Exécution de notre outil de synthèse sur un nouvel article
Vérifiez l’article ci-dessous, puis lancez sa synthèse.
From Screening to Generative Design: Advances in ML- Assisted MOFs for Carbon Capture
Muhammad Qamar Bilal, Faisal Latif, Muhammad Hasnain, Muhammad Ali, Mahnoor Saeed, Raziya Nadeem
Résumé : The ongoing climate crisis, caused by the annual release of 37 billion metric tons of CO2 emissions, is putting pressure on the advancement of Carbon Capture and Storage (CCS) and Direct Air Capture (DAC) technologies. Metal–Organic Frameworks (MOFs), with their high surface areas and modular pore topologies, present a very attractive class of sorbents for CO2 capture; however, it currently remains computationally prohibitive to explore their extensive chemical design space. Herein, we provide a thorough evaluation of how machine learning (ML) (as an emerging technology) has played an increasing role in furthering our understanding of CO2 capture from MOFs. Through an organized investigation, we provide evaluations of the latest generation of models across four key areas: application at a process level, mechanistic interpretable modelling; physically relevant descriptors, and predictive performance metrics. Recent work with Machine Learning Interatomic Potentials (MLPs) shows that traditional assumptions about rigid frameworks are being challenged by the fact that diffusion properties and adsorption thermodynamics are heavily influenced by the flexibility of the framework. The use of physics-informed descriptor engineering yields R2 values of 0.81-0.97 across gas species and pressure regimes, while the generative nature of Deep Reinforcement Learning and transformer-based architectures has been shown to allow for the inverse design of frameworks with high affinities for gas species. The trend in this sector is moving towards optimization of multiple scales simultaneously and integrating processes to achieve an optimized property prediction. Current work with machine learning is focusing on using a combination of material properties and operational indicators (such as how much gas is recovered through pressure swing adsorption) to make predictions. As these techniques improve, there will be a similar need for a design that is both physically informed and understandable, thus allowing for a link between molecular discoveries and water-stable materials that have been experimentally verified and are suitable for use in commercial applications.