The development of automated experimental facilities and the digitization of experimental data have introduced numerous opportunities to radically advance chemical laboratories.
Direct air capture (DAC) of CO2 remains limited by adsorbent performance. Adsorption kinetics are a particularly underexplored lever to improve adsorbents, despite their significance in realistic processes. Today, the profile of 'the' optimal DAC adsorbent displaying high CO2 uptake and selectivity as well as fast sorption kinetics remains unknown.
In the past decade, machine learning has emerged as a powerful tool to predict reaction outcomes.
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