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CLEANSE develops a predictive approach for creating cation-selective sorbents by integrating machine learning and molecular simulations. The project focuses on optimizing materials for selective potassium ion capture, with applications in water treatment and resource recovery.
CLEANSE replaces the time-consuming, expensive, and error-prone trial-and-error approach for developing cation-selective sorbents with a predictive, iterative computation-to-experiment loop.
First, we will resolve why sodium zirconium cyclosilicate (ZS-9) is highly selective for K+ by training equivariant machine-learning interatomic potentials (MLIPs) on ab initio data and running long (biased) molecular-dynamics simulations under realistic aqueous, multi-ion conditions.
These models will also…
UNIVERSIDADE DE AVEIRO
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