The inaugural symposium of the UK biocondensates network took place on 11-12 December 2025 in Sheffield (the Edge, University of Sheffield). This 2-day, in-person only conference attracted ~120 researchers working on condensates across scales and disciplines – including physics, mathematics, engineering, chemistry and biology. We heard talks from all career stages, including two keynote presentations from Prof Jernej Ule (KCL/Francis Crick Institute) and Prof Tuomas Knowles (Cambridge) and 7 other invited talks, as well as several short talks and flash talks from ECRs. The symposium featured sessions on Theory and modelling, Molecular grammar of condensates and Condensates in cells and disease, as well as a workshop on bridging theory and practice in condensate research. This conference brought together, for the first time, the diverse community of researchers interested in the (emergent) properties of condensates and their components.
Sir Steve Cowley presenting the public lecture at the fifth New Directions in Theoretical Physics workshop in Edinburgh. Copyright Douglas RobertsonEdinburgh
The series of talks by the world-leading experts in theoretical physics covered had the theme of “Nonlinear Physics Across the Scales” and included talks on exciting advances in 7 main areas of great societal and intellectual importance. These were Astrophysics, High Energy Physics, The Physics of Climate, The Physics of Energy, Biophysics, Fluid Dynamics, and The Next 100 years of Quantum Physics.
In addition, the public lecture entitled “Fusion Energy: When” by Sir Steve Cowley (Princeton Plasma Physics Laboratory) was very well attended, both by registered participants and members of the public. There was also a lively panel discussion with a broad range of opinions being discussed.
Spatio-temporal kymograph of flagellar curvature showing travelling bending waves generated by molecular motor activity. This example illustrates how reaction–diffusion dynamics and elasticity combine to produce self-organised motion in active slender systems [Cass, J.F., Bloomfield-Gadêlha, H. The reaction-diffusion basis of animated patterns in eukaryotic flagella. Nat Commun14, 5638 (2023).]
On December 4, 2025, the one-day workshop on Slender and Active Mechanics of Emerging Materials and Systems took place at the International Centre for Mathematical Sciences (ICMS) in Edinburgh. The meeting gathered researchers working across physics, applied mathematics, and engineering, with a strong emphasis on active and slender systems. The workshop highlighted how research on those systems is increasingly driven by the interplay between geometry, mechanics and internal activity, often far from equilibrium.
Contributions examined self-propelled and active filaments, including the formation of self-propelled aggregates in dipolar colloids, chemo-elasto-hydrodynamic motion of filaments, and the behaviour of polymers in active nematic turbulence. Another direction focused on turbulence-like and vortex states in active suspensions, where microscopic activity generates mesoscale flow structures and heterogeneous dynamical regimes. The workshop also addressed pattern formation and synchronization, from reaction–diffusion patterns driven by molecular motors to phase synchronization of shape oscillations and wave coarsening leading to synchronized states in active solids.
Several talks addressed the mechanical behaviour of active solids, including nonreciprocal constitutive laws, active bistable components, and the emergence of forces, torques, and instabilities due to activity. Those ideas were connected to biological and engineered systems, through cell-level models of tissue dynamics, ciliary locomotion with autonomous switching and instabilities in soft robotic arms interacting with viscous fluids.
The workshop clearly showed the need for multi-physics modelling frameworks combining elasticity, hydrodynamics, chemical kinetics and stochastic effects.
The Computational Physics Group is excited to be organising Day 2 of the Physics-Enhancing Machine Learning Event, focusing on Computational Physics and Applications.
It will take place at the Institute of Physics, London, UK, on the 2nd of October 2025.
We welcome your contributions on showcasing recent progress, strengths and limitations of approaches integrating physics knowledge with Machine Learning (ML).
First prize for this year’s 2023 IoP CPG Thesis Prize has been awarded to Michael Davies, University College London and University of Cambridge. Michael’s thesis, titled Solving mysteries of ice formation with simulation and data-driven methods, applied molecular dynamics simulations and machine learning techniques to understand ice formation at the molecular level.
The transition of water (left) to ice (right) is much rarer and more complex than one might expect.
At first glance the formation of ice might seem a mundane everyday phenomenon. But its impacts are vast, ranging from glaciers, to cryopreservation, to climate modelling. And its formation is perplexing: in its pure state water must be cooled to around -40 °C for ice to form and a foreign material is almost always required. To understand how materials control ice formation, Michael used high-throughput computational simulations in combination with deep learning. The work uncovered a path to an elusive “cubic ice” polymorph and produced an AI model that beat experts from across the globe in an open head-to-head challenge despite 80 years of human endeavour. Michael also investigated the formation of “amorphous ice”, which is believed to be the most common form of water in the universe. In collaboration with experiment, he discovered a new form of amorphous ice with the same density as liquid water. The discovery raises questions about the very nature of liquid water.
We look forward to reading more about Michael’s work in the next IoP Computational Physics Group Newsletter. In the meantime, Michael’s thesis is available online.
Second prize for this year’s 2023 IoP CPG Thesis Prize has been awarded to Dimitrios Bachtis, Swansea University. Dimitrios’s thesis, titled Quantum field-theoretic machine learning and the renormalization group, explores the derivation of neural networks from quantum field theories and utilizes machine learning techniques to study phase transitions.
Mapping an original image to a quantum field theory which acts as a machine learning algorithm. As the quantum field theory equilibrates from a randomly initialised image, the picture of the bird emerges as an equilibrium configuration.
An indispensable tool in the study of phase transitions is the renormalization group, which investigates how a system changes when viewed at different scales. The application of a renormalization group transformation can be intuitively understood as the “zooming out” of a map: as the image becomes smaller some of the fine details within the map have disappeared. Dimitrios explored how machine learning algorithms enable an approximate inversion of the renormalization group which, analogously to the previous example, can be understood as the “zooming in” where new fine details are now introduced by the machine learning algorithm. The method opens up the opportunity to conduct high precision computational studies of phase transitions. Dimitrios additionally investigated the derivation of neural networks from quantum field theories via the use of the Hammersley-Clifford theorem, thus establishing a mathematically rigorous connection between quantum field theory, machine learning, and probability theory.
We look forward to reading more about Dimitrios’ work in the next IoP Computational Physics Group Newsletter. In the meantime, Dimitrios’ thesis is available online.
This year’s PhD Thesis Prize is now accepting nominations. The Committee of the Institute of Physics Computational Group offers an annual prize for the author of the PhD thesis that, in the opinion of the Committee, contributes most strongly to the advancement of computational physics.
The prize submission deadline of 30 April 2023. More information can be found on the CPG page.
The Computational Physics Group are proud to announce our sponsorship of Bristol University’s Quantum in the Summer.
Quantum Summer School for students aged 16+, 31st July – 4th August 2023, Bristol
In 2023 Bristol University will host the ninth annual free-to-attend Quantum In The Summer (QITS) school for students aged 16 and over. The intensive week-long summer school will run from 31 st July – 4 th August and aims to teach students about quantum mechanics and light, incorporating both theoretical and experimental workshops. Students will participate in introductory quantum lectures, a careers panel, the opportunity to carry out optical experiments, and more. The week also offers a range of exciting icebreaker social activities around Bristol. More information is available on the website: https://www.bristol.ac.uk/qet-labs/outreach/quantum-summer/ or via email: quantum-summer@bristol.ac.uk.
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.
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.
Computational Physics Group 