using highperformance computing and molecular dynamics to rationalise micelle structure from smallangle scattering
SAS2018
— 2018/10/12
DOI:
10.6084/m9.figshare.7150823
Andrew R. McCluskey
Uni Bath/Diamond Light Source
arm61@bath.ac.uk
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arm61.github.io/talks/sas20182
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micellisation is tough to simulate
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not a real potential energy surface
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difficult to get realistic micelles without
apriori
information
easy to underestimate micelle size;
10.1016/j.theochem.2009.09.054
get
N
_{agg}
from Guinier analysis;
10.1002/anie.201713303
run a few simulations at different
N
_{agg}
pick the best agreement;
10.1002/anie.201713303
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let's treat it like an optimisation problem
move the surfactants to optimise agreement with scattering profile
micelles are an ensemble structure, perfect for a population approach
population based optimisation algorithms are trivially parallelised
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what are we optimising?
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ctab
single tailed surfactant nearly has
C
_{∞v}
symmetry
can describe the system with 5 numbers per molecule
three positional, two angular
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particle swarm algorithm
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each particle has knowledge of the personal best (within that member of the population) position and the global best position
apparently good for high dimensionality problems
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shamelessly stolen from wikipedia
By
Ephramac

Own work
,
CC BYSA 4.0
,
Link
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fitoog
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fitoog
fitoog is an opensource C_MPI code for applying a particle swarm algorithm fitting SAS data to atomistic/coarsegrained molecular models
runs a short energy minimisation using MARTINI force field parameters at each stage
github.com/arm61/fitoog
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strong scaling
each node has 20 cores
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weak scaling
each node has 20 cores
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testing
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is it good?
use fitoog to fit some "test data"
fifty CTAB molecules in a box
background subtracted SANS data
ten independent calcs, running on 16 nodes, population size 10240, number of steps 4000
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Random numbers
acceptance probability 0.11 %
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Random numbers w/ EM
acceptance probability 0.18 %
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Particle swarm w/ EM
acceptance probability 0.26 %
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not very micellar
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conclusions
fitoog looks like a very efficient, pretty pointless code
we will test other algorithms, genetic
maybe try and optimise the particle swarm
can be used to produce test data for ML algorithms
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acknowledgements
James Grant
Karen J. Edler
Stephen C. Parker
Andrew J. Smith
Jonathan L. Rawle
SCARF cluster
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Sadie, my dog