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MM: Fachverband Metall- und Materialphysik

MM 17: Poster Ib

MM 17.5: Poster

Montag, 18. März 2024, 18:30–20:30, Poster F

Investigating phonons in superconducting Lanthanum Hydride using ab initio methods accelerated by machine learning potentials. — •Abhishek Raghav¹, Kousuke Nakano², and Michele Casula¹ — ¹Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC), Sorbonne Université, Paris, France — ²Center for Basic Research on Materials, National Institute for Materials Science (NIMS), Tsukuba, Japan

Hydrogen rich materials with calthrate structures are an important class of superconducting materials. Lanthanum hydride (LaH₁₀) is one such material, demonstrated to show superconductivity at 250 K and 170 GPa.

Phonon spectrum and electron-phonon coupling are important ingredients used to predict superconductivity, being of BCS type. However, computing accurate phonons for hydrogen calthrate materials requires including anharmonicity due to nuclear quantum effects. In this work, we use the path integral molecular dynamics (PIMD) formalism to compute accurate anharmonic phonons. It is observed that, phonons for LaH₁₀ (Fm3m phase), as predicted by PIMD are dynamically stable over the experimentally relevant pressure range, in contrast with the harmonic phonons. We also use the energies and forces computed during PIMD to train a machine learning potential (MLP) for LaH₁₀ using operator quantum machine learning. This MLP is then used to drive simulations with larger supercells, to compute phonons efficiently and accurately.

Keywords: Lanthanum hydride; Superconductivity; Phonons; Molecular dynamics; Machine learning