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SUMMARY:Search for superconducting ternary hydrides at moderate and low pr
 essures with EDDP - Dr Maelie Causse\, University of Cambridge\, UK
DTSTART:20251124T143000Z
DTEND:20251124T150000Z
UID:TALK239758@talks.cam.ac.uk
CONTACT:Dr Fabian Berger
DESCRIPTION:Computational methods have revolutionised material discovery\,
  driving interest in designing new advanced materials\, e.g.\, superconduc
 tors\, which exhibit zero resistivity below a critical temperature (Tc). R
 eaching pressures in the 100 GPa range enables the synthesis of superhydri
 des\, a new class of hydrogen-rich materials that exhibit remarkable prope
 rties such as superconductivity\, hydrogen diffusion\, and hydrogen storag
 e. A striking example is LaH₁₀\, which holds the record for the highes
 t superconducting transition temperature at Tc = 250 K [1\,2]\, and showca
 ses high hydrogen diffusion at high temperatures [3]. However\, stabilisin
 g such compounds at ambient or low pressure remains a major challenge for 
 practical applications. This has driven the search beyond binary hydrides 
 toward ternary superhydrides\, aiming to discover new superconductors with
  high critical temperatures that can persist near ambient pressure.\n\nThe
  discovery of new superconducting hydrides traditionally relies on ab init
 io crystal structure prediction and experimental synthesis. However\, ab i
 nitio calculations become computationally prohibitive\, e.g. for assessing
  the thermodynamic stability of complex systems such as ternary superhydri
 des\, due to the extremely vast chemical space. To overcome this limitatio
 n\, our group has successfully developed tailored machine learning interat
 omic potentials – ephemeral data-driven potentials (EDDPs) – enabling 
 a great acceleration in structure prediction [4]. This approach was used t
 o build the convex-hull of a new predicted metastable ambient-pressure hyd
 rogen-based superconductor Mg2IrH6 with a Tc of 160 K [5]. \n\nThis semina
 r will trace the hunt for high-pressure superconductors\, highlight how ED
 DPs revolutionise our high-throughput search workflow\, and showcase our r
 ecent prediction of a new ternary hydride superconductor.\n\n\n[1] M. Soma
 yazulu et al.\, Phys. Rev. Lett.\, vol. 122 (2019) 027001\n[2] A.P. Drozdo
 v et al.\, Nature\, vol 569 (2019) 528-531\n[3] M. Caussé et al.\, Phy. R
 ev. B\, vol 107 (2023) L060301\n[4] C. J. Pickard\, Phys Rev B\, vol 106 (
 2022) 014102\n[5] K. Dolui et al.\, Phys. Rev. Lett.\, vol 132 (2024) 1660
 01\n
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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