From handling deep-sea organisms to improving climate forecasting, the technology can be transformative.
), her team used an approach called ‘active learning’ — in which the AI algorithm uses its own models to identify data that could lead to further improvements in performance — to uncover the structural and chemical features of transition-metal catalysts that can efficiently convert methane to methanol.
At present, these analyses are largely designed to identify materials that are optimized for one particular characteristic, such as stability under particular environmental conditions. But promising work is now under way in the area of ‘multi-objective optimization’, in which machine learning algorithms are used to home in on compounds and structures that simultaneously yield excellent performance across a variety of different parameters.
Working in collaboration with Imperial College London electrical engineer Pier Luigi Dragotti, Catherine Higgitt, principal scientist at the National Gallery in London, was able to detect traces of an angel and other figures hidden in da Vinci’s late-fifteenth-century work. They first used X-ray fluorescence to detect elements associated with certain pigments throughout the painting, and then used AI to reconstruct the hidden patterns formed by those pigments.
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