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SUMMARY:Fast Superconductivity Through Chemical Bonding Interactions. - Fr
 ancesco Belli - Unifersity of Buffalo
DTSTART:20260216T140000Z
DTEND:20260216T143000Z
UID:TALK244648@talks.cam.ac.uk
CONTACT:130300
DESCRIPTION:The need for rapid estimates for the superconducting onset gro
 ws exponentially with the increasing capabilities of first-principles and 
 artificial intelligence methods to explore ever-larger numbers of candidat
 e materials. In this context\, chemical bonding-oriented approaches have e
 merged as a promising proxy for predicting the value of the superconductin
 g critical temperature (Tc) at a substantially reduced computational cost.
  The concept has so far been developed and restricted to hydride supercond
 uctors through the heuristic networking value method[1\,2\,3]. The latter 
 estimates the interactions responsible for conventional superconductivity 
 by quantifying the overlap between localized electron basins and bypassing
  the elevated computational cost of the phonon and electron-phonon propert
 ies necessary for the estimate of Tc.\nThis seminar reviews the foundation
  of this method and presents a formulation to consolidate its formal basis
 \, demonstrating that a reliable estimate of Tc is achievable from a real-
 space perspective. Here is also discussed how electron localization is an 
 essential aspect to achieve high Tc in conventional superconductors. An ex
 tension of this approach is also proposed to broaden the applicability of 
 the method beyond hydride to a wider range of conventional materials.\n[1]
  Strong correlation between electronic bonding network and critical temper
 ature in hydrogen-based superconductors.  Nat. Commun. 12\, 5381 (2021)\n[
 2] TcESTIME: predicting high-temperature hydrogen-based superconductors.  
 Chem. Sci. 2024 Nov 13\;16(1):57–68\n[3] Refining Tc Prediction in Hydri
 des via Symbolic‐Regression‐Enhanced Electron‐Localization‐Functio
 n‐Based Descriptors. Ann. Phys. (Berl.) 537 (11)\, e00280
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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