スクリーニングから生成設計へ：CO2回収用MOFにおけるML支援の進展

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a comprehensive overview of ML techniques for MOF-based carbon capture, highlighting state-of-the-art methods and open challenges.

🏢実務担当者:Offers insights into how ML can accelerate materials discovery for carbon capture, potentially informing R&D investment decisions.

🏛政策担当者:Demonstrates the potential of ML in advancing CCS technologies, supporting policies that fund computational materials research.

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.

• Research Square https://doi.org/10.20944/preprints202602.1903.v3first seen 2026-09-02 04:33:58 · last seen 2026-09-14 04:19:32