Nour Issa Alkhatib - العربية

Noor Al-Khatib innovation is a multi-scale computational framework that uses atomic-level simulations and machine learning tools to accelerate the discovery and design of porous materials, such as metal-organic frameworks (MOFs), to find sustainable solutions to global water and air challenges.

Inspired by the pressing challenges of water scarcity and pollution, her innovation moved beyond traditional, costly, trial-and-error approaches. Her framework integrates canonical Grand Monte Carlo simulation and molecular dynamics to model how water or toxic gases interact with these materials at the atomic level, analyzing details that experiments cannot directly observe.

The simulation results are then used to train supervised machine learning models capable of rapidly examining thousands of structures, providing a crucial dual advantage: first, a mechanismal view of the reasons behind the effectiveness of materials like perfluorooctanoic acid (PFOA) in MOFs.

Second: Predictive power for identifying high-performance filter materials for applications such as atmospheric water harvesting and the removal of toxic gases like hydrogen sulfide by providing specific and interpretable predictions, thus shortening development time and scalable for water treatment and air purification.

Future plans include developing its innovation into an industry-leading computing engine, enhanced with generative artificial intelligence, enabling companies to input target conditions and receive optimized filter materials for sustainable water treatment, toxic gas removal, and environmental remediation.