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SUMMARY:Computational Strategies for Modelling Defects in Semiconductors -
  Dr Seán Kavanaugh\, University of Cambridge
DTSTART:20260126T143000Z
DTEND:20260126T150000Z
UID:TALK243295@talks.cam.ac.uk
CONTACT:Dr Fabian Berger
DESCRIPTION:First-principles simulations of atomic and electronic structur
 e in solids offer a powerful route to predict and understand material prop
 erties.[1] This is particularly relevant in the case of point defects whic
 h dramatically affect material properties yet present many challenges for 
 experimental characterisation.\n \nRecent years have seen significant adva
 nces in both computational methodologies[1\,2] and associated toolkits[3
 –5] for modelling defect behaviour. I will briefly discuss my collaborat
 ive efforts in this area\, including approaches for modelling defect metas
 tabilities and their effects on electron-hole recombination\,[6–8] the d
 evelopment of the open-source doped defect simulation package\,[3] and rem
 aining challenges in this area.[9] Lastly\, I will discuss recent work on 
 extending these approaches using foundational machine-learning models\, de
 monstrating exciting potential for these methods in the field of defect mo
 delling\, but with important caveats regarding their accuracy and reliabil
 ity at present.[10\,11]\n \n1.	Arrigoni\, M. & Madsen\, G. K. H. Evolution
 ary computing and machine learning for discovering of low-energy defect co
 nfigurations. npj Comput Mater 7\, 1–13 (2021).\n\n2.	Alkauskas\, A.\, Y
 an\, Q. & Van de Walle\, C. G. First-principles theory of nonradiative car
 rier capture via multiphonon emission. Phys. Rev. B 90\, 075202 (2014).\n\
 n3.	Kavanagh\, S. R. et al. doped: Python toolkit for robust and repeatabl
 e charged defect supercell calculations. Journal of Open Source Software 9
 \, 6433 (2024).\n\n4.	Huang\, M. et al. DASP: Defect and Dopant ab-initio 
 Simulation Package. J. Semicond. 43\, 042101 (2022).\n\n5.	Mosquera-Lois\,
  I.\, Kavanagh\, S. R.\, Walsh\, A. & Scanlon\, D. O. ShakeNBreak: Navigat
 ing the defect configurational landscape. Journal of Open Source Software 
 7\, 4817 (2022).\n\n6.	Kavanagh\, S. R.\, Walsh\, A. & Scanlon\, D. O. Rap
 id Recombination by Cadmium Vacancies in CdTe. ACS Energy Lett. 6\, 1392
 –1398 (2021).\n\n7.	Kavanagh\, S. R.\, Scanlon\, D. O.\, Walsh\, A. & Fr
 eysoldt\, C. Impact of metastable defect structures on carrier recombinati
 on in solar cells. Faraday Discuss. 239\, 339–356 (2022).\n\n8.	Wang\, X
 .\, Kavanagh\, S. R.\, Scanlon\, D. O. & Walsh\, A. Upper efficiency limit
  of Sb 2 Se 3 solar cells. Joule 8\, 2105–2122 (2024).\n\n9.	Squires\, A
 . G.\, Kavanagh\, S. R.\, Walsh\, A. & Scanlon\, D. O. Guidelines for robu
 st and reproducible point defect simulations in crystals. Nat Rev Mater 1
 –18 (2026) doi:10.1038/s41578-025-00879-y.\n\n10.	Mosquera-Lois\, I.\, K
 avanagh\, S. R.\, Ganose\, A. M. & Walsh\, A. Machine-learning structural 
 reconstructions for accelerated point defect calculations. npj Comput Mate
 r 10\, 1–9 (2024).\n\n11.	Kavanagh\, S. R. Identifying split vacancy def
 ects with machine-learned foundation models and electrostatics. J. Phys. E
 nergy 7\, 045002 (2025).\n\n
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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