BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Fast Kinetic Monte Carlo Methods for Novel Solar Cell Design
DTSTART;VALUE=DATE-TIME:20180710T070000Z
DTEND;VALUE=DATE-TIME:20180710T073000Z
DTSTAMP;VALUE=DATE-TIME:20200403T044412Z
UID:indico-contribution-1740@indico.math.cnrs.fr
DESCRIPTION:Speakers: Tan Trung NGUYEN (Department of Mathematical Science
s Bath University)\nThis is a joint work with A. Walker\, R. Scheichl and
C.A. Yates\, Bath University\n\nKinetic Monte Carlo (KMC) methods [1] are
widely used to simulate the surface adsorption\, diffusion\,\ngrowth\, sta
tistical physics\, radiation damage annealing\, bioglogical systems\, amon
gst other applications\nby evolving systems dynamically from state to stat
e. In our application to solar cells\, KMC are required\nto predict device
behaviour from the material properties at the microscopic scale. Our work
has mainly\nfocussed on atomistic studies of the microscopic processes\,
such as charge and exciton hopping\, recombination rates and light absorpt
ion. Using the parameters obtained from the atomistic simulations\, we foc
us on KMC simulations at the mesoscale\, computing current-voltage charact
eristics\, charge mobilities and parameters for calculating recombination
that subsequently feed into faster device design offered by continuum mode
ls where current-voltage characteristics are obtained.\n\nThe KMC method i
s equivalent to the Gillespie algorithm [2] which simulates the trajectori
es consistent\nwith the “exact” dynamical evolution of a system. The a
dvantage of KMC method is the probability that\nwe see a given sequence of
states and transition times is the same as the probability for seeing tha
t same trajectory in the molecular dynamics which is much more expensive s
ince one propagates equations of motion forward in time. However\, the com
putational cost is still very high in many practical applications such as
solar cell design\, bioglogical systems.\n\nIn order to accelerate atomist
ic simulations\, we apply the idea of multilevel Monte Carlo (MLMC)\nmetho
ds [3] to reduce the computational time significantly but still retain the
accuracy based on controlling statistical errors. The first results we ha
ve obtained with r-leaping and tau-leaping show the efficiency when skippi
ng expensive computations such as the update of the electrostatic potentia
l (the most expensive) and propensity functions in such a way that the sta
tistical errors are still acceptable (less than 10%).\nIn particular for s
olar cell designs\, this development will be coupled with fast and massive
ly parallel Poisson solvers for the modelling of long-range interactions.\
n\n[1] U. Neupane\, B. Bahrami\, M. Biesecker\, and Baroughi M.F. Kinetic
monte carlo modeling on organic\nsolar cells: Domain size\, donor-acceptor
ratio and thickness. Nano Energy\, 81:128–137\, 2017.\n\n[2] D.T. Gille
spi. Exact stochastic simulation of coupled chemical reactions. Journal of
Physical Chemistry\, 81(25)\, 1977.\n\n[3] C. Lester\, R.E. Baker\, M.B.
Giles\, and Yates C.A. Extending the multi-level method for the simulation
\nof stochastic biological systems. Bull Math Biol\, 2016\n\nhttps://indic
o.math.cnrs.fr/event/3023/contributions/1740/
LOCATION:Ho Chi Minh City University of Science
URL:https://indico.math.cnrs.fr/event/3023/contributions/1740/
END:VEVENT
END:VCALENDAR