Download Novel Machine Learning Approaches to Molecular Coherent Control

Novel Machine Learning Approaches to Molecular Coherent Control – Final Report
Summary of aims and objectives
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The stated objectives at the outset of this research were to:
• Develop the first UK capability in chemical quantum control
• Demonstrate applications of coherent control to chemical
problems
• Develop generic machine learning software for closed loop
control (applicable to non-chemical non-quantum applications)
• Develop machine-learning software for the interpretation and
generalization of quantum control across different chemical
problems
We have made significant progress, with the full delivery of the
first three objectives and the development of an internationally
competitive research platform from which we can seriously begin
to tackle the last.
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Relevance/Context
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To gain a physical insight into different coherent control scenarios
it is essential to investigate specific control schemes on simple
well defined model systems using well characterized femtosecond
(fs) laser pulses to allow for a direct comparison between theory
and experiment. At the outset of our research programme it was
generally believed that the mechanisms driving quantum control
in the perturbative limit (i.e. weak field) in atoms and diatomic
molecules were reasonably well understood, although there
remained a number of open questions as to how these mechanisms
might operate in polyatomic molecules. The mechanisms
operating in the non-perturbative regime (strong field) were, and
are still, a largely unexplored problem despite spectacular
experimental results on large molecules1.
Proposals for new technologies which explicitly exploit the
fundamental quantum nature of matter are beginning to emerge;
the most widely trumpeted being quantum information technology
(QIT) and the experimental realization of quantum cryptography.
These prospects have been built on fundamental research into the
quantum mechanical properties of atoms and molecules and the
development of ultrafast lasers and other instrumentation.
Improved quantum sensing and detection may well provide the
stimulus for a quantum industry in small scale applications far
removed from the realization of a quantum computer. There is
also a growing realization about the importance of coherent
energy transfer to the function of biological mechanisms such as
bacteriorhodopsin2 and in light harvesting proteins3.
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Fig. 1 Three photon fluorescence yield from the D state of I2 as a
function of the group delay dispersion (linear chirp) for the central
wavelengths shown. The data in each case are normalised to the
fluorescence detected for the transform limited pulse at each
wavelength.
nm on the X1Σ0+g ← D1Σ0+u McLennan band of molecular iodine
following three photon absorption (3PA). This detection scheme
follows that of Wilson and coworkers8. This work showed that
chirped laser pulses, in which the frequency components of a short
laser pulse are linearly delayed with respect to one another were
more efficient at promoting the transfer of population from the
ground state of molecular iodine to the D state than transform
limited pulses, in which all the frequency components are in step
with one another. At first sight this is a surprising result since the
excitation D1Σ0+u ← 1Σ0+g ←B3Π0+u ← X1Σ0+g between 600 and
550 nm requires three photons and thus depends on the laser
intensity cubed. Because the pulse intensity is highest for
transform limited (TL) pulses one therefore expects these to
transfer the greatest population from X to D but the experiment
shows that this is not the case. Why?
We have repeated the experiments of Yakovlev et al.8 using the
AOPDF rather than a prism pair to control the optical phase of the
excitation pulse. The salient feature here being that the device can
be used to program the spectral phase of an optical pulse as a
Taylor expansion about the central frequency or as an arbitrary
phase and amplitude mask5. This ability has allowed us to explore
the 3 photon excitation efficiency as a function of higher orders of
chirp than Yakovlev et al. and hence map the “fitness landscape”9.
Fig. 1 shows the enhancement over the transform limit to the
D1Σ0+u fluorescence for a series of pulses, whose spectra are
centred at the wavelengths shown, as a function of linear
frequency chirp. Although they made several proposals Yakovlev
et al.8 were unable to elucidate the control mechanism. In Fig. 2
we show a map of the enhancement as a function of both linear
and quadratic chirp or group delay dispersion (GDD) and third
order dispersion (TOD) defined by the coefficients ϕ (2) and ϕ (3) in
the Taylor expansion of the spectral phase:
Key advances and supporting methodology
We were the first group to pioneer the use of an acousto-optic
programmable dispersive filter (AOPDF) for molecular control
experiments. These devices had hitherto been used in the near
infra-red. Their principal application was in the pre-amplifier stage
of a regeneratively amplified femtosecond laser to control the gain
characteristics and hence the temporal and spectral profile of the
amplified pulse. Working with the manufacturer (Fastlite) and
colleagues in France (IRSAMC, Toulouse) we characterised and
optimised the performance of an AOPDF to work in the visible
region of the spectrum and the typical output of a non-collinear
optical parametric amplifier (NOPA). This work is described in
refs.[4-6]. The group of Dwayne Miller in Toronto have recently
applied this technology to coherent control of the retinal
isomerisation reaction in bacteriorhodopsin2. We were able to use
it to enhance continuum generation in liquid ethanol7.
Much of our work in coherent control to date has been
concerned with controlling the UV fluorescence yield around 320
ϕ (ω ) = ϕ (0) + ϕ (1) (ω ) (ω − ω0 ) +
1 (2)
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ϕ (ω ) (ω − ω0 ) + ϕ (3) (ω ) (ω − ω0 ) +"
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Novel Machine Learning Approaches to Molecular Coherent Control – Final Report
Fig. 4 Difference potentials for the various transitions. The X, and
D states are all well characterized. The intermediate 0g relay state
has been approximated by an exponential function. The dashed
vertical line indicates the Franck-Condon region for the first B←X
step.
Fig. 2 Fluorescence yield as a function of group delay dispersion and
third order dispersion for an excitation pulse centred at 570 nm. The
corresponding panel in Fig. 1 represents a cut through this surface for
zero TOD. The map is colour coded from blue to red, with blue
representing the lowest fluorescence yield.
Scherer et al.10, but technically easier since the two pulses are
created in our experiments by diffraction induced by a suitably
shaped acoustic pulse in the AOPDF rather than in a frequency
stabilized Mach-Zender interferometer. In two of these scans the
envelope phase of the two pulses have been deliberately unlocked
and fringes due to the oscillation of the electric field are clearly
observable. The temporal resolution of the interferometer is 0.2 fs.
The signals we have recorded are due to emission from the D1Σ0+u
state of molecular iodine. This is excited by three photon
absorption in which each step is resonantly enhanced by the
existence of intermediate relay states. The potential energy
functions of the X1Σ0+g ground state, the valence B3Π0+u state and
the D1Σ0+u ion pair state have all been extremely well
characterised spectroscopically. The nature of the second
electronically excited state in the ladder is much less certain.
Optical selection rules dictate that it must be of gerade symmetry
but there are a host of potential candidates. Somewhat
confusingly, Yakovlev et al. identify the second intermediate state
as C. This label is more commonly reserved for the 3Σ1+u state11, 12
correlating to I(2P1/2)+I(2P3/2), which cannot take part due to the
parity selection rule. We assign the intermediate state as the 1Σ0+g
state correlating to two spin-orbit excited iodine atoms. This is
known to be a repulsive state but its potential energy function is
poorly characterized and we represent it by a decaying exponential
of the form V ( R) = a exp[−b( R − R0 )] .
The greatest enhancement factor (4.8 times over that of a TL
pulse) was found for a pump-probe pulse sequence rather than a
chirped pulse. The conclusion from this is that the best strategy for
a given bandwidth and energy is to concentrate intensity in two
time windows separated by a short delay rather than simply
stretching a single pulse to cover that delay range. This evidence,
combined with the large enhancements seen for a wide range of
different time-orderings of the frequencies, allows us to
definitively rule out wavepacket following, as such a mechanism
would require light of precisely varying frequency throughout the
excitation event. To investigate this in more detail we also
performed experiments with π-step spectral phase functions. For
narrow, non-resonant three-photon transitions, this type of pulse
has been shown to cause cancellation of the transition probability
when applied about ω/3 where ω is the one-photon frequency13. In
resonant two-photon processes, flipping the phase about the
intermediate resonance causes transient enhancement of the
population in that level due to transient constructive interference
between non-resonant terms. A second pulse is needed to take
advantage of this enhancement14. The results for I2 (see Fig. 5) fall
Several “islands” where the enhancement of the fluorescence is
particularly strong are observable. Similar maps have been
recorded at a number of central wavelengths in the range 600 to
540 nm. Maps have also been obtained by systematically varying
the fourth and fifth order dispersion terms but these do not show
such strong variation in the fluorescence yield.
Fig. 3 shows the results of scans of the temporal delay between
a pair of transform limited pulses scanned through one another.
The experimental method is similar to that originally described by
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Fig. 3 Interferometric scan of two transform limit replicate pulses at the
wavelengths indicated. The scans at 560 and 555 nm have been
recorded without locking the envelope phase of the two pulses together
resulting in oscillations at the carrier frequency around the cross
correlation of the two pulses.
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Novel Machine Learning Approaches to Molecular Coherent Control – Final Report
into two categories depending on the wavelength. From 570 nm
downwards, the enhancement factor shows a featureless “hump”
as a function of the step position, usually peaking close to the
central wavelength at values of 2-3.3. The explanation for this will
be given below. In scans for 580, 590 and 600 nm, where the
absorption is mainly from the v”=1 hot band, the enhancement
factors are much smaller but structure apparently anti-correlated to
B state vibrational fine structure becomes apparent. The anticorrelation is emphasised in the inset to Figure 5, where the 580
nm scan is superposed on the B state absorption spectrum where it
overlaps B ← X vibronic lines v’=8-20 ← v”=0 and v’=9-22 ←
v”=1. The results of the π-step scans for shorter wavelengths are
well explained by a time-delay resonance mechanism. The effect
of a π-step on the temporal envelope is to introduce a minimum at
t=0 which deepens as the step is moved closer to the central
wavelength, and becomes a node when the central frequency is
reached. This is effectively a phase-only pump-probe sequence
which, because of the bandwidths used here, has inter-pulse delays
similar to those of the first peaks in the pump-probe scans. That
the greatest enhancements are seen for steps close to the central
wavelength (when the intensity goes to zero between the subpulses) is consistent with the idea that between the initiation of
wavepacket motion and the arrival of the wavepacket at the FrankCondon (FC) window the probability of reaching the D state goes
through a minimum and intensity at this time is wasted.
The question remains as to where the crucial dynamics occur.
To answer this we turned to simulations using a perturbative
treatment of the excitation and the split-time operator for
wavepacket propagation. This work was done in collaboration
with Chris Meier (IRSAMC, Toulouse) with partial support from
the British Council. The simulation is run for the lowest two
ground vibrational states and the final D state populations
weighted by the appropriate Boltzmann factor and summed. We
have been able to find values for the three potential parameters
which reproduce the positions of the peaks in the 570 nm pumpprobe scan. The appearance of a peak at around 45 fs is robust.
The peak heights are, however generally over-predicted (see Fig.
6).
It is instructive to consider the differences between the
optimised potentials (transition energies) for the two possible
time-delay resonances (Fig. 4). For the excitation spectra we have
studied, the vibrational spacing in the B state leads to a round trip
time of 300-400 fs. The first peak in the pump-probe experiment
Fig. 6 Experimental (solid line) and simulated (dashed line) pumpprobe scan for a 570 nm central wavelength. Inset – variation of the
total pulse energy as a function of delay for the experiment (points) and
simulation (dashed line).
occurs at less than half this time in all cases and must therefore be
connected with outward motion of the nuclear wavepacket. The
shifting of the peak towards longer times for lower frequencies
suggests that it corresponds to the arrival of a wavepacket in the
FC window of a transition whose frequency is a decreasing
function of internuclear distance (the wavepacket must travel
further to reach regions where lower energy photons are resonant).
Examining the difference potentials, we see that both the second
and third transition energies are greater than that of a 570 nm
photon at the X state minimum (5.0 Bohr) but both decrease to
become resonant at around 5.6 Bohr. This suggests that the crucial
dynamics occur on the B state potential. This can be seen from
Figure 7 where the population of the three excited states
(normalised to their final values) are plotted as a function of time
for a 45 fs pump-probe sequence. One can see that half of the final
B state population is excited by each pulse while the fraction of
the 0g and D state populations excited by the first pulse is much
smaller. This demonstrates that these states are inherently more
excitable after 45 fs of wavepacket evolution on B than they are
initially. The results of this work have just been submitted to a
special issue on coherent control to be published in J. Phys. B: At.
Mol. Phys.15
While there was no measurable dependence of the fluorescence
on FOD (albeit in a rather limited range) it is possible that even
higher order frequency variation can further enhance the transition
probability. To attempt to find any such enhancement we
developed a parameterisation of the phase mask of the AOPDF to
allow a learning algorithm access to the full phase shaping
Fig. 5 I2 3PA enhancement ratio over a transform limited pulse as a π-step
phase function is scanned across the spectrum for six central wavelengths
between 540 and 600 nm. Inset – scan at 580 nm superimposed on the B
← X absorption spectrum recorded in a standard UV/vis spectrometer
(Perkin Elmer Lambda 900).
Fig. 7 Simulated pulse intensity envelope (solid) and normalised
population for the B (dashed), 0g (dotted) and D (dot-dashed) states.
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Novel Machine Learning Approaches to Molecular Coherent Control – Final Report
capability of the device5. The learning algorithm chosen was an
implementation of a (μ/ρ+,λ)-Evolution Strategy (ES) as described
in [16] but without correlated mutation of the ~400 control
parameters. The results of the optimisations with this algorithm at
570 nm were described at an international conference 17. In most
cases the ES was able to improve slightly upon the optimal
GDD+TOD pulse although day-to-day variation complicates
direct comparisons. Fig. 8 shows the best solution from 3 runs at
580 nm represented as delay functions. These functions are
calculated as the derivative of the spectral phase and give a 1-D
approximation to a Wigner function. Like many of the solutions
obtained at other wavelengths, the optimal functions in Figure 4
are roughly symmetric about some frequency near the centre –
implying anti-symmetry of the phase function about this
frequency. Silberberg and co-workers have shown that antisymmetry about a given frequency preserves the TL probability of
a two-photon process at twice that frequency 13. For the time-delay
resonance mechanism one would imagine that the time-delay
corresponds to dynamics on either the first or the second
intermediate states. If this is the case the process can be viewed as
separate one-photon and two-photon transitions (not necessarily in
that order) separated by a time-delay. In this case, adding phase
anti-symmetrically about a near-central frequency would be an
efficient means of stretching the pulse to cover the time-delay
without diminishing the probability of the two-photon part. The
best enhancement factors obtained for the ES, ~4.4 for 560 nm,
were slightly above that for the optimal third order Taylor function
at that wavelength (~4.0) suggesting that GDD and TOD (an antisymmetric phase term) account for a large proportion of the
available enhancement. There is no way of knowing if the ES
solutions are globally optimal, however, the current evidence is
that an ES closed-loop control strategy appears to find solutions
that exploit the same physics as pulse sequences and π-phase
jumps in our open-loop control experiments. Leeds are currently
applying the same ES algorithm in simulation experiments (see
Fig. 8) using the split-time operator wavepacket propagation code,
while UWA are applying data-mining techniques to the many
thousands of experimental runs we have accumulated throughout
the project to date in order to look for correlations that might be
able to tease out the physics of the enhancement process from a
study of the “evolutionary history” of the optimised pulses.
The machine learning research task to “Develop generic
machine learning software for closed loop control” was
successfully completed. The code has been demonstrated doing
closed loop control (see publications). The code is also
demonstrably generic as we have ported it to Helen Fielding's
Laboratory in UCL. We have also put in place the infrastructure
for the machine learning for generalisation across chemical
problems (see Further Reserach).
Machine learning is central to closed loop control. In machine
learning developing a good representation is generally the key to
success. We therefore had to develop a novel machine learning
representation for AOPDFs. This work was done by Dr. Burbidge
and was a major research contribution to the success of whole
project. Dr. Burbidge is a computer scientist and the development
of this representation is a tribute to his cross-disciplinary skills.
There were two parts to representing the AOPDF learning
optimisation problem: the pulses need to be represented, and a
“fitness” function developed for optimisation. The pulses were
parametrized with respect to angular frequency, ω = 2πc/λ. A
pulse is shaped by varying the phase, φ(ω), at a number of control
points, ω1, . . . , ωd determined by the spectral resolution of the
pulse shaper. The amount of retardation for each frequency, ω, is
known as the delay, τ(ω). This controls the phase since τ(ω) =
delay / fs
dφ(ω)/dω. Non-linear optical processes, such as 3PA in iodine, are
unaffected by the addition of aω + b to φ(ω) thus optimisation of
φ(ω) directly results in two-dimensional infinite redundancy in the
search space. For this reason we decided to optimise the second
derivative of the phase w.r.t. angular frequency, δ(ω) = dτ((ω)/dω
= d2φ(ω)/dω2. Since there are only a finite number of control
points, ω1, . . . , ωd , at which the phase can be specified, we
optimise δ(ωi) i = 1, . . . , k and numerically integrate twice to
obtain the phase. The range of δ(ωi) is the same for all ωi and is
determined by the physical properties of the acousto-optic crystal.
There are ~430 independent frequency control points (depending
slightly on the central frequency).
We aimed to maximise the probability of a signal (3PA,
photoion yield, etc.) for a shaped laser pulse with a given power.
The most common choice of algorithm in the quantum control
literature for such problems are evolutionary algorithms, although
other search algorithms have been used, such as the modified
simplex algorithm. We therefore adopted an evolutionary
algorithm. All optimisation algorithms aim to maximise (or
minimise) some objective function. In the field of evolutionary
optimisation the objective function is called the fitness function in
analogy with Darwin’s concept of biological fitness. The choice of
fitness function will affect the properties of the solution found,
although it is rarely discussed in the quantum control literature.
The most obvious choice of fitness function is f1(δ) = S(δ) , where
S(δ) is the experimental signal and δ is shorthand for the laser
pulse with spectral phase obtained by twice numerically
integrating δ(ωi) i = 1, . . . , k. However, the mean laser power is
not constant during the course of an optimisation owing to
changes in ambient temperature and humidity that are difficult to
fully control. The result is that f1(δ) has a non-constant mean for a
fixed δ. In principle, S(δ) ∝ PD(δ)n, (PD = laser intensity
measured by a photodiode and n is the photon order of the nonlinear excitation) which suggests f2(δ) = S(δ)/PD(δ)n as the fitness
function. Owing to noise and dark signal the proportionality does
not hold in practice, so that f2(δ) also has a non-constant mean for
a fixed δ. Thus the problem of a dynamic fitness function is not
overcome when normalizing the signal by the laser power. Thus,
the signal for a shaped pulse is compared to that of the unshaped
pulse. The unshaped pulse has δ(λ) = 0, hence φ(λ) = 0, for all
wavelengths λ. This is known as the transform-limited (TL) pulse,
δTL, and is the shortest and most intense pulse for a given power
and bandwidth. A signal enhancement above that achievable with
the TL is indicative of optical effects that are not due to intensity
variation and suggests that coherent control of the optical process
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Fig. 8 Experimental (solid) and simulated (dotted) I2 3PA optimization
delay functions for a pulse centred at 580 nm. Each trace represents the
optical delay as a function of frequency that is found by the evolutionary
optimization on different runs.
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Novel Machine Learning Approaches to Molecular Coherent Control – Final Report
emission from liquid ethanol, published in [7]. However, on 21
October 2004 we suffered a major set back as a water pipe burst in
the riser ducting above our (basement) laboratory. The laboratory
was flooded to a depth of approximately 10 cm with some water
entering through the ceiling onto the optical table. The situation
was exacerbated when, in November 2005, we were the victims of
a second (less serious) flooding incident when a radiator on the
second floor of the building burst and the flood destroyed our Sun
workstation (running the evolutionary code developed in UWA)
and one of the turbomolecular pumps on our molecular beam
chamber. Although at first it appeared that the first flood had not
damaged our regeneratively amplified laser system it became
increasingly difficult to work with and after the second flood the
laser was so unreliable that it was out of action for weeks at a
time. Eventually the problems were tracked down to long-term
humidity damage to the Er fibre seed laser and the entire system
was replaced under the insurance cover in January 2007. The total
cost of replacing damaged equipment and infrastructure was £429
700, but the major disruption was in the time lost rebuilding the
laboratory after the 2004 flood. These events cost more than a year
in lost experimental time and the research programme as laid out
in the proposal had to be modified accordingly.
Although we were not able to achieve our goal of
demonstrating coherent control over a homologous chemical
series we have managed to complete a thorough study of the
control mechanisms operating in the three photon absorption of
molecular iodine using a combination of both open and closed
loop control experiments thus demonstrating the first UK
capability in molecular coherent control. We also succeeded in our
goal of developing generic software for closed-loop control.
Through additional funding obtained through LaserLab Europe
and the British Council, DTA funded PhD students Nicholas Form
and Iain Wilkinson were able to carry out work for their respective
thesis at the CEA laboratories in Saclay and IRSAMC in Toulouse
during those periods when the laboratory in Leeds was
unserviceable.
In retrospect, as far as machine learning research is concerned,
it is probable that we spent too much time on engineering the
software to make it robust and usable for the chemistry, compared
to focussing on the interesting machine learning issues connected
with fast learning. The research project in UWA was handicapped
by staff turnover. The first PDRA, David Enot, decided to leave
the project because he found the work too difficult. David is a
Ph.D. chemist with experience in machine learning, but he found
the physical chemistry a challenge. He left to take up a PDRA
position in metabolomics in the Institute of Biological Sciences at
UWA. We were then very lucky to employ Robert Burbidge. He
is a computer scientist with some experience in chemistry. He
managed to understand the physical chemistry extremely well.
However, he also decided to leave the project before the end, so as
to take up a highly paid job analysing statistical data in
advertising.
has been achieved. This suggests f3(δ) = S(δ)/S(δTL) as the fitness
function. Since the laser power varies, S(δTL) should be measured
frequently enough that the long-term drift in the laser power is
negligible. Once per generation is sufficient for the algorithm used
here in the current experimental conditions. (If S(δTL) were
measured for every shaped pulse, then the number of fitness
evaluations possible in the given available time would be halved.)
Since the mean of the dark signal varies, we also record the dark
signal, S(0), prior to evaluating S(δTL). The enhancement over that
of the TL for a shaped pulse, δ, is then estimated as (S(δ) − S(0)) /
(S(δTL) − S(0)). We do not use this as the fitness function since the
dark signal is independent of the laser power and the pulse shape,
whereas the noise in the dark signal would increase the noise in
the fitness function and lead to a deterioration in performance.
Active learning is the branch of machine learning where the
computer gets to choose the next observation. As part of the
project we examined the relationship between active learning and
closed loop control. We also carried out an in-depth investigation
of a committee-based approach for active learning of real-valued
functions. Little work has been done active learning and
regression. The committee-based approach is a variance-only
strategy for selection of informative training data. As such we
demonstrated on a standard that it suffers when the model-class is
misspecified, since the learner’s bias is high. Conversely, the
strategy outperforms passive selection when the model class is
very expressive, since active minimisation of the variance avoids
overfitting 21.
Project plan review
In our original proposed programme we pointed out that many
previous demonstrations of molecular coherent control in closedloop experiments under the control of an evolutionary algorithm
have relied on multiphoton processes often involving 7 or more
near infra-red photons. Such highly non-linear excitation
pathways mean that it is an extremely difficult task to interpret the
optimized optical waveforms mechanistically. Our aim was to
investigate photochemical reactions using fewer photons by pulse
shaping in the visible portion of the electromagnetic spectrum. We
had proposed to build a pulse shaper based on a liquid crystal
spatial light modulator for this purpose but during procurement we
became aware of a new approach to optical pulse shaping based
on an acousto-optical crystal. Although the device had only been
demonstrated previously in the near infrared the modulator had a
number of desirable properties; it was compact, occupying only a
few square centimetres of optical table space compared to the
several thousand required for a spatial light modulator; it was
efficient and combined both amplitude and phase shaping
capabilities in a single unit; and it was internally calibrated.
Working with the manufacturer (Fastlite) we were able to specify
and characterize a new device for shaping the output of a noncollinear optical parametric amplifier. These results were
published in [4] and presented at the 2004 CLEO [6]. According
to our project plan “within the first year we will have
demonstrated some simple applications of coherent quantum
control, such as the spatial compression of a deliberately chirped
optical pulse and control over the branching of a chemical process,
such as the photoion fragmentation pattern of a molecule, e.g.
toluene”. We were indeed able to apply a genetic algorithm
developed in UWA to optical pulse compression and control over
the ion fragmentation pattern of a polyatomic organic molecule,
p-nitrotoluene and to report these preliminary results at the RSC
Spectroscopy and Dynamics group meeting in December 2004.
We also demonstrated open-loop control of stimulated Raman
Research Impact and Benefits to Society
The area of coherent control falls into the general field of
atomic, molecular and optical physics. Internationally AMOP is
recognised as an excellent training ground for graduate students
and early stage postdoctoral researchers. This is because the
training is generally done in small scale laboratory environments,
where the students are at the frontiers of science and usually in
charge of running lasers, vacuum machines, electronics etc. In
addition they have to develop new scientific instruments and
methods. In Leeds the latter plays a central role and the
5
Novel Machine Learning Approaches to Molecular Coherent Control – Final Report
has also been sought (EP/F028792) and is due to be considered by
a peer review panel at the end of October. During the course of
the project Leeds and UWA developed a fruitful collaboration
with Professor Helen Fielding’s group at UCL (EP/D070651).
The collaboration has since branched into another area of overlap
between computer science and chemistry research: knowledge
representation and information retrieval. This collaboration has
resulted in £100,000 Joint Information Systems Committee (JISC
project): ART. The ART Project will develop an ontology based
article preparation tool. The project also involves the of Colin
Batchelor from the Royal Society of Chemistry publishing. The
aim of this project is to improve the use and reuse of information
in physical chemistry papers by developing a formalism and tool
for annotating the papers. Papers on physical chemistry were
chosen because of their stereotypical format. The tool will be
targeted at both paper authors and reviewers.
techniques we have developed have produced highly skilled
people (2 postdocs and 2 PhD students). Dr Anjan Barman (DoB
23.06.69; 01.02.2004 - 31.07.2005) now has a faculty position in
the Dept. of Physics, ITT, New Delhi where he is developing
ultrafast nanomagnetic devices, time-resolved near field
microscopy and coherent control by optical pulse shaping. Dr Ivan
Anton Garcia (DoB 10.01.77; 14.04.06 - 14.09.06) is now at the
University of La Rioja developing MALDI apparatus for peptide
sequencing based on work started in Leeds. Mr Nicholas Form
started his PhD studies in Oct. 2003 and is in the final stages of
writing up his thesis. During his PhD he has already co-authored 4
published papers (1 with Barman and 2 with Burbidge), and
another paper has just been submitted. Mr Iain Wilkinson is a third
year PhD student who is a co-author on 2 recently submitted
papers (both with Garcia) and another paper is in an advanced
state of preparation. A MChem student (Mr Robert Salter) also
participated in the project (Oct 2007 – June 2007) and he has
subsequently taken up a PhD course at Leeds in another group. In
total as a result of work funded by the grant in whole or in part we
have published 4 refereed papers [4, 5, 7, 18], and 4 refereed
conference papers [6, 9, 17, 21]. A further 3 papers have been
submitted for publication [15, 19, 20] and yet others are in an
advanced state of preparation.
The research has therefore had a significant impact with respect
to training and dissemination and this has led to an international
dimension involving collaboration with other European
laboratories and an industrial company. Within the UK we also
acted as a consultant to SAI, Manchester, who are interested in
applying shaped femtosecond pulses in MALDI mass
spectrometric studies of proteins.
References
1. A. Assion, T. Baumert, M. Bergt, T. Brixner, B. Kiefer, V. Seyfried,
M. Strehle and G. Gerber, Science, 1998, 282, 919.
2. V. I. Prokhorenko, A. M. Nagy, S. A. Waschuk, L. S. Brown, R. R.
Birge and R. J. D. Miller, Science, 2006, 313, 1257.
3. O. Rubner, T. Baumert, M. Bergt, B. Kiefer, G. Gerber and V.
Engel, Chem. Phys. Lett., 2000, 316, 585.
4. A. Monmayrant, A. Arbouet, B. Girard, B. Chatel, A. Barman, B. J.
Whitaker and D. Kaplan, Appl. Phys. B: Lasers and Optics, 2005,
81, 177.
5. N. T. Form, R. Burbidge, J. Ramon and B. J. Whitaker, J. Mod.
Opt., 2007, DOI 10.1080/0950034.
6. A. Monmayrant, A. Arbouet, B. Girard, B. Chatel, A. Barman, B. J.
Whitaker and D. Kaplan, Conference on lasers and electro optics,
San Francisco, 2004.
7. A. Barman, N. T. Form and B. J. Whitaker, Chem. Phys. Lett., 2006,
427, 317.
8. V. V. Yakovlev, C. J. Bardeen, J. W. Che, J. S. Cao and K. R.
Wilson, J. Chem. Phys., 1998, 108, 2309.
9. N. T. Form and B. J. Whitaker, in Coherent Control of Molecules,
eds. B. Lasorne and G. A. Worth, CCP6, Daresbury, Editon edn.,
2006.
10. N. F. Scherer, R. J. Carlson, A. Matro, M. Du, A. J. Ruggiero, V.
Romerorochin, J. A. Cina, G. R. Fleming and S. A. Rice, J. Chem.
Phys., 1991, 95, 1487.
11. C. Teichteil and M. Pelissier, Chem. Phys., 1994, 180, 1.
12. R. K. Sander and K. R. Wilson, J. Chem. Phys., 1975, 63, 4242.
13. D. Meshulach and Y. Silberberg, Phys. Rev. A, 1999, 60, 1287.
14. N. Dudovich, D. Oron and Y. Silberberg, Phys. Rev. Lett., 2002, 88,
123004.
15. N. T. Form, B. J. Whitaker and C. Meier, J. Phys. B. At. Mol. Phys.,
2007, submitted.
16. H.-G. Beyer and H.-P. Schwefel, Natural Computing, 2002 1, 3.
17. R. Burbidge, J. J. Rowland, R. D. King, N. T. Form and B. J.
Whitaker, IEEE Symposium on Computational Intelligence and
Data Mining, 2007.
18. N. T. Form, B. J. Whitaker, L. Poisson and B. Soep, PCCP, 2006, 8,
2925.
19. K. Raffael, V. Blanchet, B. Chatel, G. Turri, B. Girard, I. A. Garcia,
I. Wilkinson and B. J. Whitaker, J. Chem. Phys., 2007, submitted.
20. V. Blanchet, K. Raffael, G. Turri, B. Chatel, B. Girard, I. A. Garcia,
I. Wilkinson and B. J. Whitaker, J. Chem. Phys., 2007, submitted.
21. R. Burbidge, J.J. Rowland, and R. D. King. Active Learning
Explanation of Expenditure
Leeds salary costs were greater than anticipated and money was
vired from the travel budget to cover these. We justify this on the
grounds that we were able to secure some additional external
funding from the British Council and LaserLab Europe to cover
travel expenses and that due the previous experience and age of Dr
Barman at the time of appointment the costs of employing him
were higher than we had budgeted. Dr Barman did an exemplary
job of commissioning the laboratory and without his assistance it
would have been difficult to have recovered so quickly from the
flood. All other costs were very close to the original estimates.
Further Research
Despite the set-backs we have made very good progress and the
work has served as a springboard for follow-on projects. The work
on machine learning algorithms has led to a multi-site crossdisciplinary project involving (mainly) Nottingham, Southampton,
Edinburgh at Leeds in which we are investigating synchronization
phenomena between chemical oscillators through the application
of global optical coupling feedback using a light sensitive
Ruthenium catalyst, and the formation of electrical networks of
carbon nanotubes using current feedback (EP/D0233781). We
have also started a very productive collaboration with IRSAMC at
the Université Paul Sabatier, Toulouse (with Prof Girard, Meier
and Drs Blanchet and Chatel). This has been funded by the CNRS
(through a Poste Rouge visiting professorship to BJW) and the
British Council (through the Alliance programme). As the result
of these interactions and the impact of the research funded through
this project BJW was approached to lead an EU ITN proposal
(FP7-214082-2)on “Imaging and Coherent Control in Chemistry”
which is currently under consideration having been selected from
a pool of over 1000 initial proposals. Follow-up EPSRC support
for Regression based on Query by Committee, 8th
International Conference on Intelligent Data Engineering and
Automated Learning, 2007 (in press)]
6