Artificial Intelligence meets Material Genome: intelligent design of new MOFs for xylene separation challenge.

Descripción del proyecto

Separación eficiente de productos químicos basada en marcos organometálicos

Las tecnologías de separación química representan el 15 % del consumo total de energía en todo el mundo. El equipo del proyecto XYMOF, financiado por las Acciones Marie Skłodowska-Curie, se centrará en la separación del xileno, una sustancia química habitual producida a una escala enorme, que resulta complicada a causa de las propiedades físicas muy similares de sus isómeros. Mediante la combinación de simulaciones moleculares, análisis de datos e inteligencia artificial, el proyecto tiene como objetivo identificar marcos organometálicos adecuados para la separación del xileno. Estos materiales altamente selectivos, empleados como membrana o en la unidad de adsorción, facilitarían en gran medida la separación del xileno y generarían un ahorro energético significativo.

Objetivo

Chemical separation technologies consume 15% of the global energy. Separation of xylene, a common chemical produced on an enormous scale, is one of the most difficult cases, due to very similar physical properties of xylene isomers. This project will address this challenge by identifying an adsorptive material with superior selectivity towards the desired isomer, compared to the current state-of-the-art adsorbents. A very selective material, used as a membrane or in the adsorption unit, would make a step-change in the xylene separation technologies and lead to significant energy savings. To achieve this overall objective, I put together a novel computational strategy by combining molecular simulations, data analysis and AI methods. Using this strategy, I will mine tens and hundreds of thousands of real and hypothetical materials that already exist within the Material Genome, a global space of possible materials and their features. The focus of the project will be on Metal-Organic frameworks (MOFs) since they already show a particular promise for difficult separations. Specific scientific objectives of the project aim to i) understand what MOFs are promising for xylene separations ii) what structural features of MOFs are responsible for their specific behaviour iii) and then, using the AI models, design MOFs with superior xylene separation performance.
The progress will be enabled by i) an outstanding research environment of the host group, Prof. Sarkisov, and the infrastructure of the University of Manchester; ii) interdisciplinary and intersectoral collaborations with the leading academics and companies (Prof. Goodwin, University of Oxford, Dr. Pullumbi, Air Liquide); iii) excellent training and professional development opportunities.
Together, the environment of the fellowship and the pioneering research idea in application to the societally relevant and challenging problem, will make this project a stepping stone for my independent academic career.