Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf


Physics-informed and data-driven modeling of matter under extreme conditions

Cangi, A.; Fiedler, L.; Shah, K.; Callow, T. J.; Ramakrishna, K.; Kotik, D.; Schmerler, S.

Understanding the properties of matter under extreme conditions is essential for advancing our fundamental understanding of astrophysical objects and guides the search for exoplanets, it propels the discovery of materials exhibiting novel properties that emerge under high temperatures and pressure, it enables novel technologies such as nuclear fusion, and supports diagnostics of experiments at large-scale brilliant photon sources. While modeling in this challenging research domain has so far relied on first-principles methods [1,2], these turn out to be computationally too expensive for simulations at the required time and length scales. Reduced models, such as average-atom models [3], come at a reduced computational and are useful by connecting atomistic details with hydrodynamics simulations, but they provide less accuracy. Artificial intelligence (AI) has great potential for accelerating electronic structure calculations to hitherto unattainable scales [4]. I will present our recent efforts on accomplishing speeding up Kohn-Sham density functional theory calculations with deep neural networks in terms of our Materials Learning Algorithms framework [5,6] by illustrating results for metals across their melting point. Furthermore, our results towards automated machine-learning save orders of magnitude in computational efforts for finding suitable neural networks and set the stage for large-scale AI-driven investigations [7].

[1] T. Dornheim, A. Cangi, K. Ramakrishna, M. Böhme, S. Tanaka, J. Vorberger, Phys. Rev. Lett. 125, 235001 (2020).
[2] K. Ramakrishna, A. Cangi, T. Dornheim, J. Vorberger, Phys. Rev. B 103, 125118 (2021).
[3] T. J. Callow, E. Kraisler, S. B. Hansen, A. Cangi, Phys. Rev. Research 4, 023055 (2022).
[4] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials 6, 040301 (2022).
[5] A. Cangi et al., MALA, https://doi.org/10.5281/zenodo.5557254 (2021).
[6] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, S. Rajamanickam, Phys. Rev. B 104, 035120 (2021).
o L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol. 3 045008 (2022).

Keywords: Materials science; Electronic structure theory; Density functional theory; Machine learning; Neural networks

  • Lecture (Conference)
    Big data analytical methods for complex systems, 06.-07.10.2022, Wroclaw, Poland

Permalink: https://www.hzdr.de/publications/Publ-35451
Publ.-Id: 35451