Keynote Talks

Stochastic Galerkin (SG) approximation, also known as intrusive polynomial chaos approximation, can be used to facilitate forward uncertainty quantification (UQ) in models consisting of partial differential equations (PDEs) with uncertain inputs. Unlike conventional sampling methods, such as Monte Carlo, SG schemes yield approximations that are functions of the input random variables, leading to surrogate models. Although the study of intrusive SG methods is now mature for simple model problems, much work remains to be done to determine whether they can provide a computationally feasible framework for facilitating forward UQ in more complex PDE models arising in engineering applications. There are two key challenges - designing the polynomial space to ensure the resulting approximation is provably accurate and the efficient numerical solution of the associated (potentially huge) linear systems of equations. In this talk, we will give an introduction to SG methods for forward UQ in a simple PDE model and then discuss recent work on developing more advanced methods for linear elasticity and poroelasticity models.

Opening the Black Box: How Understanding And Interpreting Machine Learning Models can Enhance Materials Design and Characterisation

Machine learning (ML) and artificial intelligence (AI) are the subjects of much debate as to potential impact for good or ill. Depending who we talk to ML is the solution to almost every human challenge, from open boarders to pandemic control, or presents an existential crises for the species. In materials science the polarisation is perhaps less extreme, but nonetheless pervasive. The numbers of ML related works experiences an explosion, yet one highly respected theoretical chemist recently pronounced “[a]t least 50% of the machine learning papers I see regarding electronic structure are junk”. A part of the issue that many detractors have with ML methods is related to their perception of the techniques as ‘black-box’ approaches, at the same time, the same lack of understanding limitations of the models leads to some of the more outlandish boosterism surrounding the subject. In this talk I will discuss, with examples from our work, how we can open up the black-box of ML methods, highlighting and understanding limitations, increasing trust in results, and potentially improving the methods themselves.

Presentations from HetSys Cohort 1

Modelling Stimulated Raman Scattering in Laser Direct-drive Fusion Plasmas at Ignition Scale

Stimulated Raman Scattering (SRS) is a process of significant importance in laser-driven inertial confinement fusion (ICF) implosions. It is a process which reduces the efficiency of the implosion, both by direct scattering of light away from the fuel target, and by creation of hot electrons which preheat the target core. These effects are the result of unstable growth of electromagnetic and electron plasma daughter waves respectively, in a three-wave coupling mechanism with the laser pump. SRS occurs in the coronal plasma on timescales and lengthscales on the order of picoseconds and microns, whereas the hydrodynamic progression of the plasma occurs on the order of
nanoseconds and millimetres. This separation of scales means that predictive modelling of SRS in ignition-scale ICF simulations is a significant challenge.

The newly proposed modelling scheme is an extension of the method outlined by Debayle et al. [1]. It is proposed to add a Gaussian process (GP) surrogate model into this scheme which predicts effective linear convective SRS gains in each cell based on the fully resolved solution to a 1D boundary value problem over the laser ray path in that cell. On-the-fly training allows for efficient fitting of the GP in regions of input space relevant to ICF. This augmentation with a GP surrogate provides robustness to hydrodynamic grid resolution and increased accuracy of solution to the underlying equations. Going forward, the scheme should allow, with minor modification, inclusion of additional physics in a hierarchical manner; removal of the strong damping limit approximation, as well as inclusion of time dependency and kinetic effects, are potential paths forward.

References
[1] Debayle et al., 2019. Physics of Plasmas 26, 092705

In recent years, machine-learning methods, such as artificial neural networks (ANNs), have been increasingly applied to predicting the thermodynamic and kinetic properties of chemical reactions. While a number of recent studies have reported success in predicting chemical reaction activation energies, less attention has focused on how the accuracy of ML predictions filter through to predictions of macroscopic observables. In this presentation, I present an overview of how ML prediction of activation energies impacts observable properties of chemical reaction networks, as demonstrated by microkinetics simulations using ML-predicted reaction rates. Based on standard molecular descriptors for reactants and products, I will demonstrate how our trained ANN can predict activation energies, but then demonstrate how we can use the predictions for performing microkinetic simulations of three different prototypical reaction networks: formamide decomposition, aldol reactions, and 3-hydroperoxypropanal decomposition. Finally, I will focus on the impact that the structure of chemical reaction networks has on faithfully replicating ab-initio kinetics.

A precises description of the behaviours of nanoscale thinfilms is key to various applications such as printable electronics, lab-on-a-chip devices, spin coating as well as high performance lubricants. At such a small scale the free surface can be viewed as a collection of particles moving randomly (Brownian motion) generating nano waves and the continuum description of the fluid breaks down. Fluctuating hydrodynamics (the Landau-Lifstiz Navier-Stokes equations) have been raised to include the random motions and for thinfilms the stochastic lubriation equation (SLE) has been derived. Previous studies have focused on thinfilms with periodic boundary conditions assuming the film is unbounded, but this is not physical.

In our study we examined both 2D and 3D thinfilms with two physically motivated contact line conditions where the free surface meets a confining solid wall: 90 degrees contact angle and pinned contact line. To understand the behaviour of the film, we run Molecular Dynamics (MD) simulations and rationalise the results of these using conventional mathematical techniques applied to SLE. For different boundary conditions, new wave modes are derived from linear stability analysis generating results that agree excellently with the MD. The results show that the effect of confining boundaries propagates across the free surface, even for relatively long films. And with 3D thinfilms we conclude with ideas as to how these results can be experimentally validated.

Accurate simulations of the excited state dynamics of chromophores in complex environments are a prerequisite to understanding important properties such as photostability, relaxation pathways, and excited state lifetimes. An atomistic understanding of these properties can aid in the design and study of useful chemical compounds such as novel sunscreen candidates. However, with an explicit representation of the solvent, there are conflicting requirements set by the need to follow individual trajectories over long timescales, to sample over the ensemble of solvent configurations, and to use a high level of theory to obtain good chemical accuracy. This puts such methods out of the reach of DFT or QC methods alone. Our ongoing work uses machine learning to approximate ground and excited state potential energy surfaces of chromophores in a variety of solvent environments. This allows for an acceleration of the dynamics simulations, while retaining the accuracy of DFT or higher level methods. Our workflow is based in ESTEEM: Explicit Solvent Toolkit for Electronic Excitations of Molecules.

ESTEEM can call on a range of different electronic structure, molecular dynamics, machine learning codes, and spectroscopy post-processing tools to calculate excited state and spectroscopic properties of molecules in solvent environments.

From self-assembly to mechanical behaviour: a data-driven framework for block copolymers.

As inherently heterogeneous materials, block copolymers (BCPs) can demonstrate non-trivial mechanical behaviour such as hyperelasticity, viscoplasticity and hysteresis. Whilst there exist some continuum approaches to modelling the mechanical response of BCPs [1-2], models that encompass the full detail of the underlying polymer dynamics have not yet been developed. Of interest is the tendency of BCPs to self-assemble into well-defined morphologies and the effect that this self-assembly has on the mechanical response.

We employ a computational framework that combines self-consistent field theory simulations (SCFT) and molecular dynamics (MD). The use of SCFT enables us to obtain an equilibrium structure on the mesoscopic scale. Subsequently, the results of the SCFT calculation are used to generate density biased random walks (DBRW) to build a particle-based model [3-4], capturing a microstructure composed of glassy and rubbery phases. Furthermore, we present an improved DBRW algorithm that allows for the particle-based modelling of highly heterogeneous BCP systems such as ABC triblock copolymers.

Coarse-grained MD is then employed to predict the mechanical deformation of BCP systems for selected microstructural parameters. We show how the obtained simulation data can then be used to train a Gaussian process regression model for the prediction of a variety of mechanical properties. We then demonstrate how this framework may be employed to model cyclic deformation as well as efficiently capture the hysteresis effects that emerge throughout.

References

[1] C. P. Buckley, D. S. A. De Focatiis and C. Prisacariu; In Constitutive Models for Rubber VII; 1st Ed.; S. Jerrams, N. Murphy, CRC Press: London, pp. 3-10, (2011)

[2] Cho, H.; Mayer, S.; Pöselt, E.; Susoff, M.; Boyce, M. C., Polymer 128, 5 (2017)

[3] A. F. Terzis, D. N. Theodorou, A. Stroeks, Macromolecules 33, 4, pp. 1385-1396 (2000)

[4] T. Aoyagi, T. Honda, M. Doi, J. Chem. Phys. 117, 8153-8161 (2002)

Short-Range Order in High-Entropy Alloys: First Principles Theory and Atomistic Modelling

Short-Range order (SRO) can be either beneficial or detrimental to the properties of novel high- and medium-entropy alloys. An understanding of phase behaviour and underlying physical mechanisms driving ordering is therefore essential. We present results from an all-electron, first principles, Landau-type theory which enables us to obtain SRO directly, and also to obtain parameters suitable for atomistic modelling to understand incipient order in these materials. As a case study, we present results on the prototypical face-centred cubic high-entropy alloy, NiCoFeMnCr, and its derivatives, collectively referred to as the Cantor-Wu alloys [1]. We are able to show that the dominant correlations in these systems are between Co and Cr, and that Fe and Mn dilute interactions and stabilise the disordered solid solution. Our approach not only provides insights into the physical origins of ordering, but is also exceptionally computationally efficient compared to conventional supercell-based approaches.

1. C. D. Woodgate, J. B. Staunton, "Compositional Phase Stability in medium-entropy and high-entropy Cantor-Wu alloys from an ab initio all-electron Laundau-type theory and atomistic modelling", Phys. Rev. B, 105, 115124 (2022) - Editors’ Suggestion

Talks and Posters from HetSys Cohort 2

An Efficient Approach for Improving Phonon Representation in Machine Learned Potentials.

Gaussian approximation potentials (GAP) have been trained on density functional theory (DFT) predictions for free energy and derivative quantities. We demonstrate a computationally economical means to capture the phonon representation in Al, Mg, Si and W, and also use GAP to investigate the ordering of a dilute amount of Al within hcp-Ti. Vibrational and elastic properties of Al, Mg, Si and W are reproduced with excellent accuracy as compared against DFT. From hybrid Monte-Carlo molecular dynamics simulations, we find that Al preferentially orders itself in hcp-Ti such that Al-Al nearest neighbours are disfavoured.

Uncertainty Quantification in Atomistic Simulations using Interatomic Potentials

Atomistic simulations in several fields frequently rely on interatomic potentials (IPs) to predict physical output quantities of interest (QoIs). This is due to the vast decrease in cost of simulation when compared to ab-initio approaches – such as DFT – and correspondingly a far greater accessibility to more complex QoIs in both time- and length-scales. However, any move away from first principles methods will incur a loss in accuracy and an increase in uncertainty of QoIs, since we cannot expect a parameterised IP to reproduce the true potential energy surface of a given system. This uncertainty due to utilising an IP can arise from several sources: the parametric uncertainty from not knowing the ‘true’ parameters, uncertainty from incomplete/noisy training data in the case of machine leaning IPs, or from model form error arising from limitations in the functional form of the model, amongst others.

A complete method of placing meaningful error bars on output QoIs, by propagating uncertainties in a model through simulation to output(s), remains an important goal in multiscale materials modelling. Previous work towards this has been focused on ensemble/committee methods [1], producing a set of likely parameters by minimisation of a loss function aimed at matching DFT forces and energies.

We approach this problem from a robust statistical viewpoint by recasting model calibration as a Bayesian inverse problem; assuming prior distributions for IP parameters, formulating our likelihood based on observed data and model parameters, and finally forming a posterior
distribution of coefficients. Sampling from this distribution, we form ensembles of possible models, perform simulations for each model and in this way, estimate the uncertainty in output QoIs. This approach can be utilised for both fixed form IPs and for machine learning IPs, for a wide range of possible QoIs, however recent focus is concentrated on relatively simple quantities such as elastic constants, vacancy formation energies and bulk moduli of silicon, using different atomic clusterexpansion (ACE) [2,3] potentials.

[1] - Sarah Longbottom and Peter Brommer. “Uncertainty quantification for classical effective potentials: An extension to potfit”. In: arXiv (2018), pp. 0–12.
[2] - Ralf Drautz. “Atomic cluster expansion for accurate and transferable interatomic potentials”. Phys. Rev. B 99, 014104 (2019).
[3] - Yury Lysogorskiy, Matteo Rinaldi, Sarath Menon, Cas van der Oord, Thomas Hammerschmidt, Matous Mrovec, Aidan Thompson, Gábor Csányi, Christoph Ortner, and Ralf Drautz, “Performant implementation of the atomic cluster expansion”. Npj Comput. Mater. 7, 97 (2021).

Using kinetic Monte Carlo to investigate crystal growth in NiAl Superalloys.

Superalloys can be used at a high percentage of their melting point with good resistance to fatigue and creep. Nickel-Aluminium superalloys gain much of this resistance from the interaction between and phases. We aim to study the growth of from the phase at the atomistic level. We test the feasibility of using the kinetic Activation-Relaxation Technique (kART), an off lattice kinetic Monte Carlo (kMC) method. kART finds and catalogues the barriers within the system and evolves the system through a chain of metastable states. Using the barriers heights that separate the states, a rate is calculated based upon harmonic transition state theory (HTST). To examine the feasibility of using kART to study the growth of , a / interface was set up: 256 atoms of next to 256 atoms of random alloy, with one vacancy introduced to facilitate diffusive processes. The system was then evolved over 650 kMC steps at 900 K using a published potential for to calculate energies and forces. The study shows that kART evolves the system with the system lowering in energy over time, indicating that it is moving towards an equilibrium. However, there is no real order emerging within the test cell indicating access to longer time scales is required. Many areas for further research are identified including using growth rates learnt to parametrise a partial differential equation model to simulate the ageing processes on technologically relevant time and length scales.

Towards Automatic Reaction Networking for Modelling Long-Term Degradation of Materials

Knowing the pathways and products of material decomposition is of vital importance to industry, where environmental factors can cause accelerated aging within critical components. However, computationally modelling this molecular breakdown process requires expansive networks of reactions with accurate reaction rates, such that they can be propagated forwards through time to predict how a material will decompose under specific conditions. The creation and solving of such networks is a computationally expensive process. We employ a single-ended graph-driven searching technique to programmatically build complete reaction networks for the breakdown of polyethylene (PE), using machine learning to efficiently predict reaction barriers. We then propagate these networks in time using differential equation solvers written in Julia, culminating in a highly efficient workflow that is capable of creating and exactly solving complete breakdown networks to predict how PE thermally decomposes over timespans of decades within just a few hours of compute time. We explore further improvements that could be made to the efficiency and accuracy of this method, as well as discussing how the method could be extended to elucidate long-term breakdown pathways due to other non-thermal environmental effects.

Dynamic Leidenfrost Effects: Computational Modelling to Predict Transitions in Drop Impact

When a liquid drop is placed gently on a sufficiently hot surface it is able to levitate on its own evaporative vapour cushion. This `Leidenfrost effect' is well understood and has been the focus of much research, motivated by its importance for numerous industrial applications where the dramatic decrease in thermal conductivity caused by it is often detrimental. Notably, however, many technologies, such as spray cooling, actually involve the impact of droplets in Leidenfrost conditions.

Recently, experimental studies have probed the dynamic Leidenfrost effect, where droplets can be forced into contact when impact speeds are large enough, uncovering several interesting modes of contact and discovering new unexpected effects, such as an oscillating film height in certain regimes. To provide new insight into the physical mechanisms involved in such phenomena and as an important predictive tool, we have developed a novel computational model for the dynamic Leidenfrost process. This uses lubrication theory to model the evaporated vapour, and the finite element method to solve the Navier-Stokes equations in the drop, implemented in the open source library oomph-lib and has passed a number of benchmarking tests.

Our model enables us to explore the parameter space of impacting velocity and solid surface temperature, and probe different regimes of contact and vapour film behaviour, with the aim of predicting the critical impact speed at which contact will occur.

Probabilistic Meshless Methods for Parabolic Partial Differential Equations (P.D.Es)

When a physical phenomena is being modelled by a P.D.E we often have data that represents a partial solution to the P.D.E but not necessarily the parameters that describe the P.D.E, e.g the diffusivity coefficient in the heat equation. Currently, it is popular to use probabilistic methods like maximum likelyood estimation to estimate parameters as they can also provide a confidence interval of how accurate our estimation of the parameter is. Most of these methods use a 'forward solver' that solves the P.D.E for some set of parameters and then apply statistical techniques to determine which parameter is mostlikley based on the data.

In my Ph.D I am developing a forward solver that is probabilistic in it self, as most solvers are determnistic. The main idea of the solver is based on the Kalman filter, which is an algorithm that uses a series of measurements observed over time to produce estimates of the unknown variables, by estimating a joint probability distribution over the variables for each timeframe. One assumes that the prior probability distribution of the unknown variables is a multivariate Gaussian and therefore the algorithm reduces to simply applying linear transformations and conditioning this multivariate Gaussian on the observed data. As we are interested in solutions to P.D.Es our unknown variables belong to some infitie dimensional function space, so we can no longer place a Gaussian prior on our solution space, so we use Gaussian processes instead. So far I have been developing this theory and currently I am trying to implement this Gaussian process based Kalman filter. 

Validating Hydrodynamic Codes for Inertial Confinement Fusion using experimental Shock-timing Data

Nuclear fusion provides access to clean, unlimited, and reliable power source. One of the ways in which to achieve fusion here on Earth is through Inertial Confinement Fusion (ICF). The design and interpretation of ICF experiments depends on accurate, predictive numerical calculations, along with well-defined uncertainties. Success of these calculations depends on high-quality equation of state (EoS) data. Out of several competing EoS models which currently exist, none of these do a good job at matching all the experimental data and only perform well in a subset of relevant parameter space. The hydrodynamic codes used to simulate ICF experiments also have many free parameters, and we need to quantify the uncertainty associated with them.

One way in which the free parameters of hydrodynamic simulations are optimised are through large ensemble parameter scans. However, this can be computationally expensive given the dimensionality of parameter space for a full laser-driven fusion simulation. Therefore, this project focuses on the implementation of a Gaussian process (GP) surrogate which provides an inexpensive model from which a large number of samples can be obtained. The GP is trained using simulations from the 1D Lagrangian hydrodynamics code Freyja, simulating shock-timing experiments conducted by Cao et al (2018). These shock-timing experiments are commonly used to validate models used in ICF codes, and we aim to optimise 4 free parameters within Freyja which best match experimental results. Three EoS models are used in Freyja simulations, namely Ideal Gas, FEOS and FPEOS.

A machine learning interatomic potential for simulation of fracture in α-iron

Neutron irradiation and extreme temperature changes in reactor pressure vessels (RPV) lead to embrittlement of the RPV’s structural steel – a low-alloy of α-iron. Large scale modelling of this phenomenon using local approach models to fracture [1] require accurate estimates of fracture effective surface energy γ_fes as an input. The γ_fes can be calculated from atomistic modelling of fracture, which in turn requires a reliable interatomic potential that is suitable for fracture simulation. In this work, we start by evaluating the crack simulations using two strong candidate machine learning potentials for α-iron – a Gaussian approximation potential (GAP) by Dragoni et al. [2], and the quadratic-noise machine learning potential by Goryaeva et al. [3] – both of which were not trained specifically for crack properties. We then present iterative modifications of the GAP training database and kernel choices, to make it suitable for simulation of cracks and their interaction with simple radiation-induced point defects in α-iron, such as vacancies and self-interstitials. These results are a step towards modelling fracture in the low-alloy ferritic steels used in RPVs.

References:

[1] Mathieu, Jean-Philippe, et al. Journal of nuclear materials 406.1 (2010): 97-112.

[2] Dragoni, Daniele, et al. Physical Review Materials 2.1 (2018): 013808.

[3] Goryaeva, Alexandra M., et al. Physical Review Materials 5.10 (2021): 103803.

Simulation of turbulent mixing in channels with reactive boundary conditions

Reactive boundary conditions are employed to model an increasingly wide-range of phenomena (e.g., [4];[1];[5]). Capability of a variety of computational schemes for reliable description of such reaction-diffusion processes at the boundary of the simulated domain have been assessed in the literature[2], but the effects of hydrodynamic process on the boundary reaction rates are
less understood. We present a computational algorithm on the basis of probability density function theory and for modelling turbulent mixing and transport of scalars through a channel with reactive boundary condition. Parametrised Scalar Profile theory (PSP) originally proposed by Meyer and Jenny [3] is used to model turbulent mixing while a Lagrangian stochastic particle
technique is used to model scalar transport. We employed a partially adsorbing boundary condition system which is formulated in terms of the partially adsorbing boundary, which means that some molecules hitting the boundary are reflected and some are adsorbed. The formulation rests on a Gaussian update rule derived with the assumption of the particle reactions being families
of weakly dependent events. This allows such reactions to be incorporated in the modelling system, while maintaining the ability to use the full PDF of the scalar concentration across time and space. Applied to the problem of turbulent flow in a narrow channel with a single reactive wall, increasing the turbulence frequency leads to a reduction in the peak concentration variance
achieved in the channel, corresponding to an increase in mixing rate. Additionally a parameter effect study was performed for one of the parameters of the reaction model, it was shown to be able to reduce the initially predicted multi-modality in the concentration distribution.

References:

[1] Steven S Andrews and Dennis Bray. Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Physical Biology, 1(3):137–151, aug 2004. doi: 10.1088/1478-3967/1/3/001.
[2] Radek Erban and S Jonathan Chapman. Reactive boundary conditions for stochastic simulations of reaction–diffusion processes. Physical Biology, 4(1):16–28, feb 2007. doi: 10.1088/1478-3975/4/1/003.
[3] D. W. Meyer and P. Jenny. A mixing model for turbulent flows based on parameterized scalar profiles. Physics of Fluids, 18(3):035105, 2006. doi: 10.1063/1.2182005.
[4] Arnab Pal, Isaac P´erez Castillo, and Anupam Kundu. Motion of a brownian particle in the presence of reactive boundaries. Phys. Rev. E, 100:042128, Oct 2019. doi: 10.1103/Phys-RevE.100.042128.
[5] Alireza Sayyidmousavi and Katrin Rohlf. Reactive multi-particle collision dynamics with reactive boundary conditions. Physical Biology, 15(4):046007, may 2018. doi: 10.1088/14783975/aabc35.

Quantum Interference Enhanced Thermopower in Single Molecules

My PhD aims to exploit the electronic and vibrational properties of nanoscale materials that are 1000 times smaller than the diameter of a human hair to discover new materials for energy harvesting and cooling in consumer electronics such as mobile phones and laptops. The ultimate goal of the research is to understand quantum and phonon transport through molecular structures for thermoelectricity and thermal management.

One of the projects currently being carried out is to see how quantum interferences affect the thermopower in molecular junctions like single molecules. Molecular junctions usually comprise a bridge, anchors and electrodes, and gold electrodes and SCH3 (SMe) anchors have been chosen for this project. Our molecular junctions demonstrated improved Seebeck coefficients, which is desirable. All the theoretical predictions are obtained using computational material modelling and quantum transport simulation methods such as SIETA [1], GOLLUM [2], tight-binding model (TB) and Dr Sadeghi’s tutorial [3]. And these results have been confirmed by collaborators from experimental groups in the UK and China.

[1] J. M. Soler et al., J. Phys. Condens. Matter, 2002, 14, 2745–2779.

[2] J. Ferrer et al., New J. Phys., 2014, 16, 093029.

[3] H. Sadeghi, Nanotechnology, 2018, 29, 373001.

A SOAP Opera: Methods for Modeling the Relationship Between Molecular Structure and Solubility
Abstract forthcoming

Flash Presentations and Slides from HetSys Cohort 3

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Title: Using surrogate models to optimise designs for laser-driven fusion power production

Laser-plasma experiments in fusion research trigger kinetic scale (pico-second timescale) instabilities, whose effects need to be included in larger-scale fluid models (nano-second timescales) to predict ICF experiments accurately. These laser-plasma instabilities are three-wave parametric couplings that can lead to the generation of ’Hot electrons’, which reduces the efficiency of the ICF process. Given the significant difference in time and length scales for kinetic and hydrodynamic processes, suitable surrogate models must be found to include the laser-plasma instability information in large-scale hydrodynamic implosion models. In this short talk, I will aim to give a very brief overview of the impact of laser-plasma instabilities and what I have been working on for the past month, which is the development of metrics to be used to build a Gaussian process surrogate.

Hierarchical Modelling Approaches to Manipulating Thin Liquid Films.

I will provide a brief overview of the current state of my project, which seeks to develop a control theoretic framework for controlling the Navier-Stokes equations in the setting of a flow of a thin film of fluid down an inclined plane. Common control theory results cannot be applied to the two-phase Navier-Stokes equations since they are too complex and nonlinear. Instead, we couple the governing equations to more tractable lower-order models which allow for the development of a range of control designs. We also seek to gain an understanding of the robustness of these controls to noise — both in the observations and in the controls themselves.

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Using machine learning to predict cardiolipin binding sites on bacterial membrane proteins

Lipid binding sites play an important role in the function of bacterial membrane proteins and are often targets for antibiotics. Being able to quickly predict their locations from the protein structure would expedite the discovery of antibiotic pharmaceuticals. Currently computational methods used to identify binding sites involve expensive MD simulations and analysis of the resulting trajectories. Using machine learning methods, such as Gaussian processes and deep neural networks, I hope to predict cardiolipin (CDL) binding sites at a fraction of the computational cost.

Atomistic Modelling of III-V Semiconductor Device Failure

III-V alloys are common in semiconductor optoelectronic devices. The high energy conditions during device operation lead to growth of crystal dislocations, which cause loss of efficiency and eventual failure of the active region. Using Gaussian Approximation Potentials, we hope to atomistically model absorption of point defects into the dislocation core at near-quantum accuracy in order to better understand the mechanisms behind dislocation growth in these devices.

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