Berlin 2024 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

MM: Fachverband Metall- und Materialphysik

MM 4: Data Driven Material Science: Big Data and Workflows I

MM 4.3: Vortrag

Montag, 18. März 2024, 10:45–11:00, C 243

Pressure-transferable neural network models for density-functional theory — •Timothy Callow¹, Lenz Fiedler¹, Normand Modine², and Attila Cangi¹ — ¹Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf, Untermarkt 20, Görlitz, 02826, Saxony, Germany — ²Computational Materials and Data Science, Sandia National Laboratories, 1515 Eubank Blvd, Albuquerque, 87123, NM, USA

Density functional theory (DFT) is well-known as the workhorse of electronic structure calculations in materials science and quantum chemistry. However, its applications stretch beyond these traditionally-studied fields, such as to the warm-dense matter (WDM) regime. Under WDM conditions, there are different challenges to consider (compared to ambient conditions) when using DFT. Namely, the electronic structure problem must be solved (i) for large particle numbers, (ii) for a range of temperatures, and (iii) for a range of pressures. Promising solutions were demonstrated for problems (i) and (ii) [1,2] using a recently-developed workflow to machine-learn the local density of states (LDOS) [3]. In this talk, we discuss our progress in developing a solution for problem (iii). This problem presents additional challenges because the LDOS varies quite significantly with changes in the pressure, making it a difficult problem for neural network models.

[1] L Fiedler et al., npj Comput Mater 9, 115 (2023) [2] L Fiedler et al., Phys. Rev. B 108, 125146 (2023) [3] J. A. Ellis et al., Phys. Rev. B 104, 035120 (2021)

Keywords: Density-functional theory; Neural networks; Warm-dense matter