Dresden 2026 – scientific programme

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DS: Fachverband Dünne Schichten

DS 17: Focus Session: High-Temperature Superconductivity in Hydride Materials at High Pressures (joint session TT/DS)

DS 17.1: Topical Talk

Thursday, March 12, 2026, 15:00–15:30, HSZ/0003

Computational searches for conventional high temperature superconductivity — •Chris Pickard — University of Cambridge

First principles methods for the prediction of the structure of materials have delivered a powerful tool for generating candidate structures for comparison with experimental analytical methods. Early studies focused on the exotic properties and structures of relatively simple systems, typically the elements and binary compounds. The promise of discovering materials with extreme properties relies on the ability of screen a wide variety of compounds.[1] I will reflect on why ab initio random structure searching (AIRSS) is particularly suited to these challenges, focussing on the dramatic acceleration that ephemeral data derived potentials (EDDPs) afford,[2] and their role in the uncovering of Mg₂IrH₆ as a feasible ambient pressure high temperature superconductor.[3]
[1] A.M.Shipley, M.J Hutcheon, R.J.Needs, Ch.J.Pickard, Phys. Rev. B 104, 054501 (2021)
[2] Ch.J.Pickard, Phys. Rev. B 106, 014102 (2022)
[3] K.Dolui, L.J.Conway, Ch.Heil, T.A.Strobel, R.Prasankumar, Ch.J.Pickard, Phys. Rev. Lett. 132, 166001 (2024)

Keywords: First principles; Computation; Superconductivity; Machine learning