BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Pushing back the boundaries for the atomistic simulation of electr
 onic and ionic transport processes - Professor Jochen Blumberger\, UCL
DTSTART:20250319T143000Z
DTEND:20250319T153000Z
UID:TALK222367@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:The last decades have witnessed tremendous progress in Theoret
 ical and Computational Chemistry.\nDevelopment of ever more ingenious algo
 rithms have allowed us to solve some of the most fundamental equations\ngo
 verning chemical processes at steadily improved accuracy or for larger sys
 tems/longer time scales.\nAs a result\, Computational Chemistry has now pe
 netrated many disciplines of the Natural Sciences and\nEngineering includi
 ng Material and Energy Science\, Catalysis and Chemical Biology. In my tal
 k I will\nsurvey a number of recent studies where our group has contribute
 d to this endeavour. In the first part\nof my talk I will describe how we 
 pushed mixed quantum-classical non-adiabatic molecular dynamics from\nthe 
 molecular to the true nanoscale (10-100 nm) revealing a new transport mech
 anism\, transient quantum\ndelocalization\, of charge carriers[1\,2] and e
 xcitons[3] in organic photovoltaic and thermoelectric\n materials.[4] In t
 he second part of my talk I will describe a machine learning method that w
 e have recently\ndeveloped to simulate condensed phase systems interacting
  with an external electric field\, termed\nperturbed neural network potent
 ial molecular dynamics (PNNP-MD).[5] We find that PNNP-MD accurately\ndesc
 ribes the dielectric properties of liquid water\, specifically the field-i
 nduced relaxation dynamics\, the dielectric\nconstant\, the field-dependen
 t IR spectrum[5] and ionic conductivites[6] up to surprisingly high\nfield
  strengths of about 0.2 V/Angstrom with little loss in accuracy when compa
 red to ab-initio molecular dynamics.\nGoing forward\, we expect PNNP to gi
 ve vital atomistic insight into\nmyriad processes\, ranging from ionic con
 duction in electrolytes to field-directed catalysis to electrochemical\nen
 ergy conversion.\n\nReferences:\n\n[1] S. Giannini\, A. Carof\, M. Ellis\,
  H. Yang\, O. G. Ziogos\, S. Ghosh\, and J. Blumberger\,\n“Quantum local
 ization and delocalization of charge carriers in organic semiconducting cr
 ystals\,” Nat. Commun.\, vol. 10\, p. 3843\, 2019.\n\n[2]S. Giannini\, L
 . Di Virgilio\, M. Bardini\, J. Hausch\, J. Geuchies\, W. Zheng\, M. Volpi
 \, J. Elsner\, K. Broch\, Y. H.\nGeerts\, F. Schreiber\, G. Schweicher\, H
 . Wang\, J. Blumberger\, M. Bonn\, and D. and Beljonne\,\n“Transiently d
 elocalized states enhance hole mobility in organic molecular semiconductor
 s\,” Nat. Mater.\, vol. 22\, pp. 1361-1369\, 2023.\n\n[3] S. Giannini\, 
 W. -T. Peng\, L. Cupellini\, D. Padula\, A. Carof\, and J. Blumberger\,\n
 “Exciton transport in molecular organic semiconductors boosted by transi
 ent quantum delocalization\,” Nat. Commun.\, vol. 13\, p. 2755\, 2022.\n
 \n[4]  J. Elsner\, Y. Xu\, E. D. Goldberg\, F. Ivanovic\, A. Dines\, S. Gi
 annini\, H. Sirringhaus\, and J. Blumberger\,\n“Thermoelectric transport
  in molecular crystals driven by gradients of thermal electronic disorder\
 ,” Sci. Adv.\, vol. 10\, p. eadr1758\, 2024.\n\n[5] K. Joll\, P. Schienb
 ein\, K. M. Rosso\, and J. Blumberger\,\n“Machine learning the electric 
 field response of condensed phase systems using perturbed neural network p
 otentials\,” Nat. Commun.\, vol. 15\, p. 8192\, 2024.\n\n[6] K. Joll\, P
 . Schienbein\, K. M. Rosso\, and J. Blumberger\, in preparation\, 2025.
LOCATION:Unilever Lecture Theatre\, Yusuf Hamied Department of Chemistry
END:VEVENT
END:VCALENDAR