of molecular electronic devices. Physical Review B, 63, 245407. DOI: 10.1103/PhysRevB.63.245407
- Wang, J., Wang, B., & Guo, H. (2008). Quantum transport calculations based on density functional theory. Frontiers of Physics in China, 3, 267–279. DOI: 10.1007/s11467-008-0031-9
- Dongarra, J., Beckman, P., Moore, T., et al. (2021). The International Exascale Software Project Roadmap. International Journal of High Performance Computing Applications, 25(1), 3–60. DOI: 10.1177/1094342010391989
- Shalf, J., Dosanjh, S., & Morrison, J. (2020). Exascale Computing Technology Challenges. International Conference on High Performance Computing. DOI: 10.1007/978-3-030-50743-5_1
- Marzari, N., Schleife, A., Kaxiras, E., & Chang, C. (2023). Simulations in the era of exascale computing. Nature Reviews Materials, 8, 309–310. DOI: 10.1038/s41578-023-00546-0
- Bauer, B., Bravyi, S., Motta, M., & Chan, G. K.-L. (2020). Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. Chemical Reviews, 120(22), 12685–12717. DOI: 10.1021/acs.chemrev.9b00829