Author: tnshendruk

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Zafiirah Hosenie Awarded IoP CPG Thesis Prize

First prize for this year’s 2022 IoP CPG Thesis Prize has been awarded to Zafiirah Hosenie, University of Manchester. Zafiirah’s thesis, titled Feature Detection and Classification in streaming and non-streaming astronomical datasets, applied machine learning techniques to the challenges that arise from the large, streaming, data volumes that are prevalent in modern Astronomy.

Fast Radio Burst Intelligent Distinguisher (FRBID) is a machine learning model designed to filter out the so called Radio Frequency Interference (RFI) detections from true astrophysical sources (single pulses (SP) or fast radio bursts (FRB)) for real-time classification of candidates. The performance of FRBID shows a false positive rate of less than 1%. To-date, FRBID has detected more than half a dozen new single pulse candidates.

When classifying astronomical source types to their observed variations in brightness, there exists an imbalance: There are many class types that are rare but potentially quite interesting. Zafiirah enacted a rigorous statistical analysis of the features used to identify these systems, and developed novel machine learning approaches to deal with the class imbalance. Additionally, she worked with the real-time transient pipeline of the MeerLICHT telescope to resolve the problem of distinguishing between real transients and ‘bogus’ ones. Time-domain astrophysics, studying transient and variable stars, also allows astronomers to explore the Universe from a new perspective, and the algorithms Zafiirah developed have been successfully deployed at the MeerKAT radio telescope array and the MeerLICHT optical telescope, both in South Africa.

We look forward to reading more about Zafiirah’s work in the next IoP Computational Physics Group Newsletter. In the meantime, Zafiirah’s thesis is available online.

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Mary Coe Awarded IoP CPG Thesis Prize

Second prize for this year’s 2022 IoP CPG Thesis Prize has been awarded to Mary Coe, University of Bristol. Mary’s thesis, titled Hydrophobicity Across Length Scales: The Role of Surface Criticality, employed Monte Carlo simulations and density function theory to elucidating the behaviour of water near a hydrophobic solid surface. Despite its ubiquity in everyday life and in many scientific disciplines, the underlying physical mechanism relating hydrophobicity on the microscopic scale to hydrophobicity on macroscopic length scales has remained a difficult problem. Mary studied density depletion and enhanced fluctuations in the vicinity of the drying critical point for several fluid-fluid and fluid-solid interactions near curved surfaces, and so extended her work beyond hydrophobicity to consider more generally solvophobicity. Mary’s results provide strong numerical evidence that the mechanism underlying both hydrophobicity and solvophobicity across microscopic and macroscopic length scales is a drying surface critical point.

Cross-sections through the centre of simulation boxes containing particle of radius Rs, given in terms of the diameter of a monatomic water particle σmw, show the formation of fluctuating ‘nanobubbles’ across the solute surface as the solute size increases. There is debate within experimental work about the formation of the vapour layer – Mary’s simulations favoured the nano bubble view.

We look forward to reading more about Mary’s work in the next IoP Computational Physics Group Newsletter. In the meantime, Mary’s thesis is available online.

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Conference on Motility in Microbes, Molecules and Matter, 6-7 December 2021, London, England

Image credit: Esinam Dake, Loughborough University
Living systems are continually in active motion. From global scale migration down to enzymatic conformational transitions and kinetic action, living systems self-organize by moving. Moreover, motility as a response to stimuli is a key strategy by which living organisms capitalize on opportunities and combat threats. Motion is then a characteristic hallmark of biological complexity; however, it is also fundamentally physical. This has made studying motility one of the most fruitful points of collaboration between biologists and physicists, and remains an exciting frontier for both groups.This workshop aims to stimulate new collaborative partnerships between experimental biologists and computational physicists. The programme is organized jointly by the IOP Biological and Computational Physics Groups and seeks to address: Biological questions that have yet to receive sufficient attention from computational modellers; Emerging numerical approaches with potential for simulating biological motions.

More details can be found at the conference webpage:
http://mmmm2021.iopconfs.org/home

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Sarah Jenkins Awarded IoP CPG Thesis Prize

This year’s 2021 IoP CPG Thesis Prize has been awarded to Sarah Jenkins, University of York. Sarah’s thesis, titled Spin Dynamics Simulations of Iridium Manganese Alloys, develops an atomistic model of IrMn. This poorly understood material is antiferromagnetic and has been used in hard disk drives for some time; however, its physics at the atomic scale has not previously been well understood due to the complexity of the material’s structure. Sarah implemented a multiscale micromagnetic model within the open-source VAMPIRE simulation package. Sarah’s thesis presents her findings on IrMn alloys in three parts: (i) its ground state magnetic structure and thermal stability, (ii) its magnitude and magnetic anisotropy (iii) the interaction (exchange bias) at the interface with a ferromagnetic layer. Her results resolve the microscopic origins of exchange bias with potential impacts in future data storage, neuromorphic computing and antiferromagnetic spintronics.

We look forward to hearing about Sarah’s work in the CPG Talks Series and reading more about Sarah’s project in the next IoP Computational Physics Group Newsletter. In the meantime, Sarah’s thesis is available online.

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Javier Díaz Brañas Awarded IoP CPG Thesis Prize

This year’s 2020 IoP CPG Thesis Prize has been awarded to Javier Díaz Brañas, University of Lincoln. Javier’s thesis, titled Computer Simulations of Block Copolymer Nanocomposite Systems, implemented efficient, parallel code to simulate the interaction of nanoparticles in diblock copolymer systems by developing a hybrid-technique based on Cell Dynamic Simulations for the polymers and Brownian Dynamics for the particles. Block copolymer melts can themselves self-assemble into mesoscale soft matter structures, thanks to the connectivity between different segments along these macromolecules. The addition of nanoparticles can induce morphological transitions, resulting in complex co-assembly processes in which a rich variety of structures are formed.

large-NP-system
A large-scale simulation result of block copolymers mixed with nanoparticles (a) and an associated detailed view around a single nanoparticle (b)

We look forward to reading more about Javier’s work in the next IoP Computational Physics Group Newsletter. In the meantime, Javier’s thesis is available online.