Machine learning potential-driven insights into pH-dependent CO₂ reduction
Some of the most encouraging results for reaction-enhancing catalysts come from one material in particular: tin (Sn). While Sn's overall utility as a catalyst is well-known, its underlying structure-performance relationship is poorly understood, which limits our ability to maximize its potential. To address this knowledge gap, researchers at Tohoku University's Advanced Institute for Materials Research (WPI-AIMR) used machine learning to characterize Sn catalyst activity. The highly accurate simulations could be a game-changer that helps researchers swiftly and simply ...