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Novel Machine Learning Approaches to Molecular Coherent Control – Final Report Summary of aims and objectives 6 0 0 nm 1 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. 0 590 nm enhancement on TL 1 0 5 8 0 nm 1 0 5 7 0 nm 2 0 5 6 0 nm 2 0 Relevance/Context 5 5 0 nm 2 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. 0 -1 0 0 0 0 -5 0 0 0 0 G D D / fs 5000 10000 2 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) 1 2 3 ϕ (ω ) (ω − ω0 ) + ϕ (3) (ω ) (ω − ω0 ) +" 2 6 1 (1) 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 1.0 600 nm 0.5 0.0 590 nm fluorescence signal / arb. units 1.0 0.0 580 nm 0.5 0.0 560 nm 0.5 0.0 555 nm 0.5 0.0 2.0 550 nm 1.0 0.0 0 500 1000 delay / fs 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. 2 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. 3 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 40 20 0 -20 -40 -60 -80 -100 -120 -140 -160 -180 -200 -220 -240 3.15 3.20 3.25 3.30 3.35 3.40 3.45 3.50 ω / rad PHz 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. 4 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. 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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