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The impact of AI is being explored across the full spectrum of theoretical and experimental physics at Surrey, from nuclear physics to astrophysics, photonics and quantum sciences.

In the field of astrophysics, the advent of ground-based and space-based missions has delivered six-dimensional space-velocity coordinates and up to 20 elemental abundances for more than a million stars in our Milky Way. Deciphering the history of the Milky Way, and the role played by dark matter, requires highly complex computational models. A key objective within Surrey’s Department of Physics – in collaboration with CVSSP and the Alan Turing Institute – is therefore to develop new machine learning tools for deriving stellar orbits and ages.

Predicting possible combinations

Within nuclear physics, a major challenge is to understand and predict the properties of all possible combinations of neutrons and protons, or ‘nuclear isotopes’. Isotopes with masses above 100 are typically difficult, if not impossible, to compute using the current generation of computers and theoretical techniques. Surrey’s Theoretical Nuclear Physics group is investigating the use of AI techniques to solve this challenge in two ways.

Firstly, where high performance computing tools are at the limits of their capabilities, researchers are using neural networks to provide a basis for systematic extrapolations of nuclear properties. Secondly – since modern computers cannot store the wavefunctions of nuclear systems with more than 20 particles – they are employing machine learning techniques to directly encapsulate the information of many-body systems, optimising wavefunctions in order to find the best variational solutions for the smallest nuclear systems.

The long-term objective is to extend these techniques to simulate not only the properties of static nuclei but also their dynamics, in order to provide systematic, improvable calculations which could be relevant for processes such as nuclear fusion.

Optimising nanophotonic devices

The development of novel nanophotonic materials is critical for enabling the next generation of technologies which will be used in information processing and communication, energy harvesting, healthcare and biophotonics, and quantum information processing on nanophotonics platforms.

Recent developments in AI have dramatically changed the way that nanophotonic devices are designed, with deep learning algorithms able to provide an almost instantaneous design solution after the learning phase. At Surrey we are investigating how deep learning techniques can be applied to optimise nanophotonic device performance by exploiting the correlation between material properties, structural geometry, topology, and advanced functionalities such as the strength of light-matter interaction in a quantum computing device. We are also focused on identifying and optimising the optical performance of novel types of cavities, low-loss waveguides and optical non-linear devices which can serve as photonic axons, dendrites and somas in the all-optical implementation of artificial neural networks.

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