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              <text>Photosynthesis could aid in reducing atmospheric CO2 levels, cyanobacteria being a large driver of this process. However, the mechanisms responsible for controlling photosynthesis, specifically how protein complexes reach the thylakoid membranes, remain unclear. The spatial organisation of chromosomes is crucial in regulating gene function in other bacteria but is not yet recognised in cyanobacteria. We, therefore, test the hypothesis that cyanobacterial chromosome structure is important for the correct and coordinated activity of photosynthesis, utilising synechocystis, a model cyanobacteria, and performing molecular cloning and Gibson assembly.</text>
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              <text>Boron nanoclusters can form planar, cage-like, open and compact structures because electron-deficient multicentre bonding produces many low-energy arrangements. Identifying stable structures means the use of a large number of geometry optimisations, making direct screening with density functional theory computationally expensive. In this project, 125 AIRSS-generated candidates for each cluster size from B₁ to B₂₀₀ were independently optimised using the 7NET, MACE and PET-MAD machine-learning interatomic potentials, giving 75,000 optimisation attempts. The lowest-energy converged structure from each model and cluster size was selected, producing 600 structures for analysis. PET-MAD achieved the highest overall convergence at 99.83%, followed by 7NET at 99.44% and MACE at 96.92%. 7NET generally produced the most compact, highly coordinated structures, while PET-MAD gave the closest qualitative comparison with an independent VASP-DFT B₅₃ reference. These results show that MLIPs can efficiently screen boron nanoclusters, although their predictions remain model-dependent and require matched DFT calculations for rigorous validation.</text>
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