Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

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CO2 conversion plays a vital role in addressing climate change by cutting greenhouse gas emissions and promoting a circular carbon economy. Rather than releasing carbon dioxide into the atmosphere, carbon dioxide recycling technologies allows transformatio ...

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

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Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

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Secondary aluminium production relies on natural gas to transform both primary and recycled aluminium into semi-fabricated products, leading to significant atmospheric emissions, energy losses, and resource consumption. Despite the potential for waste heat ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Fuel cells are considered one of the most efficient and environmental-friendly technologies available for converting chemical energy into electricity and heat, and considered a key technology to close the hydrogen cycle. In particular, combined heat and po ...

Industrial chemistry heavily relies on traditional separation methods which are both energy-demanding and environmentally detrimental. This thesis addresses critical separation challenges, specifically carbon capture applications and the separation of ethy ...

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

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