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HL: Fachverband Halbleiterphysik

HL 39: Poster III

HL 39.25: Poster

Mittwoch, 19. März 2025, 15:00–18:00, P3

A machine learning potential for tellurium — •Andrea Corradini, Giovanni Marini, and Matteo Calandra — Department of Physics, University of Trento, Via Sommarive 14, 38123 Povo, Italy

Elemental tellurium has drawn attention in recent years, due to its possible technological application as switching device in phase change memories [1]. Recent computational studies are addressing the behaviour of elemental Te under operating conditions with a focus on the crystallization dynamics [2]. In addition, experiments have found anomalous thermodynamic maxima in undercooled liquid Te around 615 K, i.e. 130 K below the melting point [3]. Thermodynamic maxima behave in a very similar way as those in undercooled liquid water. Hence the question whether elemental Te shows a liquid-liquid phase transition, analogously to what is claimed for water. In this work, we develop a robust machine learning potential to study elemental Te and try to answer this question.

Funded by the European Union (ERC, DELIGHT, 101052708). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

[1] Shen et al., Science 374, 1390*1394 (2021)

[2] Zhou et al., arXiv:2409.03860 [cond-mat.mtrl-sci]

[3] Sun et al., PNAS 119 (28), e2202044119 (2022)

Keywords: Machine learning; Undercooled liquids; Tellurium; Liquid-liquid phase transitions; Molecular dynamics