Coveney also started other productive lines of research at that time while at SCR. Among these was the study of two-dimensional nanomaterials, including cationic and anionic clays and their interactions with water and organic compounds. Initially, this work was done to understand the mechanism by which water caused certain kinds of clays to swell and ultimately disintegrate, and ways of inhibiting this of concern to the oil and gas industry. This subsequently evolved to the study of nanocomposite materials consisting of 2d nanoparticles embedded in polymer matrices including, most recently, graphene and its role in enhancing the properties of polymeric systems. One of the most complex systems encountered in the inorganic world is cement, although it has been known of since at least Roman times and remains in very widespread use today. The need to predict the properties of this material for construction and oilfield use is central to many industrial operations. In a forerunner of many contemporary applications of machine learning, Coveney showed that one can use a combination of infrared spectroscopy and artificial neural networks to predict the setting properties of the material, without any need to dwell on the polemics of the chemical composition of cementitious materials and the concrete that forms when it hardens. At the same time, but using methods from nonlinear dynamics, he was able to identify the rate-determining processes that enable one to design new compounds which inhibit the crystallisation of the mineral ettringite by molecular modelling. Coveney’s work on lattice-Boltzmann models took a different turn in the period from 2006 onwards. He moved away from studying oilfield fluids to investigate blood flow in the human body, including the brain. To this end, and working with a PhD student, Marco Mazzeo, he developed a new code, named HemeLB, which simulates blood flow in the complex geometries of the human vasculature, as derived from a variety of medical imaging modalities. That code is now widely used for the study of blood flow in humans and animals. The algorithm, based on indirect addressing, scales to very large core counts on CPU-based supercomputers. Most recently, he and his team have developed a GPU-accelerated version of the code which scales to around 20,000 GPUs on the Summit supercomputer and will soon be deployed on the world’s first exascale machine, Frontier (both at Oak Ridge National Laboratory, USA). Coveney works actively in the domain of multiscale modelling and