H2020Индивидуална стипендия2022–2025

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2022-09-01 → 2025-04-28
Финансиране от ЕС: 224 934 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Изкуствен интелект и компютърно моделиране се използват за създаване на нови порести материали (MOFs), които да разделят ефективно химическите изомери на ксилола. По-доброто разделяне на тези вещества помага за намаляване на енергийния разход и въглеродните емисии в индустрията.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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

• What is the problem being addressed? This project addresses the major industrial challenge of efficient chemical separation: responsible for 10–15% of global energy use. In particular, separating xylene isomers (p-, o-, and m-xylene) is difficult due to their similar properties. The core issue is understanding how to achieve efficient separation through computational modelling and the development of new porous materials. • Why is it important for society? Improving xylene separation can reduce industrial energy consumption and carbon emissions, contributing to sustainability goals. Para-xylene, a key isomer, is widely used to produce plastic bottles and other everyday items. Enhancing this process supports cleaner production, less environmental harm, and improved societal well-being. • What are the overall objectives? The main goal is to develop computer-based design tools, drawing on the Material Genome concept, molecular modeling, and AI, to create new metal-organic frameworks (MOFs) capable of energy-efficient separation of liquid xylene isomers. The project aims to achieve 99.9% recovery of para-xylene. Scientific Objective 1: Evaluating MOFs for Xylene Separation Using high-throughput simulations, the project screens thousands of real and hypothetical MOFs from databases like CoRE MOF (~15,000 entries). The goal is to assess liquid-phase xylene separation performance and create a training dataset linking structural features to separation ability. Scientific Objective 2: Understanding What Makes MOFs Effective Multivariate data analysis (MDA) is applied to identify patterns between MOF structure and separation efficiency. These insights lead to design principles that guide the creation of improved MOFs. Top-performing MOFs are then re-tested to validate predictions. Scientific Objective 3: Designing Better MOFs with AI The final step uses AI and machine learning to generate novel MOFs based on structure-performance data from earlier stages. These next-generation materials are optimized specifically for highly efficient xylene separation, expanding the frontiers of AI-driven materials design. These models are being used to generate novel MOFs that go beyond what currently exists, pushing the boundaries of how AI can contribute to materials design. The ultimate aim is to create next-generation materials specifically tailored for efficient and effective xylene separation.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

THE UNIVERSITY OF MANCHESTER · ManchesterКоординаторОбединеното кралство