Skip to main content
SpringerLink
Log in

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials

  • Conference paper
  • First Online:
Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation (SMC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1690))

Included in the following conference series:

Abstract

The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system’s Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models have been developed and implemented on high-performance computing (HPC) architectures. In this paper, we describe two ML methods (linear mixing model and HydraGNN) as surrogates for first principles density functional theory (DFT) calculations with classical MC simulations. These two surrogate models are used to learn the dependence of target physical properties from complex compositions and interactions of their constituents. We present the predictive performance of these two surrogate models with respect to their complexity while avoiding the danger of overfitting the model. An important aspect of our approach is the periodic retraining with newly generated first principles data based on the progressive exploration of the system’s phase space by the MC simulation. The numerical results show that HydraGNN model attains superior predictive performance compared to the linear mixing model for magnetic alloy materials.

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. PyTorch. https://pytorch.org/docs/stable/index.html

  2. PyTorch Geometric. https://pytorch-geometric.readthedocs.io/en/latest/

  3. Conduit, B., et al.: Probabilistic neural network identification of an alloy for direct laser deposition. Mater. Des. 168, 107644 (2019)

    Google Scholar 

  4. Corso, G., Cavalleri, L., Beaini, D., Liò, P., Veličković, P.: Principal neighbourhood aggregation for graph nets. arXiv:2004.05718 [cs, stat] (December 2020). http://arxiv.org/abs/2004.05718, arXiv: 2004.05718

  5. Curtarolo, S., et al.: AFLOW: an automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 58, 218–226 (2012)

    Google Scholar 

  6. Dai, M., Demirel, M.F., Liang, Y., Hu, J.M.: Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials. NPJ Computational Mater.7(103) (2021). https://doi.org/10.1038/s41524-021-00574-w, https://www.nature.com/articles/s41524-021-00574-w#citeas

  7. Bacon, G.E., Crangle, J.: Chemical and magnetic order in platinum-rich pt + fe alloys. Proc. R. Soc. Lond. A 272, 387–405 (1963). https://doi.org/10.1098/rspa.1963.0060

  8. Eisenbach, M., Li, Y.W., Odbadrakh, O.K., Pei, Z., Stocks, G.M., Yin, J.: LSMS. https://github.com/mstsuite/lsms, https://www.osti.gov/biblio/1420087

  9. Eisenbach, M., Larkin, J., Lutjens, J., Rennich, S., Rogers, J.H.: GPU acceleration of the locally selfconsistent multiple scattering code for first principles calculation of the ground state and statistical physics of materials. Comput. Phys. Commun. 211, 2–7 (2017). https://doi.org/10.1016/j.cpc.2016.07.013

    Article  MATH  Google Scholar 

  10. Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)

    Google Scholar 

  11. Fuhr, A.S., Sumpter, B.G.: Deep generative models for materials discovery and machine learning-accelerated innovation. Front. Mater. 9 (2022). https://doi.org/10.3389/fmats.2022.865270, https://www.frontiersin.org/articles/10.3389/fmats.2022.865270/full

  12. Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)

    Google Scholar 

  13. Higashiyama, Y., Tsunoda, Y.: Magnetism of pt0.67fe0.33 alloy. J. Phys. Soc. Jpn. 72(12), 3305–3306 (2003). https://doi.org/10.1143/JPSJ.72.3305

  14. Jain, A., et al.: Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1(1), 11 (2013). https://doi.org/10.1063/1.4812323

    Article  Google Scholar 

  15. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 [cs] (January 2017). http://arxiv.org/abs/1412.6980, arXiv: 1412.6980

  16. Lavrentiev, M.Y., Drautz, R., Nguyen-Manh, D., Klaver, T.P.C., Dudarev, S.: Monte Carlo study of thermodynamic properties and clustering in the BCC Fe-Cr system. Phys. Rev. B 75(014208) (2007). https://doi.org/10.1103/PhysRevB.75.014208

  17. Liu, X., Zhang, J., Yin, J., Bi, S., Eisenbach, M., Wang, Y.: Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach. Comput. Mater. Sci. 187, 110135 (2021). https://doi.org/10.1016/j.commatsci.2020.110135

  18. Louis, S.Y., et al.: Graph convolutional neural networks with global attention for improved materials property prediction. Phys. Chem. Chem. Phys. 22, 18141–18148 (2020)

    Google Scholar 

  19. Lupo Pasini, M., Burĉul, M., Reeve, S.T., Eisenbach, M., Perotto, S.: Fast and accurate predictions of total energy for solid solution alloys with graph convolutional neural networks. In: Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation. SMC 2021. Communications in Computer and Information Science, vol. 1512. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96498-6_5

  20. Lupo Pasini, M., Eisenbach, M.: FePt binary alloy with 32 atoms - LSMS-3 data, February 2001. https://www.osti.gov/dataexplorer/biblio/dataset/1762742, https://doi.org/10.13139/OLCF/1762742

  21. Lupo Pasini, M., Li, Y.W., Yin, J., Zhang, J., Barros, K., Eisenbach, M.: Fast and stable deep-learning predictions of material properties for solid solution alloys. J. Phys. Condens. Matter 33(8), 084005. IOP Publishing (2020). https://doi.org/10.1088/1361-648X/abcb10

  22. Lupo Pasini, M., Zhang, P., Reeve, S.T., Choi, J.Y.: Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems. Mach. Learn. Sci. Technol. 3(2), 025007 (2022). https://doi.org/10.1088/2632-2153/ac6a51

  23. Lupo Pasini, M., Reeve, S.T., Zhang, P., Choi, J.Y.: HydraGNN. [Computer Software] (2021). https://doi.org/10.11578/dc.20211019.2, https://github.com/ORNL/HydraGNN

  24. Lépinoux, J., Sigli, C.: Precipitate growth in concentrated binary alloys: a comparison between kinetic monte carlo simulations, cluster dynamics and the classical theory. Philos. Mag. 93(23), 3194–3215 (2013)

    Google Scholar 

  25. Mohammadi, H., Eivani, A.R., Seyedein, S.H., Ghosh, M.: Modified monte carlo approach for simulation of grain growth and ostwald ripening in two-phase zn-22al alloy. J. Mater. Res. Technol. 9(5), 9620–9631 (2020)

    Google Scholar 

  26. Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024–8035. Curran Associates, Inc. (2019). https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf

  27. Reitz, D.M., Blaisten-Barojas, E.: Simulating the nak eutectic alloy with monte carlo and machine learning. Sci. Rep. 9(704) (2019). https://doi.org/10.1038/s41598-018-36574-y

  28. Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., Wolverton, C.: Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database (OQMD). JOM 65(11), 1501–1509 (2013)

    Google Scholar 

  29. Slater, J.C.: The ferromagnetism of nickel. Phys. Rev. 49, 537–545 (1936). https://doi.org/10.1103/PhysRev.49.537

    Article  Google Scholar 

  30. Tétot, R., Finel, A.: Relaxed Monte Carlo Simulations on Au-Ni Alloy, pp. 179–184. Springer, US, Boston, MA (1996). https://doi.org/10.1007/978-1-4613-0385-5_8

  31. Tobita, N., et al.: Antiferromagnetic phase transition in ordered fept3 investigated by angle-resolved photoemission spectroscopy. J. Phys. Soc. Jpn. 79(2), 024703 (2010). https://doi.org/10.1143/JPSJ.79.024703

    Article  Google Scholar 

  32. Vlaic, P., Burzo, E.: Magnetic behaviour of iron-platinum alloys. J. Optoelectron. Adv. Mater. 12, 1114–1124 (2010)

    Google Scholar 

  33. van de Walle, A., Asta, M.: Self-driven lattice-model monte carlo simulations of alloy thermodynamic properties and phase diagrams. Model. Simul. Mater. Sci. Eng. 10, 521–538 (2002)

    Article  Google Scholar 

  34. Wang, Y., Stocks, G.M., Shelton, W.A., Nicholson, D.M.C., Temmerman, W.M., Szotek, Z.: Order-N multiple scattering approach to electronic structure calculations. Phys. Rev. Lett. 75, 2867 (1995). https://doi.org/10.1103/PhysRevLett.75.2867

    Article  Google Scholar 

  35. Xie, T., Grossman, J.C.: Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120(14), 145301 (2018)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Office of Science of the Department of Energy and by the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility and of the Edge Computing program at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Author information

Authors and Affiliations

  1. National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA

    Markus Eisenbach, Mariia Karabin & Junqi Yin

  2. Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA

    Massimiliano Lupo Pasini

Authors
  1. Markus Eisenbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariia Karabin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Massimiliano Lupo Pasini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junqi Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Markus Eisenbach .

Editor information

Editors and Affiliations

  1. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Kothe Doug

  2. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Geist Al

  3. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Swaroop Pophale

  4. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Hong Liu

  5. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Suzanne Parete-Koon

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Eisenbach, M., Karabin, M., Lupo Pasini, M., Yin, J. (2022). Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials. In: Doug, K., Al, G., Pophale, S., Liu, H., Parete-Koon, S. (eds) Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation. SMC 2022. Communications in Computer and Information Science, vol 1690. Springer, Cham. https://doi.org/10.1007/978-3-031-23606-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23606-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23605-1

  • Online ISBN: 978-3-031-23606-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation